Real-time Electrocardiogram Monitoring - Bradley University.
A wearable device that monitors ecg. it performs ecg signal processing and alerts the current health readings in real time.
Instant elelectrocardiogram monitoring in android smart phonesIjrdt Journal
ECG (electrocardiogram) is very essential component for the doctors to diagnose the state of patient’s cardiovascular system. In critical situations doctors may need to examine ECG of patient instantly to take a firm and better decision in their absence near patient. In this paper a better way of instant ECG datatransfer, processing and display is demonstrated. Here ECG is acquired using simple 3 electrode single lead configuration then it is digitized and transmitted to Android smart phone in SMS message format. This SMS data is a bundle of values representing digital ECG. Acquired SMS data is fetched from inbox of the phone and processed for calculation of heart rate and detection of arrhythmia by Android application software. Then ECG is displayed on phone screen along with conclusion of heart rate and arrhythmia (if any).
Transmission of arm based real time ecg for monitoring remotely located patienteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
ECG SIGNAL ACQUISITION, FEATURE EXTRACTION AND HRV ANALYSIS USING BIOMEDICAL ...IAEME Publication
This Paper contains the complete process of ECG/EKG signal Acquisition from
hardware to its analysis using LabVIEW and Biomedical Workbench. Hardware of ECG
has the amplification, filtering and conversion of analog ECG data to digital by using
Arduino Uno. The acquisition part deal with acquiring the hardware data to analyzable
file format into pc. Here 6-channel ADC in Arduino Uno with LabVIEW interface is used
for conversion. Now the acquired ECG data is processed and analyzed with biomedical
workbench that provides the various features of ECG signal processing. This system is
very easy to implement and cost effective
Instant elelectrocardiogram monitoring in android smart phonesIjrdt Journal
ECG (electrocardiogram) is very essential component for the doctors to diagnose the state of patient’s cardiovascular system. In critical situations doctors may need to examine ECG of patient instantly to take a firm and better decision in their absence near patient. In this paper a better way of instant ECG datatransfer, processing and display is demonstrated. Here ECG is acquired using simple 3 electrode single lead configuration then it is digitized and transmitted to Android smart phone in SMS message format. This SMS data is a bundle of values representing digital ECG. Acquired SMS data is fetched from inbox of the phone and processed for calculation of heart rate and detection of arrhythmia by Android application software. Then ECG is displayed on phone screen along with conclusion of heart rate and arrhythmia (if any).
Transmission of arm based real time ecg for monitoring remotely located patienteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
ECG SIGNAL ACQUISITION, FEATURE EXTRACTION AND HRV ANALYSIS USING BIOMEDICAL ...IAEME Publication
This Paper contains the complete process of ECG/EKG signal Acquisition from
hardware to its analysis using LabVIEW and Biomedical Workbench. Hardware of ECG
has the amplification, filtering and conversion of analog ECG data to digital by using
Arduino Uno. The acquisition part deal with acquiring the hardware data to analyzable
file format into pc. Here 6-channel ADC in Arduino Uno with LabVIEW interface is used
for conversion. Now the acquired ECG data is processed and analyzed with biomedical
workbench that provides the various features of ECG signal processing. This system is
very easy to implement and cost effective
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals
A Wireless ECG Plaster for Real-Time Cardiac Health Monitoring in Body Senso...ecgpapers
In this paper we present a wireless ECG plaster
that can be used for real-time monitoring of ECG in cardiac
patients. The proposed device is light weight (25 grams),
wearable and can wirelessly transmit the patient’s ECG signal to
mobile phone or PC using ZigBee. The device has a battery life of
around 26 hours while in continuous operation, owing to the
proposed ultra-low power ECG acquisition front end chip. The
prototype has been verified in clinical trials.
With rapid development of economies, growth of aging population and the prevalence of chronic diseases across the world, there is an urgent need to find new ways to improve patient outcomes, increase access to care, and reduce the cost of medical care. A health care monitoring system is necessary to constantly monitor patient’s physiological parameters. The tele-medical system focuses on the measurement and evaluation of vital parameters e.g. temperature, electrocardiogram (ECG), heart rate variability, fall detection etc. This will enable doctors and care givers to observe patients without having to be physically present at their bedside, be it in the hospital or in their home.
Real Time Signal Quality Aware Internet of Things IOT Framework for FPGA Base...ijtsrd
Day by day the scope and use of the electronics concepts in bio-medical field is increasing gradually. A novel approach to the design of real time ECG signal acquisition system for patient monitoring in medical application, FPGA Field Programmable Gate Array is the core heart of proposed system which is configured and programmed to acquire using ECG Electrocardiogram sensor. In this paper a new concept of ECG telemetry system is discussed along with signal quality aware IoT framework for energy efficient ECG monitoring system. Tele monitoring is a medical practice that involves monitoring patients who are not at the same location as the healthcare provider. The purpose of the present study is use to identify heart condition and give the information to the doctor. The objective of the study is to improve the doctor-patient ratio and evaluation of cardiac diseases in the rural population. The proposed system for the electrocardiogram ECG monitoring controlled by FPGA and implemented in the form of android application. Dhanashri P. Yamagekar | Dr. P. C. Bhaskar "Real Time Signal Quality Aware Internet of Things (IOT) Framework for FPGA Based ECG Telemetry System and Development of Android Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18938.pdf
http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18938/real-time-signal-quality-aware-internet-of-things-iot-framework-for-fpga-based-ecg-telemetry-system-and-development-of-android-application/dhanashri-p-yamagekar
During treatment, it is highly important to continuously monitor the vital physiological signs of the patient. Therefore , patient monitoring systems has always been occupying a very important position in the field of medical devices.
The continuous improvement of technologies not only helps us transmit the vital physiological signs to the medical personnel but also simplifies the measurement and as a result raises the monitoring efficiency of patients.
Now-a-days, a growing number of people in a developing countries like India forces to look for new solutions for the continuous monitoring of health check-up. It has become a necessity to visit hospitals frequently for doctor’s consultation, which has become financially related and a time consuming process. To overcome this situation, we propose a design to monitor the patient’s health conditions such as heart beat, temperature, ECG and BP and send the message to guardian using GSM. In the recent development of internet of things(IoT) makes all objects interconnected and been recognized as the next technical revolution. Patient monitoring is one of the IoT application to monitor the patient health status. Internet of things makes medical equipments more efficient by allowing real time monitoring of health. Using IoT doctor can continuously monitor the patient’s on his smart phone and also the patient history will be stored on the web server and doctor can access the information whenever needed from anywhere.
Design and Implementation of Real Time Remote Supervisory SystemIJERA Editor
In today’s fast growing communication environment and rapid exchange of data in networking field has triggered us to develop a home based remote supervisory monitoring system. In the present paper the physiological parameters of the patient such as body temperature, ECG, Pulse rate and Oxygen Saturation is displayed in MATLAB graphical user interface which is processed using ARM7 LPC2138. In case any emergency persist and parameters goes abnormal over the optimum level then a buzzer will ring to alert the caretaker. And the vital parameters will be displayed on the patient side computer and an automatic SMS will be sent to the doctor using GSM interface.
