The project is about personalized heart monitoring system using smartphones and sensors & capable of monitoring the health of high risk cardiac patients and alert the relative if required.
WBSN based safe lifestyle: a case study of heartrate monitoring system IJECEIAES
A Heart is the vital organ of the body. According to the “world health statistics 2017” by WHO, about 460,000 people die due to fatal heart attacks every year. To reduce the death rate due to fatal heart attacks and malfunctioning of the cardiovascular system, this paper proposed a Wireless Body Sensor Network (WBSN) based, portable, easily affordable, miniatured, accurate “Heartrate Monitoring System (HMS)”. HMS can be used to regularly examine the cardiac condition at home or hospital to avoid or early detection of any serious condition. Heartrate Monitoring Algorithm (HMA) was designed to observe the spread heartbeat spectrum and worked at the backend of HMS. A case study was performed for forty healthy young subjects. Each subject data was computed for 푠푢푏 ̅̅̅̅̅ − 3푆 푑 < 푠푢푏 < 푠푢푏 ̅̅̅̅̅ + 3푆 . All subjects’ 99% data lie in the custom range. The result produced by HMS was the same as the previous medical record of subjects.
A general framework for improving electrocardiography monitoring system with ...journalBEEI
As one of the most important health monitoring systems, electrocardiography (ECG) is used to obtain information about the structure and functions of the human heart for detecting and preventing cardiovascular disease. Given its important role, it is vital that the ECG monitoring system provides relevant and accurate information about the heart. Over the years, numerous attempts were made to design and develop more effective ECG monitoring system. Nonetheless, the literature reveals not only several limitations in conventional ECG monitoring system but also emphasizes on the need to adopt new technology such as machine learning to improve the monitoring system as well as its medical applications. This paper reviews previous works on machine learning to explain its key features, capabilities as well as presents a general framework for improving ECG monitoring system.
WBSN based safe lifestyle: a case study of heartrate monitoring system IJECEIAES
A Heart is the vital organ of the body. According to the “world health statistics 2017” by WHO, about 460,000 people die due to fatal heart attacks every year. To reduce the death rate due to fatal heart attacks and malfunctioning of the cardiovascular system, this paper proposed a Wireless Body Sensor Network (WBSN) based, portable, easily affordable, miniatured, accurate “Heartrate Monitoring System (HMS)”. HMS can be used to regularly examine the cardiac condition at home or hospital to avoid or early detection of any serious condition. Heartrate Monitoring Algorithm (HMA) was designed to observe the spread heartbeat spectrum and worked at the backend of HMS. A case study was performed for forty healthy young subjects. Each subject data was computed for 푠푢푏 ̅̅̅̅̅ − 3푆 푑 < 푠푢푏 < 푠푢푏 ̅̅̅̅̅ + 3푆 . All subjects’ 99% data lie in the custom range. The result produced by HMS was the same as the previous medical record of subjects.
A general framework for improving electrocardiography monitoring system with ...journalBEEI
As one of the most important health monitoring systems, electrocardiography (ECG) is used to obtain information about the structure and functions of the human heart for detecting and preventing cardiovascular disease. Given its important role, it is vital that the ECG monitoring system provides relevant and accurate information about the heart. Over the years, numerous attempts were made to design and develop more effective ECG monitoring system. Nonetheless, the literature reveals not only several limitations in conventional ECG monitoring system but also emphasizes on the need to adopt new technology such as machine learning to improve the monitoring system as well as its medical applications. This paper reviews previous works on machine learning to explain its key features, capabilities as well as presents a general framework for improving ECG monitoring system.
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
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
Human Activity Monitoring System Using Wearable Sensors presentation a7275
The recognition of varied human actions has been a pursuit focus in technology for many years. human action recognition systems composed of wirelessly connected sensing element notes (equipped with accelerometers ) connected to totally different body sites change a range of applications like sports, geriatric care, and fitness observance. wearable sensing element accelerometers had benefits over different techniques in measurement human movement. Micro electromechanical system technology has less price of accelerometers in smaller kind factors.
Electrocardiogram signal processing algorithm on microcontroller using wavele...IJECEIAES
The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.
