Abstract: Electrocardiogram is a machine that is used for the detection and the analysis of the peaks of the ECG signal. ECG signal is used for the detection of various diseases related to the heart. The cardiac arrhythmia shows abnormalities of heart that is considered as the major threat to the human. The peaks that are present in the ECG signal are used for detection of the disease. The R peak of the ECG signal is used for the detection of the disease, the arrhythmia is detected as Tachycardia and Bradycardia. This paper presents a study of the ECG signal, peaks and of the various techniques that are used for the detection of disease.
AR-based Method for ECG Classification and Patient RecognitionCSCJournals
The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
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.
St variability assessment based on complexity factor using independent compon...eSAT Journals
Abstract
In recent days the computerized ECG has become the most effective and convenient diagnostic tool to identify cardiac diseases
such as Myocardial Ischemia (MI). Among the Cardio vascular diseases (CVDs) the Myocardial Ischemia (MI) is one of the
leading causes of heart attacks. The Myocardial Ischemia (MI) occurs due to the difficulties in the flow of the electrical impulses
from SA node to bundle branches because of the abnormalities in the conduction system. Normally the ECG is used as a main
diagnostic tool to identify the cardiac diseases. In order to obtain accurate information from ECG it is necessary to remove all the
artifacts and extract the pure ECG from noise background. In this paper the removal of the artifacts is achieved with linear
filtering and the extraction of the clean ECG signal is performed using Independent Component Analysis (ICA). After
preprocessing and ECG extraction, the QRS complex of each beat is detected by using Hilbert Transform and simple threshold
detection algorithm. Next the Instantaneous Heart Rate (IHR) from RR interval and Complexity Factor (CF) from time series ST
segment are computed for each beat to form desired feature sets. Later a linear regression model is designed using Instantaneous
heart rate (IHR) and ST segment Complexity Factors (STCFs) based on Linear Regression analysis. The proposed ICA-STCFR
model is used to identify the ischemic beats from the test feature sets of ECG signal to assess the ST-Segment Variability (STV).
The ECG data sets obtained from a local hospital were used to design and test the model. The evaluation parameters, Ischemic
Intensity Factor (IIF), Ischemic Activity Factors (IAF) and Peak to Average Value (PAV) were used to evaluate the proposed
method and compared with Wavelet Transform based method. The proposed ICA-STCFR was found to be yielding better results
than WT-ST method.
Key Words: Myocardial Ischemia, ICA, HT, QRS Complex, RR interval, ST segments, IHR, STCF, Scatter-plot
Abstract: Electrocardiogram is a machine that is used for the detection and the analysis of the peaks of the ECG signal. ECG signal is used for the detection of various diseases related to the heart. The cardiac arrhythmia shows abnormalities of heart that is considered as the major threat to the human. The peaks that are present in the ECG signal are used for detection of the disease. The R peak of the ECG signal is used for the detection of the disease, the arrhythmia is detected as Tachycardia and Bradycardia. This paper presents a study of the ECG signal, peaks and of the various techniques that are used for the detection of disease.
AR-based Method for ECG Classification and Patient RecognitionCSCJournals
The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
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.
St variability assessment based on complexity factor using independent compon...eSAT Journals
Abstract
In recent days the computerized ECG has become the most effective and convenient diagnostic tool to identify cardiac diseases
such as Myocardial Ischemia (MI). Among the Cardio vascular diseases (CVDs) the Myocardial Ischemia (MI) is one of the
leading causes of heart attacks. The Myocardial Ischemia (MI) occurs due to the difficulties in the flow of the electrical impulses
from SA node to bundle branches because of the abnormalities in the conduction system. Normally the ECG is used as a main
diagnostic tool to identify the cardiac diseases. In order to obtain accurate information from ECG it is necessary to remove all the
artifacts and extract the pure ECG from noise background. In this paper the removal of the artifacts is achieved with linear
filtering and the extraction of the clean ECG signal is performed using Independent Component Analysis (ICA). After
preprocessing and ECG extraction, the QRS complex of each beat is detected by using Hilbert Transform and simple threshold
detection algorithm. Next the Instantaneous Heart Rate (IHR) from RR interval and Complexity Factor (CF) from time series ST
segment are computed for each beat to form desired feature sets. Later a linear regression model is designed using Instantaneous
heart rate (IHR) and ST segment Complexity Factors (STCFs) based on Linear Regression analysis. The proposed ICA-STCFR
model is used to identify the ischemic beats from the test feature sets of ECG signal to assess the ST-Segment Variability (STV).