Design and implementation of portable electrocardiogram recorder with field ...IJECEIAES
The electrical activities of the heart are used to monitor cardiovascular diseases. It can be measured using electrocardiogram (ECG), a simple, painless test that can be recorded graphically. The physician, to predict the patient’s heart conditions and recommend suitable treatments, uses electrodes placed on the patient’s skin surface, to record these signals. The P, Q, R, S, T waves in the ECG signal can be used to determine the normality and abnormality of the heart's condition. The time interval differs for each cardiovascular condition of the heart. In this work, the ECG signal is acquired real-time using an intelligent sensor module, and the recorded value is processed to find the peak values. The data is sent to the web server using internet of things technology at a minimal time, where the physician can view it and proper decision can be taken. The real-time ECG data acquisition is also made using the field programmable gate array kit as it is a low cost, high-speed device and the output is viewed in the computer. The developed model is validated through MATLAB software and implemented for real-time applications.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals
A Wireless ECG Plaster for Real-Time Cardiac Health Monitoring in Body Senso...ecgpapers
In this paper we present a wireless ECG plaster
that can be used for real-time monitoring of ECG in cardiac
patients. The proposed device is light weight (25 grams),
wearable and can wirelessly transmit the patient’s ECG signal to
mobile phone or PC using ZigBee. The device has a battery life of
around 26 hours while in continuous operation, owing to the
proposed ultra-low power ECG acquisition front end chip. The
prototype has been verified in clinical trials.
With rapid development of economies, growth of aging population and the prevalence of chronic diseases across the world, there is an urgent need to find new ways to improve patient outcomes, increase access to care, and reduce the cost of medical care. A health care monitoring system is necessary to constantly monitor patient’s physiological parameters. The tele-medical system focuses on the measurement and evaluation of vital parameters e.g. temperature, electrocardiogram (ECG), heart rate variability, fall detection etc. This will enable doctors and care givers to observe patients without having to be physically present at their bedside, be it in the hospital or in their home.
Real Time Signal Quality Aware Internet of Things IOT Framework for FPGA Base...ijtsrd
Day by day the scope and use of the electronics concepts in bio-medical field is increasing gradually. A novel approach to the design of real time ECG signal acquisition system for patient monitoring in medical application, FPGA Field Programmable Gate Array is the core heart of proposed system which is configured and programmed to acquire using ECG Electrocardiogram sensor. In this paper a new concept of ECG telemetry system is discussed along with signal quality aware IoT framework for energy efficient ECG monitoring system. Tele monitoring is a medical practice that involves monitoring patients who are not at the same location as the healthcare provider. The purpose of the present study is use to identify heart condition and give the information to the doctor. The objective of the study is to improve the doctor-patient ratio and evaluation of cardiac diseases in the rural population. The proposed system for the electrocardiogram ECG monitoring controlled by FPGA and implemented in the form of android application. Dhanashri P. Yamagekar | Dr. P. C. Bhaskar "Real Time Signal Quality Aware Internet of Things (IOT) Framework for FPGA Based ECG Telemetry System and Development of Android Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18938.pdf
http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18938/real-time-signal-quality-aware-internet-of-things-iot-framework-for-fpga-based-ecg-telemetry-system-and-development-of-android-application/dhanashri-p-yamagekar
During treatment, it is highly important to continuously monitor the vital physiological signs of the patient. Therefore , patient monitoring systems has always been occupying a very important position in the field of medical devices.
The continuous improvement of technologies not only helps us transmit the vital physiological signs to the medical personnel but also simplifies the measurement and as a result raises the monitoring efficiency of patients.
Now-a-days, a growing number of people in a developing countries like India forces to look for new solutions for the continuous monitoring of health check-up. It has become a necessity to visit hospitals frequently for doctor’s consultation, which has become financially related and a time consuming process. To overcome this situation, we propose a design to monitor the patient’s health conditions such as heart beat, temperature, ECG and BP and send the message to guardian using GSM. In the recent development of internet of things(IoT) makes all objects interconnected and been recognized as the next technical revolution. Patient monitoring is one of the IoT application to monitor the patient health status. Internet of things makes medical equipments more efficient by allowing real time monitoring of health. Using IoT doctor can continuously monitor the patient’s on his smart phone and also the patient history will be stored on the web server and doctor can access the information whenever needed from anywhere.
Design and Implementation of Real Time Remote Supervisory SystemIJERA Editor
In today’s fast growing communication environment and rapid exchange of data in networking field has triggered us to develop a home based remote supervisory monitoring system. In the present paper the physiological parameters of the patient such as body temperature, ECG, Pulse rate and Oxygen Saturation is displayed in MATLAB graphical user interface which is processed using ARM7 LPC2138. In case any emergency persist and parameters goes abnormal over the optimum level then a buzzer will ring to alert the caretaker. And the vital parameters will be displayed on the patient side computer and an automatic SMS will be sent to the doctor using GSM interface.
Design and implementation of portable electrocardiogram recorder with field ...IJECEIAES
The electrical activities of the heart are used to monitor cardiovascular diseases. It can be measured using electrocardiogram (ECG), a simple, painless test that can be recorded graphically. The physician, to predict the patient’s heart conditions and recommend suitable treatments, uses electrodes placed on the patient’s skin surface, to record these signals. The P, Q, R, S, T waves in the ECG signal can be used to determine the normality and abnormality of the heart's condition. The time interval differs for each cardiovascular condition of the heart. In this work, the ECG signal is acquired real-time using an intelligent sensor module, and the recorded value is processed to find the peak values. The data is sent to the web server using internet of things technology at a minimal time, where the physician can view it and proper decision can be taken. The real-time ECG data acquisition is also made using the field programmable gate array kit as it is a low cost, high-speed device and the output is viewed in the computer. The developed model is validated through MATLAB software and implemented for real-time applications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Wireless Physiological Monitoring System for Hyperbaric Oxygen ChamberIJRES Journal
This paper introduces a system which can monitor multi-physiological parameters in the hyperbaric oxygen chamber. The monitoring system was designed as a star wireless sensor network and the system’s transmission protocol based on the IEEE802.15.4 were programmed. The signals can be collected with the sensor network working under network synchronization. The system can be used to monitor physiological parameters such as blood pressure, pulse rate and temperature. A prototype of the monitoring system has been fabricated and extensively tested with very good results.
A Real Time Electrocardiogram (ECG) Device for Cardiac PatientsIJERD Editor
Now-a-days due to rising stress levels, change in lifestyles and a variety of different issues, the number of people suffering from heart related diseases is increasing. This number would significantly rise in the next few years. As the technology enhanced, a significant paradigm shift has been observed in the biomedical industry. To tackle the heart related issues, technology can be introduced in one’s life. This paper proposes a wireless, wearable ECG device capable of processing the patient’s ECG in a real time environment. It is capable of comparing the ECG with threshold parameters, and if ECG of the patient is not in the range of the threshold values, the device notifies the cardiac patient’s mobile phone by sending a Multimedia Messaging Service (MMS) of the changed ECG and, in turn the patient’s mobile phone sends this changed ECG image to the mobile phone present at the hospital.
Health Care Monitoring for the CVD Detection using Soft Computing Techniquesijfcstjournal
Now-a-days, many diseases are reducing the life time of the human. One of the major diseases is cardiovascular disease (CVD). It has become very common perhaps because of increasingly busy lifestyles.The rapid development of mobile communication technologies offers innumerable opportunities for the development of software and hardware applications for remote monitoring of cardiac disease. Compressed ECG is used for fast and efficient. Before performing the diagnosis, the compressed ECG must be decompressed for conventional ECG diagnosis algorithm. This decompression introduces unnecessary delay. In this paper, we introduce advanced data mining technique to detect cardiac abnormalities from the
compressed ECG using real time classification of CVD.When the patient affect cardiac disease, at the time hospital server can automatically inform to patient via email/SMS based on the real time CVD classification. Our proposed system initially uses the data mining technique, such as Genetic algorithm for attribute selection and Expectation Maximization based clustering. In this technique are used to identify the
disease from compressed ECG with the help of telecardiology diagnosis system
In this paper, an ATmega16 based system for vital signs recording using GSM is developed to measure patient’s
Heart Rate, Blood oxygen saturation percentage ,Body Temperature & also records ECG in real time. Nowadays people
are dying because of various health problems so a device will be designed to keep track on patient which should be easy
to use, portable, light weighted, small size so that it gives freedom of mobility for patient. The system is for home use by
patients that are not in critical condition but need to be periodically monitored by clinician. At any critical condition the
SMS is send to the doctor so that quick services can be provided.