E-HEALTH BIOSENSOR PLATFORM FOR NONINVASIVE HEALTH MONITORING FOR THE ELDERLY...ijbesjournal
New technologies in the field of tele-health using biosensor systems for non-invasive vital signs monitoring of patients, especially elderly people who need long-term care, and marginalized areas with hard to reach health care services are emerging. A study involving a self-care approach within the cardiac domain, where late detection increases the likelihood of patient disability or of premature death is proposed. In the
study the application of e-health biosensors platform in medical services is experimented. The study resulted into the synthesis of vital signs from various body positions with biosensors that does not require a full coupled system. A model for the prevention of cardiovascular disease management based on noninvasive personal health monitoring systems with easy access for everybody, at any time or location is designed. A personal vital sign system such as ECG sensor which contain the functionality, allows recording anywhere and at any time a diagnostic quality ECG and analyzing it “on-board” by comparing it to a reference ECG, is modelled. The model called Mobile Health for the Elderly Persons (MOHELP)
which relies on with application in estimation and control of boolean processes based on noisy and incomplete measurements is designed. This enabled a reliable recommendation from a digital artificial intelligence-based diagnosis, which can support an elderly person to take timely and correct decisions upon his (her) health status. In a case of urgency, the assistant puts the elderly person in a contact with
healthcare providers. The signal pattern sensitivity related to sensors placement is one of the issues this study addressed using e-sensor platform. Sensors displacement errors have a direct impact on the medical diagnosis, especially if the diagnostic procedure is automated. The study resulted into the formulation of a methodology for e-Health Sensor Platform, in software architecture terms, that permits use of system
biosensors to adapt to the user-specific context for self-healthcare
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
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
Human Activity Monitoring System Using Wearable Sensors presentation a7275
The recognition of varied human actions has been a pursuit focus in technology for many years. human action recognition systems composed of wirelessly connected sensing element notes (equipped with accelerometers ) connected to totally different body sites change a range of applications like sports, geriatric care, and fitness observance. wearable sensing element accelerometers had benefits over different techniques in measurement human movement. Micro electromechanical system technology has less price of accelerometers in smaller kind factors.
Electrocardiogram signal processing algorithm on microcontroller using wavele...IJECEIAES
The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.
E-HEALTH BIOSENSOR PLATFORM FOR NONINVASIVE HEALTH MONITORING FOR THE ELDERLY...ijbesjournal
New technologies in the field of tele-health using biosensor systems for non-invasive vital signs monitoring of patients, especially elderly people who need long-term care, and marginalized areas with hard to reach health care services are emerging. A study involving a self-care approach within the cardiac domain, where late detection increases the likelihood of patient disability or of premature death is proposed. In the
study the application of e-health biosensors platform in medical services is experimented. The study resulted into the synthesis of vital signs from various body positions with biosensors that does not require a full coupled system. A model for the prevention of cardiovascular disease management based on noninvasive personal health monitoring systems with easy access for everybody, at any time or location is designed. A personal vital sign system such as ECG sensor which contain the functionality, allows recording anywhere and at any time a diagnostic quality ECG and analyzing it “on-board” by comparing it to a reference ECG, is modelled. The model called Mobile Health for the Elderly Persons (MOHELP)
which relies on with application in estimation and control of boolean processes based on noisy and incomplete measurements is designed. This enabled a reliable recommendation from a digital artificial intelligence-based diagnosis, which can support an elderly person to take timely and correct decisions upon his (her) health status. In a case of urgency, the assistant puts the elderly person in a contact with
healthcare providers. The signal pattern sensitivity related to sensors placement is one of the issues this study addressed using e-sensor platform. Sensors displacement errors have a direct impact on the medical diagnosis, especially if the diagnostic procedure is automated. The study resulted into the formulation of a methodology for e-Health Sensor Platform, in software architecture terms, that permits use of system
biosensors to adapt to the user-specific context for self-healthcare
This research task develops a mobile healthcare analysis system (PHAS) which combines both easy ECG signal measurement and reliable analysis of heart rate variability for home care purpose. The PHAS is composed by a health care platform (HCP) and a data system analysis (DSA) module. The HCP consists of a self-developed two pole electrocardiography (ECG) measuring device and the DSA a data processing unit for detection and analysis of heart rate variability. For the DSA module, the adaptive R Peak detection algorithm is proposed to reliably detect the R peak of ECG for HRV analysis. A number of features are extracted from ECG signals. A data mining method is employed for HRV analysis to exploit the correlation between HRV and these features. Experiments are conducted by establishing a database of ECG signals measured from 29 subjects under rest and exercise condition. The results show the PHAS’s significant potential in mobile applications of personal daily health care.