Microcontroller Based Intelligent Blood Collecting Systemijsrd.com
In Hospitals Blood Plays major role, so to collect blood doctor has to conduct blood camps. To this blood camps we need more number of equipment and heavy man power. This paper mainly introduces a system to collect blood automatically and also reduce the manpower and time. In this system microcontroller is used to control all the equipment like RFID, BP Sensor, DC motor and fingerprint scanner. Here RFID is used to collect donor details of health condition to avoid accident like HIV etc. BP Sensor will sense the blood pressure in digital value. In this paper DC motor used to collect blood and Fingerprint is for doctor authentication. Finally by internetworking we can collect the blood.
The ECG signals captured from the body of the patient using three electrode model is processed and conditioned by the analog front end device is finally sent to the data acquisition unit. The data acquisition unit used is the user pc/ laptop with MATLAB. Using very specific image processing techniques the critical intelligence from the captured image is extracted. From this processed image any sort of abnormal conditions is determined which is informed to the corresponding doctor via text message. Simultaneously the processed image is sent to the doctor mail by using specific TCP/IP protocol.
The development of wireless patient monitoring system has been quite intensive in the past decade. Hence, in the present study, a new approach of wireless patient monitoring system was proposed as a prototype to minimize the power consumption and the costing issue. Visual Basic Net. 2010 as the software and Peripheral Interface Controller (PIC) 16F877 microcontroller as the hardware circuit were used to implement the system. The communication between the hardware and software systems is in the full duplex communication via the XBee modules happened. The results show that XBee module is successfully communicated with the whole system and the monitoring software is in the best condition to be implemented. Since the prototype using variable voltage, good comparison with the experimental and previous studies shows that the present study can be improved by using the real ECG machine so that the system can be ready to the real user.
An Efficient System Of Electrocardiogram Data Acquisition And Analysis Using ...IJTET Journal
The Electrocardiogram has a vital role in the diagnosis of heart related diseases. Through the technology has improved a lot, still we cannot reduce a death because of patient gets delay in reaching the hospital. In medical emergency, saving a single minute is worthwhile. The ultimate aim of this work is to develop a handy cost effective Data Acquisition (DAQ) and analysis system for ECG. This DAQ comprises of several modules like Analog to Digital Converter (ADC), power supply, amplifiers, isolators, filters and interfacing circuits. This system chiefly intends to collect the ECG signal is highly useful in clinical application such as diagnosing the problems like tachycardia, bradycardia, IInd degree heart block, myocardial infarction, etc. ECG signal will be collected from the patient using 3 lead ECG sensors and given to NI ELVIS DAQ will then transfer the signal to laptop through NI6008 data acquisition card. The Graphical User Interface (GUI) in LabVIEW software is also developed to incessantly monitor the ECG signal traces and record the ECG data with high accuracy, and from the ECG signal is analyzed using LabVIEW software and the data is send to hospital through wireless transmitter prior to ambulance reaching the hospital. Also 104 is configured further proficiency of treatment to patient. This system is applicable in the people crowded area to diagnose heart related emergency and read the ECG value with the help of a medical physician.
A low-cost electro-cardiograph machine equipped with sensitivity and paper sp...TELKOMNIKA JOURNAL
The price of electrocardiograph (ECG) machine on the market is very high. Currently, the technology used is still very complicated and ineffective, and the ECG machine cannot be connected to other devices. A new development of a low-cost ECG machine with a customized design was needed to integrate the machine with other devices. Therefore, the purpose of this study is to develop a low-cost ECG machine which can be connected to other devices and equipped with sensitivity and paper speed setting. So that portable ECG machines can be produced and used at small clinics in the society. In this study, the main controller of the 12 channels ECG machines was supported by ATMEGA16 microcontroller, that is available on the market at low prices. The main part of the ECG amplifier is built using a high common mode rejection ratio (CMRR) instrumentation amplifier (AD620) and a bandpass filter which the cutoff frequency for highpass filter and lowpass filter are 0.05 Hz and 100 Hz, respectively. In order to complement the previous study, some features were introduced such as selectivity and motor speed option. In this study, 10 participants are involved for data acquisition,and an ECG phantom was used to calibrate the machine. The performance of the ECG machine was evaluated using standard measurement namely relative percentage error (% error) and uncertainty (UA). The result shows that %error from all of the feature is less than 2% and the UA is 0.0 which shows that the ECG machine is feasible for diagnostic purposes.
Recently, in many cases, the reason for a patient staying in the hospital is not that he or she actually needs active medical care. Often, the principal reason for a lengthy stay in the hospital is simply continual observation. Therefore, efforts have been made to avoid acute admissions and long lengths of stay in the hospital. In recent years, emergency admissions and long lengths of stay have become extremely costly. So the focus of health policy has shifted away from the provision of reactive, acute care toward preventive care outside the hospital. As models of care are redesigned, health economies are seeking to provide more care outside large acute centers. The drivers for this shift are two-fold; first, there is a quality-of-care issue and second, there is a resource allocation issue. Being cared for in a patient’s own home is a key aim of current U.K. government health policy and that is driven by an imperative to provide better quality care to people without the need to disrupt their lives. Investment in technologies that enable remote monitoring would lead to long-term gains in terms of hospital finances and patient care.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
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Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
Maxilla, Mandible & Hyoid Bone & Clinical Correlations by Dr. RIG.pptx
7c457e8657c654c22747faeda03f8e180c5d
1. Real-time Electrocardiogram Monitoring
Final Report
Nicholas Clark, Edward Sandor, and Calvin Walden
Advised By: Drs. In Soo Ahn and Yufeng Lu
Department of Electrical and Computer Engineering
Bradley University, Peoria IL
May 10, 2017
2. Abstract
An arrhythmia is an irregular heartbeat that occurs when the electrical signals controlling the
heart's muscular contractions become malformed. Patients who suffer from these symptoms are
often given a Holter monitor to wear, which records electrocardiogram (ECG) data. During a
subsequent healthcare provider visit, the patient's ECG data is analyzed, and if an arrhythmia
was successfully recorded, the underlying condition can be diagnosed.
This project aimed to develop a wearable medical device for real-time arrhythmia detection,
which acquires ECG data through a three-lead ECG sensor. It performs ECG signal processing
and immediately alerts the patient's health care provider of an arrhythmia via wireless messaging.
At the current stage of this project, a common form of arrhythmia known as premature
ventricular contractions (PVCs) are identified using the Pan-Tompkins and the wavelet-based
Template-Matching algorithms. When three or more consecutive PVCs are detected, the device
sends urgent report email to a patient’s health care provider. In the experimental study, the
design has been successfully validated using benchmark records from the MIT-BIH arrhythmia
database.
A low-cost digital signal processor evaluation kit, the Texas Instruments TMS320C5515 eZdsp
USB stick, and an embedded Linux system, the Raspberry Pi 3 Model B, were chosen to be the
hardware platform for this project. This study suggested a viable, low-complexity solution for
real-time heart monitoring and arrhythmia detection.
1
3. Table of Contents
Abstract 1
Table of Contents 2
I. Introduction 4
A. Objectives 5
B. Constraints 5
II. Related Work 6
A. System Design 6
B. Results and Analysis 7
III. Implementation 9
A. System Block Diagram 9
B. Subsystems 10
1. Digital Signal Processor 11
Project Hardware 11
DSP Software 12
2. System Controller 16
Hardware 16
Software Design 17
Acquisition Daemon 17
Communication Daemon 18
3. Electrocardiogram Sensors and Prefilter Board 18
ECG Electrodes and Hardware 18
4. User Interface 20
Hardware 21
LCD Software 21
Web Server 22
5. Printed Circuit Board 23
C. Arrhythmia Detection Algorithm 24
1. QRS Peak Detection 24
2. Template Matching 25
3. Conversion to Platform Independent Code 25
4. Implementation on the DSP 25
2
4. IV. Results and Discussion 26
A. Digital Signal Processor Performance 26
1. Acquisition 26
2. Processing Time 26
B. Arrhythmia Detection Algorithm Performance 28
1. Pan-Tompkins Algorithm 28
2. Template Matching Algorithm 29
3. Overall Algorithm Performance 31
C. System Controller 32
1. ECG Monitor Report Messages 32
2. Performance 33
D. User Interface 34
1. LCD 34
2. Web Server 36
E. Power Consumption 37
V. Project Management 38
A. Division of Labor 38
B. Project Schedule 38
VI. Conclusion 41
VII. Recommendations for Future Work 42
VII. References 43
Appendix A: Applicable Standards 44
1. IEEE 802.11 44
2. MISRA C 44
Appendix B: Interface Circuit Schematic 45
3
5. I. Introduction
Arrhythmias are irregular heartbeats that occur when the electrical signals controlling the heart's
muscular contractions become malformed[1]
. While these occur occasionally in healthy people,
frequent occurrences can be a symptom of heart disease. The most common form of arrhythmia
is premature ventricular contraction (PVC)[2]
. When experienced in succession, PVCs cause a
patient's heart to fail to circulate the necessary volume of blood through the body. Figure 1
shows an example of ECG data with two PVC spikes.
Figure 1: ECG data with two PVC spikes[3]
Holter monitors allow care providers to use electrocardiograms (ECGs) to monitor a patient's
heartbeat and diagnose irregularities[4]
. However, with present technology, the ECG data must be
analyzed after it is acquired, and arrhythmias cannot be detected as they are occurring[5]
.
Previously, the project Real-time Heart Monitoring and ECG Signal Processing[6]
addressed the
need for real-time ECG signal processing, and successfully implemented a PVC detection
algorithm. However, the system was not equipped with sensors to acquire ECG data real-time. It
can only test off-line benchmark ECG data from the MIT-BIH arrhythmia database. While the
final product could process ECG data directly acquired from a sensor in real-time, it was only
used to test pre-recorded, benchmark ECG data from the MIT-BIH arrhythmia database.
This project aimed to implement a system that can be realized as a standalone medical device
that could be comfortably worn by an outpatient. Taking this project closer to this goal involves
re-evaluating the implementation of the PVC detection algorithms and the choice of embedded
platform for operability, connectivity, and battery life.
4
6. A. Objectives
The current standard of care used to detect arrhythmias is the Holter monitor. It is a portable
ECG device worn by a patient outside the hospital. It records patient ECG data for 24 to 48
hours, after which an external device must copy and process the data, which requires the patient
to return to the care provider’s office. This device cannot notify a patient's care provider of
cardiac events, because it does not process and detect arrhythmias in real-time.
Figure 2: Example of a Holter monitor[7]
Because of the shortfalls of the Holter monitor, this project could improve upon the standard of
care. To do this, three primary objectives were identified:
● Develop a portable, mobile device with ECG sensors.
● Implement an embedded system with ECG algorithms to monitor ECG signals in
real-time.
● Wirelessly notify a patient’s care provider of PVC.
B. Constraints
Based on the objectives, a number of design constraints were also identified and considered
throughout the project. Because of the size constraints of the device, embedded hardware with
limited processing power and memory is the optimal choice. Wireless connectivity must be
reliable and, for the most part, constant to ensure that communication between the device and a
care provider is possible. Additionally, battery life must be comparable to that of current Holter
monitors to ensure operability throughout the patient's day.
5
7. II. Related Work
In 2016, the Real-time Heart Monitoring and ECG Signal Processing[6]
project was completed
by Bamarouf, Crandell, and Tsuyuki. This project looked into the application of detecting
arrhythmias in real-time. It aimed to improve the Holter monitor by implementing the
Pan-Tompkins and Template Matching algorithms on the low-power microcontroller. The
portable and mobile system demonstrated that it could successfully transmit an alert message
wirelessly in the event of an arrhythmia.
A. System Design
The system was centered around the Texas Instruments TI CC3200 Internet of Things-enabled
microcontroller development platform, shown in Figure 3. Due to the limited memory and
computing power in the chosen platform, some modifications to the ECG signal processing
algorithms were made. This was due in part to the combined processor load of the real-time
signal processing, wireless communication, and data logging on the device.
Figure 3: TI CC3200 IoT enabled MCU[8]
6
8. B. Results and Analysis
The previous project initially was planned to design a complete system to process ECG data
acquired in real-time from sensors. Due to the complexity of system and the unknowns of
exploring ECG signal processing algorithms, a sensor interface was not pursued. Benchmark
ECG data demonstrating various arrhythmias was used instead. The system successfully acquired
and processed ECG data in real-time, and wirelessly transmitted instances of ventricular
tachycardia, which are instances of three or more consecutive PVC. Figure 4 shows the
wirelessly transmitted plot.
Figure 4: Wirelessly transmitted plot of VT via Plotly[6]
For the benchmark ECG data, the MIT-BIH arrhythmia database was used. This database offers
hundreds of ECG samples to test and compare ECG algorithms. Some benchmark tests were
extremely successful at not only detecting the QRS peaks, but also the PVC occurrences. The
results from these tests are shown in Table 1.
7
10. III. Implementation
A. System Block Diagram
The system block diagram of the Real-Time Electrocardiogram Monitoring device is shown in
Figure 5. The whole design is centric around the system controller, which is responsible for
managing and logging data received from the DSP. If the DSP's algorithm detects a PVC, it
sends an urgent report message to the configured care provider email address. If configured to do
so, it can also send periodic ECG report messages to a care provider or patient.
Figure 5: System block diagram
9
11. B. Subsystems
Table 2 lists the device's subsystems and the functionality of each of them.
Table 2: Subsystems
Subsystem Description
Digital Signal Processor Manages data acquisition from ECG sensors and runs arrhythmia
detection algorithm. ECG data, analysis results, and instructions
are communicated via UART.
System controller Manages user interface, logs data, sends instructions to DSP, and
sends wireless notifications using an SMS/email service. Only
component connected directly to the power supply. On-board
voltage regulators distribute power to the other subsystems.
ECG Electrodes Attaches to a patient's chest to sample electrical signal information
from the heart using foam adhesive pads.
ECG Prefilter Amplifies and filters raw signals from ECG electrodes to provide
a signal which can be acquired by the DSP’s ADC.
LCD Provides patient with quick information such as the status of the
system and heart rate.
Push Buttons Allows the patient to control the system to perform operations
such as starting and stopping monitoring or shutdown of the
device.
SMS/Email Service Communicates ECG analysis to patient or caregiver using an
email service.
Power Supply Selects between a battery or AC power supply and provides each
subsystem with clean and regulated power.
10
12. 1. Digital Signal Processor
The core of the Real-time Electrocardiogram Monitoring system is a digital signal processor
(DSP), which is responsible for ECG data acquisition and processing to detect arrhythmias in
real time. The DSP communicates via the Universal Asynchronous Receiver/Transmitter
(UART) protocol to transfer raw data and signal analysis to the system controller for logging, as
well as to receive benchmark data and instructions to start or stop processing.
Project Hardware
The TI C5515 DSP was chosen for this project. It is a low-power, 16-bit fixed-point processor
with a sufficient amount of built-in RAM for the project's arrhythmia detection algorithm. A
TMS320C5515 eZdsp prototyping board, shown in Figure 6, is used to acquire and process ECG
data.