The last several decades have seen cardiovascular illnesses become the leading cause of mortality globally, in both industrialized and developing nations alike. Clinical staff monitoring and early diagnosis of heart disorders can both lower death rates. However, because it takes more intelligence, time, and skill, precise cardiac disease identification in every case and 24-h patient consultation by a doctor are not yet possible. With the use of machine learning techniques, a preliminary concept for a cloud-based system to predict heart disease has been put out in this study. An effective machine-learning strategy should be applied for the precise identification of cardiac illness. This method was created after a thorough comparison of many machine learning methods in MATLAB coding. The application may thus be utilized by the medical professionals to monitor the patient’s real-time sensor data and begin live video streaming if urgent care is necessary. The ability of the suggested method to notify both parties right away when the patient checks the stage while the doctor isn’t there was a crucial component.
An internet of things-based automatic brain tumor detection systemIJEECSIAES
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
An internet of things-based automatic brain tumor detection systemnooriasukmaningtyas
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
Ecis final paper-june2017_two way architecture between iot sensors and cloud ...Oliver Neuland
Improving health care with IoT - Research into a weight monitoring bed - ECIS 2017 paper.
Resulting from smart furniture applications research project in Germany, Oliver Neuland and partners from AUT developed a smart bed concept which utilizes weight monitoring for AAL and elderly care. Initially strategies were applied to find meaningful use cases, later a prototype was developed. Here a paper presented during ECIS in Portugal which describes the architecture of the prototype.
Detection of heart pathology using deep learning methodsIJECEIAES
In the directions of modern medicine, a new area of processing and analysis of visual data is actively developing - a radio municipality - a computer technology that allows you to deeply analyze medical images, such as computed tomography (CT), magnetic resonance imaging (MRI), chest radiography (CXR), electrocardiography and electrocardiography. This approach allows us to extract quantitative texture signs from signals and distinguish informative features to describe the heart's pathology, providing a personified approach to diagnosis and treatment. Cardiovascular diseases (SVD) are one of the main causes of death in the world, and early detection is crucial for timely intervention and improvement of results. This experiment aims to increase the accuracy of deep learning algorithms to determine cardiovascular diseases. To achieve the goal, the methods of deep learning were considered used to analyze cardiograms. To solve the tasks set in the work, 50 patients were used who are classified by three indicators,
13 anomalous, 24 nonbeat, and 1 healthy parameter, which is taken from the MIT-BIH Arrhythmia database.
This research aimed to design a device that can monitor heart rate and help nurses or doctors when they need to monitor and retrieve data of patients. By utilizing Android as a displayer makes it easier for nurses to minimize data retrieval time. The principle of the tool is to record the heart rate data received by the ear clip sensor to be processed by the Atmega328 microcontroller, then displayed on the Oled LCD and sent to Android phones via HC-05 Bluetooth for display. If the heart rate data is beyond the normal range, the Android application will post a notification in the form of an SMS to the recipient's cellphone. In testing the tool, it uses a comparison device (pulse oximetry) to determine its accuracy. Based on the testing, the heart rate monitoring device had a small error value of 0.32% and had the most substantial error value of 0.81%. The application of the monitoring system in android data can be sent well at a maximum distance of 13 meters, as well as the implementation of telemedicine in the form of a warning (SMS) can work properly.
Capital Budget Proposal NameInstitutional Affiliation CourTawnaDelatorrejs
Capital Budget Proposal
Name
Institutional Affiliation
Course
Professor
Due Date
The capital budget item is a portable cardiac monitor
The monitor is a small wearable device that keeps track of an individual’s heart rhythm
It can be worn for one or two days in which it records all the heartbeats (Briginets, et al., 2019)
Portable monitor may be prescribed after the electrocardiogram, especially if the ECG does not give satisfactory results. (Briginets, et al., 2019).
The information on the cardiac monitor can be used to determine the presence of heart rhythm problem
Capital Budget Item
Portable cardiac monitor is worn on the chest and helps evaluate the activities of an individual’s heart. It is usually prescribed as an alternative to the traditional electrocardiogram. It can be worn for one or two days in which it records all the heartbeats. The information on the cardiac monitor can be used to determine the presence of heart rhythm problem
2
Portable cardiac monitor may be used in places where the traditional electrocardiogram does not give reliable results
The device helps monitor the electrical activities of a person’s cardiovascular system (Bauer, et al., 2018)
With the portable monitor, a patient can continue with their daily activities while the heart is being monitored
The Need for the Device
A portable cardiac monitor is worn underneath the clothing and close to the patient's heart. The device is more accurate than the traditional ECG. The device can help in ascertaining the patient’s level of exercise and activity they can handle without straining. It helps in identifying the risks and causes of cardiac abnormalities
3
It is a convenient method for doctors to monitor and detect arrhythmias
The device can help in ascertaining the patient’s level of exercise and activity they can handle without straining (Bauer, et al., 2018)
As such, the risk and causes of cardiac abnormities can be easily identified
The Need for the Device, cont…
When a portable cardiac monitor is not purchased, data may not be sent from patients to healthcare professionals in real-time
Patients who suffer long term illnesses may not be able to communicate their progress with healthcare professionals in real-time (Lezhnina et al., 2020)
When the device is not purchased, patients with chronic diseases may record a reduced quality of life
Consequences if the Device is not Purchased
Patients may record reduced quality of life since the device helps improve their lifestyle. Patients may not be able to save cash if the device is not purchased since they will rely on the traditional ECG which is expensive. The device helps in sending real-time data to medical professionals for effective care. When the device is not purchased, patients with chronic diseases may record a reduced quality of life
5
Potential patients may not be reached since healthcare will not be more available (Lezhnina et al., 2020)
Patients may not be able ...
Activity and health monitoring systems
This paper presents an Open Platform Activity and health monitoring systems which are also called e-Health systems. These systems measure and store parameters that reflect changes in the human body. Due to continuous monitoring (e.g. in rest state and in physical effort state), a specialist can learn about the individual's physiological parameters. Because the human body is a complex system, the examiner can notice some changes within the body by looking at the physiological parameters. Six different sensors ensure us that the patient's individual parameters are monitored. The main components of the device are: A Raspberry Pi 3 small single-board computer, an e-Health Sensor Platform by Cooking-Hacks, a Raspberry Pi to Arduino Shields Connection Bridge and a 7-inch Raspberry Pi 3 touch screen. The processing unit is the Raspberry Pi 3 board. The Raspbian operating system runs on the Raspberry Pi 3, which provides a solid base for the software. Every examination can be controlled by the touch screen. The measurements can be started with the graphical interface by pressing a button and every measured result can be represented on the GUI’s label or on the graph. The results of every examination can be stored in a database. From that database the specialist can retrieve every personalized data
Classifying electrocardiograph waveforms using trained deep learning neural n...IAESIJAI
Due to the rise in cardiac patients, an automated system that can identify different heart disorders has been created to lighten and distribute the duty of physicians. This research uses three different electrocardiograph (ECG) signals as indicators of a person's cardiac problems: Normal sinus rhythm (NSR), arrhythmia (ARR), and congestive heart failure (CHF). The continuous wavelet transform (CWT) provides the mechanism for classifying the 190 individual cases of ECG data into a 2-dimensional time-frequency representation. In this paper, the modified GoogLeNet is used for ECG data classification. Using a transfer learning approach and adjustments to parts of the output layers, ECG classification was conducted and the effectiveness of convolutional neural network (CNN) designs was tested. By comparing the results that the optimized neural network and GoogLeNet both had classification accuracy about of 80% and 100%, respectively. The GoogLeNet provide the best result in term of accuracy and training time.