The TMS320C5515 eZdsp specifications are as follows:
● 120-MHz 16-bit fixed-point DSP
● 64 KB of DARAM
● 256 KB of SARAM
● 128 KB of ROM
● Up to 16 MB external RAM
● 3 General purpose timers
● 4-input 10-bit ADC
● UART, SPI, and I2
C serial communication
Figure 6: TMS320C5515 eZdsp prototyping board[9]
11
13. DSP Software
The software running on the DSP consists of three parts: data acquisition, arrhythmia detection,
and UART communication. The main software loop is responsible for running the arrhythmia
detection algorithm, transmitting ECG data over UART, and parsing data received over UART.
Compared with the operation of data acquisition from the sensor, these three operations are less
time sensitive. Therefore, these three operations can be interrupted by higher-priority functions.
The flowchart of the main loop is shown in Figure 7.
Figure 7: DSP functionality flowchart
12
14. To ensure a consistent and accurate sampling rate of 360 samples per second, acquisition is
handled in the manner of interrupt. A hardware timer is used to trigger an interrupt service
routine triggered every 2.78ms. The flowchart of timer interrupt routine is shown in Figure 8.
The on-board analog-to-digital converter (ADC) is used for data acquisition. If the DSP is
configured to acquire ECG data from the sensors instead of using benchmark data, a hardware
timer initializes the start of ADC conversion. When the conversion is complete, an interrupt is
triggered, and the acquired ECG sample is saved to the system's input buffer. The flowchart of
ADC interrupt is shown in Figure 9.
If the DSP is set to process benchmark data instead, the timer interrupt retrieves a single sample
of ECG data from the UART receive buffer and saves it to the input buffer of system. When the
input buffer is full, a flag is set to run the arrhythmia detection algorithm in the main loop.
13
16. Figure 9: DSP ADC Interrupt Flowchart
Instructions can be sent to the DSP via UART. These instructions are designed to control the
DSP operation, change data source, report its status, or perform a system reset. In addition,
benchmark data can be sent to the DSP via UART to validate the arrhythmia detection algorithm.
Whenever the hardware UART buffer is full, an interrupt is triggered to unload the data from the
buffer. In the corresponding interrupt service routine, the data in the hardware buffer is unloaded
to a larger software buffer, which is parsed in the main loop. The flowchart of UART interrupt
service routine is shown in Figure 10.
15
17. Figure 10: DSP UART Received Data Interrupt Flowchart
2. System Controller
Hardware
A Raspberry Pi 3 Model B, shown in Figure 11, is used as the system controller. It is a mobile
and portable Linux-based platform with integrated wired and wireless networking, as well as a
40-pin GPIO expansion port. The operating system chosen for this project is Raspbian 8.0, a
Debian Linux derivative. Its specifications are as follows:
● 1.2 GHz Quad-Core ARM Cortex CPU
● 1 GB LPDDR2 RAM
● SD card slot as boot media and local storage
● 27 GPIO pins, including UART and I2
C
● 10/100 BaseT Ethernet wired LAN
● 802.11b/g/n wireless LAN
Figure 11: Raspberry Pi 3 Model B[10]
16
18. Software Design
All of the system controller software components are written in C and are built using the GNU
toolchain. These pieces of software make use of several open-source libraries and projects to
ease the development workload. These include:
● libconfig, a library that handles the storage and parsing of plain text configuration files.
The Real-Time ECG software shares the configuration file /etc/rtecg/rtecg.conf to
store information such as patient details, patient and care provider email addresses, and
paths for generated files and external binaries.
● wiringPi, a library that allows direct control of the 27 GPIO pins of the Raspberry Pi,
including UART and I2
C lines.
● gnuplot, a command-line graphing utility that can generate intricate and detailed plots.
The Communication Daemon uses this to generate plots of ECG data to attach to report
emails.
Acquisition Daemon
Data sent over UART from the DSP is first received by the Acquisition Daemon,
rtecg_acquisition. This program waits for serial data and accumulates several DSP buffers'
worth in memory before writing it to a time-stamped file in /var/rtecg/logs/. Its functionality
is visualized in Figure 12. In the case that an arrhythmia is detected and flagged by the DSP, it
will also signal the Communication Daemon to report this to the patient or care provider,
depending on configuration.
Figure 12: Acquisition Daemon functionality flowchart
17
19. Communication Daemon
The second stage of ECG data management in the system controller is performed by the
Communication Daemon, rtecg_comm. Its functionality is visualized in Figure 13.
Upon receiving a signal from rtecg_acquisition or another source, this program finds the
most recently created log file in /var/rtecg/logs/, and pipes its contents into gnuplot to
generate a plot as a PNG image file. It then composes a timestamped and descriptive message,
and sends it using mail to the email addresses specified in the configuration file, with both the
complete log file and newly generated plot attached.
Figure 13: Communication Daemon functionality flowchart
Upon startup, this daemon sets up a POSIX signal handler for both of the user-defined signals,
SIGUSR1 and SIGUSR2. While these both trigger the same events, SIGUSR2 is reserved for the
event where the DSP's algorithm detected an arrhythmia and will generate an urgent message,
whereas SIGUSR1 is intended to be used to trigger a routine ECG monitor report that a care
provider or patient may desire. An example of the former is shown in Figure 29.
Additionally, at system startup, the communication daemon sends a message to the care provider
email address, containing system information, including network interface IP addresses and local
storage free space. An example of this is shown in Figure 30.
3. Electrocardiogram Sensors and Prefilter Board
ECG Electrodes and Hardware
A three-channel ECG was determined to be sufficient for the ECG data acquisition portion of
this project. To acquire an ECG signal, the patient wears three foam electrodes that are secured
to their chest with an adhesive. During the development of this project, 3M Red Dot™ electrodes
18
20. were used. These electrodes are then connected to the device using a three-conductor cable with
color-coded electrode connectors on one end and a 3.5mm TRS connector on the other.
An AD8232 ECG prefilter board, shown in Figure 15, is used to amplify and filter the raw ECG
signal from the electrodes in order to generate a clean analog signal which may be acquired by
the ADC of the DSP. The analog signal, driven by an op-amp, ranges from 0-3.3V, but the
DSP’s ADC has a maximum input voltage of 1V. In order to interface the prefilter with the
DSP, a voltage divider is used.
In addition to an analog ECG signal, the prefilter board also has digital outputs that indicate if
the ECG leads are connected to electrodes on the patient's chest. This information can be used to
pause processing on the DSP until the electrodes are reconnected and alert the patient of an error.
Another digital pin causes the prefilter board to be shut down to save power.
Figure 14: AD8232 Prefilter Board, ECG leads, and electrodes
19
21. Figure 15: Close-up of the AD8232 prefilter board[11]
During the development of this project, each group member experimented with acquiring ECG
data from their own heart to test the DSP algorithm and data acquisition software of the device.
Electrodes were placed on their chests using Figure 16 as a reference.
Figure 16: Reference figure for 3-lead ECG electrode placement[12]
4. User Interface
In order to provide the patient with simple information and configuration options, a user
interface (UI) was developed. The primary component of the UI is the liquid crystal display
(LCD) and button interface. A web server running on the Raspberry Pi also allows for additional
customization and configuration of the device.
20
22. Hardware
The Adafruit 16x2 Character LCD + Keypad for Raspberry Pi was the chosen display and
pushbutton interface for this project. It offers the most flexibility by utilizing the I2
C and GPIO
buses on the first 26 pins of the Raspberry Pi 3's GPIO header. The notable components of this
package are the MCP23017 I2
C port expander chip and the HD44780 LCD Controller. Button
press events are signaled to controlling software on the Raspberry Pi via its GPIO buses.
Figure 17: Adafruit 16x2 LCD and Keypad[13]
LCD Software
The LCD process, rtecg_ui, is a Linux daemon written in C using the Adafruit-RPi-LCD
library that handles direct control of the LCD display and onboard keypad. The daemon waits for
updates to specific file descriptors and button-press events, and updates local variables and LCD
text as needed.