New methodology to detect the effects of emotions on different biometrics in...IJECEIAES
Recently, some problems have appeared among medical workers during the diagnosis of some diseases due to human errors or the lack of sufficient information for the diagnosis. In medical diagnosis, doctors always resort to separating human emotions and their impact on vital parameters. In this paper, a methodology is presented to measure vital parameters more accurately while studying the effect of different human emotions on vital signs. Two designs were implemented based on the microcontroller and National Instruments (NI) myRIO. Measurements of four different vital parameters are measured and recorded in real time. At the same time, the effects of different emotions on those vital parameters are recorded and stored for use in analysis and early diagnosis. The results proved that the proposed methodology can contribute to the prediction and diagnosis of the initial symptoms of some diseases such as the seventh nerve and Parkinson’s disease. The two proposed designs are compared with the reference device (beurer) results. The design using NI myRIO achieved more accurate results and a response time of 1.4 seconds for real-time measurements compared to its counterpart based on microcontrollers, which qualifies it to work in intensive care units.
Computer-aided automated detection of kidney disease using supervised learnin...IJECEIAES
In this paper, we propose an efficient home-based system for monitoring chronic kidney disease (CKD). As non-invasive disease identification approaches are gaining popularity nowadays, the proposed system is designed to detect kidney disease from saliva samples. Salivary diagnosis has advanced its popularity over the last few years due to the non-invasive sample collection technique. The use of salivary components to monitor and detect kidney disease is investigated through an experimental investigation. We measured the amount of urea in the saliva sample to detect CKD. Further, this article explains the use of predictive analysis using machine learning techniques and data analytics in remote healthcare management. The proposed health monitoring system classified the samples with an accuracy of 97.1%. With internet facilities available everywhere, this methodology can offer better healthcare services, with real-time decision support in remote monitoring platform.
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Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
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MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
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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
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.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
4. Objective
O To create a personalized device that is
capable of receiving bio-medical signal from
the body and is capable to transmit it to the
mobile device.
O To create an android application which can
receive and process the signal to display the
heart rate as well as monitors the other
diseases.
4
5. Introduction
O The aim of this project is to create a
personalized heart monitoring system using
mobile devices and electrodes and an
application which is capable of monitoring the
health of high risk cardiac patients.
O The project aims to have a better system that
is able to monitor patient clearly and make the
record of abnormalities obtained.
5
12. Conclusion
O For an anywhere and anytime monitoring
system, we have considered mobile phones
as the core of this kind of monitoring system
along with the detector that detects the pulse
from the body to generate the ECG, which
allow us to improve the quality of life for those
who suffer from cardiac disorders.
12
13. Reference
O Ramesh Gamasu, “ Literature based Survey on ECG Based Integrated Mobile Tele-
medicine System for Emergency Health Disorders” International Journal of Energy,
Information and Communications ,Vol.5, Issue 2 (2014).
O Peter Leijdekkers & Valérie Gay, " Personal Heart Monitoring and Rehabilitation System
using Smart Phones ", Mobile Business, 2006. ICMB '06. International Conference on
26-27 June 2006
O Shital L. Pingale & Nivedita Daimiwal, “Detection Of Various Diseases Using ECG
Signal In MATLAB” International Journal of Recent technology & Engineering ,Vol.3,
Issue 1 (2014).
O Patrique Fiedler, Jens Haueisen, Dunja Jannek, Stefan Griebel, Lena Zentner, Filipe
Vaz and Carlos Fonseca , “Comparison of three types of dry electrodes for
electroencephalography” , ACTA IMEKO, September 2014, Volume 3.
O Article “How the Heart functions”(2016,August 31) retrieved from
http://www.sads.org.uk/heart-functions.
O Article “how the human body generates electricity” (2016,August 31) retrieved from
http://www.todayifoundout.com/index.php/2013/07/how-the-human-body-generates-
electricity
O Article “How is ECG generated”(2016,August 31) retrieved from
http://iitr.vlab.co.in/?sub=49&brch=267&sim=1305&cnt=1 13