In order to update the LCD, commands must be sent using a specific packet protocol used by the
Microchip MCP23017 I/O Expander. This component adds additional GPIO pins for the Hitachi
HD44780 LCD controller over the Raspberry Pi's I2
C bus.
Upon startup, the LCD software initializes the GPIO pins that it uses and sets up its menu
options. It then runs in a loop, looking for changes to file descriptors that point to network
information or piped data from the webserver. If changes are detected, the menus are updated
accordingly. It also tests for detected button presses and denounces them in software before
handling them.
21
23. Figure 18: Flowchart of LCD process
Web Server
A web server was also implemented on the Raspberry Pi. For a responsive web interface, PHP5
is used alongside a common gateway interface (CGI). The web interface is served by Lighttpd, a
lightweight, secure, and open source web server. This web interface allows the patient to access
device information and configure it remotely.
22
24. 5. Printed Circuit Board
To connect the Raspberry Pi 3, the DSP, and the ECG prefilter board, a printed circuit board
using the Raspberry Pi "Hat" formfactor was designed and fabricated. Its complete schematic can
be found in Appendix A. The final, fabricated circuit board can be seen in Figure 19.
The circuit board incorporates the 40-pin Raspberry Pi 3 header, the 60-pin TMS320C5515
eZdsp expansion edge connector, a header for the prefilter board, and the voltage divider used to
convert the ECG signal to the range 0 to 1 volts. The DSP power bus and UART and I2
C signals
are connected using jumpers. Additionally, the board has debug headers for the ECG signal,
Raspberry Pi GPIO, and DSP GPIO signals to serve as accessible test points and to aid in future
work.
Figure 19: Layout of PCB to interface system components
23
25. Figure 20: Top and bottom of fabricated circuit board
C. Arrhythmia Detection Algorithm
The arrhythmia detection algorithm used in this project uses C code written by Bamarouf,
Crandell, and Tsuyuki for their Real-time Heart Monitoring and ECG Signal Processing
project.
Only the arrhythmia detection algorithm portion of their code is applicable to the system
developed for this project. The algorithm works in two stages, first QRS peaks are detected then
the locations are used with a template matching algorithm.
1. QRS Peak Detection
In order to identify most arrhythmias, some form of QRS peak detection is required. Two sets of
research have identified the Pan-Tompkins (Hamilton-Tompkins) algorithm as the simplest and
most efficient algorithm to use on embedded devices[14][15]
. This algorithm cleans the raw ECG
signal using a bandpass filter then emphasises the QRS signal using differentiation, squaring, and
an moving average filter. The algorithm then applies a set of rules to set an adaptive threshold to
detect QRS based on the bandpass filtered signal and the QRS emphasised signal.
24
26. Figure 21: Flowchart of the Pan-Tompkins Algorithm
2. Template Matching
After QRS peaks have been detected, a template matching algorithm is used to determine if a
beat is healthy or is an arrhythmia[15]
. This algorithm performs a wavelet transform on the raw
data. When first started, the algorithm creates a template for a healthy heartbeat for the particular
patient based on the best of the first beats detected. When a QRS peak is detected by the
Pan-Tompkins algorithm, the raw signal is correlated with the template and the beat is
determined to be healthy based on a threshold.
Figure 22: Flowchart of the Template Matching Algorithm
3. Conversion to Platform Independent Code
The complete arrhythmia detection algorithm was extracted from their project and made to be
platform independent, and callable as a standalone C function. A few functions were rewritten to
remove dependencies on floating point operations and libraries specific to their original platform.
The advantage of being platform independent is the code can be recompiled for different
platforms and therefore easily ported if different hardware is selected. Platform independence
also allowed the code to be directly simulated using MATLAB by creating a MEX file;
Bamarouf, Crandell, and Tsuyuki had a separate implementation that had to be maintained for
simulation. Any change made to the platform independent version algorithm changes
simultaneously on the DSP and in the MATLAB simulation reducing the chance of discrepancies
between hardware and simulation results.
4. Implementation on the DSP
The code by Bamarouf, Crandell, and Tsuyuki did not fit on the C5515 DSP as it required a large
amount of RAM. In order to make the code compatible, some of the larger buffers were
converted to circular buffers and their sizes were reduced.
25
27. IV. Results and Discussion
A. Digital Signal Processor Performance
1. Acquisition
The first major responsibility of the DSP is to acquire ECG information from a patient. The
acquisition setup is described in section III.A.3. The DSP successfully samples the ECG
prefilter board’s output with 10-bits of resolution at a rate of 360 samples per second. Figure 23
shows a section of acquired raw ECG data with a normalized magnitude. At this stage, a strong
instrumental noise is pronounced in the acquired data.
Figure 23: Raw ECG data acquired by DSP
2. Processing Time
The second major responsibility of the DSP is to process the ECG signal and communicate
analysis data to the system controller in real-time to detect arrhythmias. The measurement of
26
28. processing time starts when an output GPIO pin on the DSP is set which launches the arrhythmia
detection algorithm and the input buffer is accessed. The measurement of processing time ends
when the GPIO pin is cleared upon completion of detection algorithm.
Figure 24 shows an oscilloscope screenshot of monitor the DSP's activity. The green trace
indicates that the DSP is processing buffered ECG data. It takes 21 ms to process a 550 ms
buffer of ECG data. Next, the red trace shows it takes 120 ms to send a 550 ms buffer of data
from the DSP to the Raspberry Pi. Finally, the blue trace shows the ECG output from the
prefilter board.
Figure 24: Oscilloscope monitoring of DSP activity. From top to bottom: pulse indicating
arrhythmia detection processing time, DSP transmitting analysis data, raw ECG signal output
from prefilter board
Our profiling results show that approximately 3.8% of the DSP’s main loop is dedicated to
arrhythmia detection and approximately 22% of the main loop is dedicated to communicating
analysis. The DSP is idle approximately 74% of the time. It suggests that the system could be
set in hibernate or power-saving mode to reduce the power consumption of the system. In
addition, the idle time could be utilized to implement a more sophisticated detection algorithm
on the DSP. Currently all data are transferred in the format of blocks. Multithreading or
27
29. asynchronous hardware operations could be used to further improve the system performance in
terms of execution time.
B. Arrhythmia Detection Algorithm Performance
Performance of the arrhythmia detection algorithm has been evaluated using the same C code
compiled for the DSP as well as compiled for MATLAB. Evaluation on the DSP primarily
verifies execution on the embedding platform and real-time processing. Evaluation using
MATLAB verifies accuracy and consistency of the detection algorithm. Results are shown to be
comparable by observing the same output from both the DSP with MATLAB. Because the same
code is compiled for both platforms, modifications to the codes are reflected in the output from
both platforms.
1. Pan-Tompkins Algorithm
The Pan-Tompkins algorithm has been verified using both the DSP and MATLAB. Figure 25
shows the output from the DSP demonstrating bandpass prefiltering between 5 and 11 Hz for the
Pan-Tompkins algorithm.
Figure 25: Bandpass filtered data output from DSP
28
30. After prefiltering, QRS complex and heart beats are marked by the Pan-Tompkins algorithms.
Markings are used to identify heartbeats for the template matching algorithm. Figure 26 shows a
bandpass filtered ECG signal with heartbeats marked. A similar output has been observed from
the codes running on the DSP.
Figure 26: Detected QRS peaks by arrhythmia detection algorithm run using MATLAB
2. Template Matching Algorithm
The template matching algorithm generates two templates for a patient's normal heartbeat by
monitoring average amplitude and duration during the first two minutes of operation. A beat
closely matching the average parameters is used as a template for arrhythmia detection. Two
templates, T1 and T2, are used. Template T1 is the QRS portion of a heartbeat and template T2
is the interval between R peaks of a heart beat. Figure 27 and Figure 28 are examples of
obtained templates T1 and T2 through the MATLAB simulation of the algorithm.
29
31. Figure 27: Template of QRS peak used by template matching algorithm
Figure 28: RR interval template used by template matching algorithm
30
32. 3. Overall Algorithm Performance
The ECG signal processing algorithm on the DSP successfully prefilters data for the
Pan-Tompkins algorithm, identifies heartbeats using the Pan-Tompkins algorithm, and generates
template for the template matching algorithm. No major changes were made to the detection
technique completed by the group from previous project [6]. There are incorrect identification of
arrhythmias after compiling either the DSP or MATLAB version of the modified codes. It
generates inaccurate correlation values in the final stage of the template matching algorithm
which eventually results in incorrect identification of arrhythmias. Further analysis and
refinement of codes is needed. A detailed evaluation of the overall algorithm performance can
be seen in the reference Real-time Heart Monitoring and ECG Signal Processing[6]
.
Table 3: Real-time Heart Monitoring and ECG Signal Processing Benchmark Results[6]
Record QRS Sensitivity QRS Positive
Predictivity
PVC Sensitivity PVC Positive
Predictivity
116 0.988 0.999 0.972 0.954
119 1.000 1.000 1.000 1.000
201 0.973 0.979 0.864 0.665
203 0.991 0.982 0.854 0.548
205 0.998 1.000 0.958 0.986
208 0.938 0.994 0.826 0.972
31
33. C. System Controller
1. ECG Monitor Report Messages
The ultimate responsibility of the system controller is to alert the patient or patient's health care
provider in the event when an arrhythmia is detected in the ECG signal. Figure 29 shows a
sample alert message, as seen on a care provider's smartphone. The message includes patient
information, a timestamp, and attachments of a plot of the patient's ECG data, as well as the
complete ECG data log.
Additionally, Figure 30 shows the message to a health care provider or device administrator
upon system startup. It includes the name and ID of the device, its IP address, and the available
space of the Raspberry Pi's SD card for ECG data storage.
Figure 29: Screenshot of an ECG monitor alert email on a smartphone, indicating that the
running system detected that its patient experienced an arrhythmia.
32
34. Figure 30: Communication Daemon startup notification email
2. Performance
One of the goals of this project is to improve upon the algorithm performance. Here system
resource usage on this device is used as a metric to evaluate the performance.
With all of the Real-Time Electrocardiogram Monitoring software loaded and running, the
one-minute load average, as reported by uptime, was consistently between 0.30 and 0.50. It can
be interpreted as 7.5% to 12.5% total resource capacity, given the Raspberry Pi 3 being a
quad-core system. The Acquisition Daemon is the most resource-intensive process of all of the
project's software, using between 5% and 9% of CPU time. It could be attributed to frequent
serial port polling in the daemon.
Memory usage, again measured with all of the project's software running, is consistently found
to be about 20% of its capacity, which is about 200 MB of the total available 923 MB system
memory.
The low resource usage figures suggest that in a future iteration of this project, improvements
can be made either to harness the unused computing power to perform additional ECG
information acquisition and processing, or to invoke power-saving modes to reduce power
consumption and improve battery life.
33
35. D. User Interface
1. LCD
The LCD component of the user interface displays a number of pieces of important information
across five menu options. These include the project title, patient name, device's IP address, LCD
color options, and power options. The user can scroll through the menu items using the up and
down buttons, and toggle between options on certain menu items using the left and right buttons.
In addition, the select button can be used to select an option when applicable.
Currently, this LCD serves to convey important information quickly to the patient. The
32-character dual-line LCD restricts the information to be displayed. It also limits the amount of
interactivity. Text-only information can be displayed, not graphic data. In a future iteration of
this project, this component could be further developed to add more options to the menu, such as
a display for heart rate and more configuration options. Or an app running on smartphone could
be used for configuration.
34
36. Figure 31: LCD displaying project title Figure 32: LCD displaying color menu option
Figure 33: LCD displaying power options
menu item
Figure 34: LCD in screensaver mode after 20
seconds of inactivity
35
37. 2. Web Server
The web server allows the patient to access important patient and device information. In the
patient information section, the patient name and identification number are displayed. In the user
configuration section, the patient can see the email recipient information for the notification
emails. In the device configuration section, the device name and identification number are listed.
There is also a field to upload a new configuration file for remote configuration. There are also
power options listed including poweroff and reboot. The network information shows the current
IP address, as well as the gateway and subnet information. The final two sections include
download links for logs and files for patient access.
Figure 35: Web interface on a mobile phone
36
38. E. Power Consumption
An experiment has been performed to have a rough estimate of the average power consumed by
the entire system. The power consumption is estimated by measuring the time required to
discharge a Anker Astro E1 portable lithium polymer battery. Here the battery has a built-in
voltage regulator that makes the battery pack usable as a 5 volt power source.
Capacity is estimated by measuring the time required to discharge the battery through a resistive
load. An Arduino Nano, powered by a separate power supply, is used to measure the amount of
time required to discharge the battery. The experiment was run two times with two different
loads to estimate the battery’s capacity. In the first experiment, the battery is discharged through
a 10 ohm resistive load. In the second experiment, a 50 ohm resistive load is used to discharge
the battery. The estimated capacity of battery is approximately 14.6 Wh. The fully-charged
battery is used to run the system developed for this project. The system operates 8.7 hours under
the battery power. The estimated system power consumption is approximately 1.7 W.
The Raspberry Pi 3 is reported to use approximately 1.3 W alone[16]
. It can be seen that the
Raspberry Pi 3 is a power-hungry unit in the whole system. An alternate embedded system with
low power rating to replace the Raspberry Pi would greatly reduce the system power
consumption.
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39. V. Project Management
A. Division of Labor
Table 4 lists the division of labor for the project. In general, Ed Sanders is the primary
contributor to DSP and arrhythmia detection algorithm development, Calvin Walden is the
primary contributor to the system controller software, and Nick Clark is the primary contributor
to the user interface of the system. The bolded name next to each task denotes the primary
contributor.
Table 4: Tasks divided amongst group members
Task Contributor(s)
DSP and algorithm evaluation Ed
System Controller acquisition and communication software Calvin
Printed circuit board design Calvin
PCB review Calvin, Ed, Nick
System Controller user interface software Nick
Poster, presentation, and reports Calvin, Ed, Nick
Documentation Calvin, Ed
ECG electrode testing Calvin, Ed, Nick
B. Project Schedule
During its development, this project followed its original, proposed scale reasonably well. While
its goal was to complete all lab work in March of 2017, system testing and tuning continued into
April of 2017. When we decided to do this, each part of the system was functional, but the team
members felt that more refinement was necessary. As a result, some time originally reserved for
written deliverables was reallocated for more lab time.
Table 5 shows the updated schedule, with reallocated tasks stuck out and amendments and added
tasks in red.
38
40. Table 5: Updated Project Schedule
Week Of Work To Be Completed
11/21/16 ● MATLAB simulation of Pan-Tompkins algorithm
11/28/16 ● Project Proposal Presentation (12/1)
● Determine complexity of Pan-Tompkins algorithm
12/5/16 ● Project Proposal (12/5)
● Receive ordered parts
Winter Break ● Begin software development for embedded computer and DSP
1/16/17 ● Implement initial Pan-Tompkins algorithm on choice platform
● Begin interfacing board for embedded computer
● Interface embedded computer with DSP
1/23/17 ● Refine Pan-Tompkins algorithm, add Template Matching algorithm
● Trial wireless communication and SMS
1/30/17 ● Trial ECG data from MIT-BIH database
● Refine wireless communication and SMS
2/6/17 ● Refine Pan-Tompkins and Template Matching algorithms
● Trial ECG data from MIT-BIH database
● Begin embedded computer serial communication
2/13/17 ● Begin real-time ECG testing
● Add LCD and pushbutton interface to interfacing board
● Begin UI development
2/20/17 ● Refine Pan-Tompkins and Template Matching algorithms
● Continue real-time ECG testing
● Continue UI development
2/27/17 ● Complete real-time ECG testing
● Complete UI development
3/6/17 ● Progress Evaluation
● Continue system testing and tuning
Spring Break ● Continue system testing and tuning
3/20/17 ● Complete all lab work
● Continue written deliverables
● Continue system testing and tuning
3/27/17 ● Continue written deliverables
● Update Student Scholarship Expo poster
● Continue system testing and tuning
4/3/17 ● Finalize final report draft
● Continue system testing and tuning
39
41. 4/10/17 ● Final Report Draft (4/10)
● Finalize draft of presentation slides
● Finalize Student Scholarship Expo poster (4/10)
● Event: Student Scholarship Expo (4/11)
● Oral Presentation Preparation (4/13)
● Continue system testing and tuning
4/17/17 ● Complete all written deliverables
● Oral Presentation Preparation (4/18)
● Continue system testing and tuning
4/24/17 ● Event: Project demonstration
● Event: Poster Presentation (4/28)
● Complete all lab work
5/1/17 ● Event: Presentation of project (5/2)
● Project Website Verification (5/2)
● Complete all written deliverables
40
42. VI. Conclusion
This project group concluded that this project is a success because it met all of its primary
objectives:
● Develop a portable, mobile device with ECG sensors.
● Implement an embedded system with ECG algorithms to monitor ECG signals in
real-time.
● Wirelessly notify a patient’s care provider of PVC.
This project also met secondary objectives that would not detrimental to the project if not
completed. These include logging data and plots and providing a simple user interface. At the
end of the project timeline, the device was capable of processing, recording, and transmitting
acquired and benchmark ECG data in real-time. The primary weakness at this time is the power
consumption of the device, but this can be addressed in future iterations of the project.
This project built on the work of the Real-time Heart Monitoring and ECG Signal Processing
project. Strides were made in the ECG processing component as existing code was optimized for
cross-platform use. The system tasks were spread across a number of platforms with the
introduction of a Linux-based system controller which is better capable of logging and handling
external actions such as wireless notification. A user interface was added to make the device
more usable for a care provider or patient, and to provide access to logged information.
This project group is pleased with the outcome, but acknowledges that there is much room for
improvement. Like the additions to the previous project, more can be done to enhance this
system, and ultimately produce a marketable product in the future.
41
43. VII. Recommendations for Future Work
This project was successful in completing its primary objectives, and included a variety of
additional functionalities not initially accounted for in its original proposal. This project has
much room for improvement, and if a new group were interested in taking it further, this project
group has several suggestions:
● Reduce power consumption - According to simulated design testing, the system is
currently only capable of an 8 hour battery life using a 15 Wh battery. The next
suggestion explores one way to reduce power consumption.
● Integrate more components onto single PCB - Integrating device components
including the DSP and embedded computer (if applicable) on the same board will remove
development kits with unnecessary power consuming components from the design. This
may also allow for increased accuracy and speed of the device. It may also involve
finding a lower power embedded computer or removing the embedded computer from the
system entirely.
● Add new algorithms to detect additional types of arrhythmias - This would increase
the scope of the device, and make it more marketable in the future. There is additional
processing time available in the system for additional detection algorithms to process
data.
● Make transmission and storage of patient data secure - If this is to be a marketable
product, patient information must be secure. Look into storage and transmission
encryption.
42
44. VII. References
[1]
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http://www.heart.org/HEARTORG/Conditions/Arrhythmia/AboutArrhythmia/About-Arrhythmia_UCM_
002010_Article.jsp.
[2]
“Why Arrhythmia Matters.” [Online]. Available:
http://www.heart.org/HEARTORG/Conditions/Arrhythmia/WhyArrhythmiaMatters/Why-Arrhythmia-Ma
tters_UCM_002023_Article.jsp.
[3]
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http://classconnection.s3.amazonaws.com/833/flashcards/1119833/jpg/pvc_21349464440513.jpg
[4]
M. AlGhatrif and J. Lindsay, “A brief review: history to understand fundamentals of
electrocardiography,” J Community Hosp Intern Med Perspect, vol. 2, no. 1, Apr. 2012.
[5]
J. A. Z. Justo, R. A. G. Calleja, and A. M. Diosdado, “Acquisition software development for monitor
Holter prototype signals and its use for pre-diagnosis of cardiac damage based on nonlinear dynamic
techniques,” in AIP Conference Proceedings, 2016, vol. 1747, p. 90001.
[6]
F. Bamarouf, C. Crandell, and S. Tsuyuki, “Real-time heart monitoring and ECG signal processing,”
Bradley University, May 2016.
[7]
“acardio20140402v0005.jpg.” [Online]. Available:
https://api.kramesstaywell.com/Content/ebd5aa86-5c85-4a95-a92a-a524015ce556/medical-illustrations/I
mages/acardio20140402v0005.jpg.
[8]
“ee0e66c4-ff50-42e2-85b5-cdf9f64d6208.jpg.” [Online]. Available:
http://www.multivu.com/players/English/70647514-add-wi-fi-to-anything-with-ti-s-internet-on-a-chip-ne
w-simplelink/gallery/image/ee0e66c4-ff50-42e2-85b5-cdf9f64d6208.jpg.
[9]
“med_tmdx5515ezdsp_c5515_ezdsp_board_72.jpg (350×225).” [Online]. Available:
http://www.ti.com/diagrams/med_tmdx5515ezdsp_c5515_ezdsp_board_72.jpg.
[10]
“913XYU1VtjL._SX355_.jpg (355×228).” [Online]. Available:
https://images-na.ssl-images-amazon.com/images/I/913XYU1VtjL._SX355_.jpg.
[11]
“sku_389334_1.jpg (700×700).” [Online]. Available:
http://img.dxcdn.com/productimages/sku_389334_1.jpg.
[12]
“tmp14285_thumb1.jpg (372×480).” [Online]. Available:
http://what-when-how.com/wp-content/uploads/2012/04/tmp14285_thumb1.jpg.
[13]
“1115-00.jpg (1200×900).” [Online]. Available: https://cdn-shop.adafruit.com/1200x900/1115-00.jpg.
[14]
H. Khamis, R. Weiss, Y. Xie, C. W. Chang, N. H. Lovell, and S. J. Redmond, “QRS detection
algorithm for telehealth electrocardiogram recordings,” IEEE Transactions on Biomedical Engineering,
vol. 63, no. 7, pp. 1377–1388, Jul. 2016.
[15]
N. M. Arzeno, Z.-D. Deng, and C.-S. Poon, “Analysis of first-derivative based QRS detection
algorithms,” IEEE Trans Biomed Eng, vol. 55, no. 2, pp. 478–484, Feb. 2008.
[16]
"Power Consumption," Raspberry Pi Dramble, [Online]. Available:
https://www.pidramble.com/wiki/benchmarks/power-consumption.
43
45. Appendix A: Applicable Standards
Standards are guidelines agreed upon in an industry that ensure understandable and successful
implementation and interfacing of systems. There are two applicable standards that have been
identified for this project.
1. IEEE 802.11
This IEEE standard outlines a wireless networking topology, in this case, WiFi. This project is
based on a Raspberry Pi 3 embedded platform, which is IEEE 802.11n compliant at 2.4GHz.
2. MISRA C
The Motor Industry Software Reliability Association (MISRA) has developed coding standards
for C language. This ensures safety, security, portability, and reliability of C code across
embedded systems. Developers across numerous technical industries have adopted these
standards, including the medical device industry.
44