The ECG data sets obtained from a local hospital were used to design and test the model. The evaluation parameters, Ischemic
Intensity Factor (IIF), Ischemic Activity Factors (IAF) and Peak to Average Value (PAV) were used to evaluate the proposed
method and compared with Wavelet Transform based method. The proposed ICA-STCFR was found to be yielding better results
than WT-ST method.
Key Words: Myocardial Ischemia, ICA, HT, QRS Complex, RR interval, ST segments, IHR, STCF, Scatter-plot
Project Report on ECG Transmitter using Agilent ADS (Advance System Design)Manu Mitra
In 1996, Intellidesign Pty Ltd (then IntelliMed) was approached by a cardiologist to design an ECG Holter monitor. This original device was a two or three lead, single channel ECG device, which could continuously record for a maximum of one hour. Additionally, the device had Polar Chest Strap capabilities for the added functionality as a Heart Rate monitor. The device could operate in four different modes: 1-hour ECG Recording Mode, in which the device would record one continuous hour of near diagnostic quality ECG trace during exercise; Event Recording Mode, in which the device would record up to 60, one minute segments around a recorded event, over a period of up to 24 hours; Heart rate Recording Mode, in which the unit would have the capacity to record up to 24 hours of heart rate information; and ECG Telemetry Mode, in which the unit would transmit, via a Radio Frequency (RF) link, a real-time ECG signal to a receiver unit. The purpose of this project is to design ECG transmitter using the software Agilent ADS.
Krammer P. et al.: Electrical impedance tomography Simulator.Hauke Sann
Swisstom Scientific Library; 16th International Conference on Biomedical Applications of Electrical Impedance Tomography, Neuchâtel Switzerland, June 2-5, 2015
Conduction system of the heart
Action potential of cardiac tissues
Cardiac cell architecture
Blood supply of conduction tissue of heart
Influence of autonomic nervous system on heart
Conduction of impulse
Cardiac cycle
Electrical vector
Recording and presentation of Electrical activity of the heart
Parts and components of a normal ECG
SA node
Ionic Basis of Electrical Activity in Pacemaker Cells
Secondary/ Latent pacemaker
How Atrial impulse is transmitted?
AV node
Bundle of His
Purkinje fibers/ventricular myocardium
Heart is the most important organ of a human body. It circulates oxygen and other vital nutrients through blood to different parts of the body and helps in the metabolic activities. Apart from this it also helps in removal of the metabolic wastes. Thus, even minor problems in heart can affect the whole organism. Researchers are diverting a lot of data analysis work for assisting the doctors to predict the heart problem. So, an analysis of the data related to different health problems and its functioning can help in predicting with a certain probability for the wellness of this organ. In this paper we have analysed the different prescribed data of 1094 patients from different parts of India. Using this data, we have built a model which gets trained using this data and tries to predict whether a new out-of-sample data has a probability of having any heart attack or not. This model can help in decision making along with the doctor to treat the patient well and creating a transparency between the doctor and the patient. In the validation set of the data, it’s not only the accuracy that the model has to take care, rather the True Positive Rate and False-Negative Rate along with the AUC-ROC helps in building/fixing the algorithm inside the model.
Classification of Arrhythmia from ECG Signals using MATLABDr. Amarjeet Singh
An Electrocardiogram (ECG) is defined as a test
that is performed on the heart to detect any abnormalities in
the cardiac cycle. Automatic classification of ECG has
evolved as an emerging tool in medical diagnosis for effective
treatments. The work proposed in this paper has been
implemented using MATLAB. In this paper, we have
proposed an efficient method to classify the ECG into normal
and abnormal as well as classify the various abnormalities.
To brief it, after the collection and filtering the ECG signal,
morphological and dynamic features from the signal were
obtained which was followed by two step classification
method based on the traits and characteristic evaluation.
ECG signals in this work are collected from MIT-BIH, AHA,
ESC, UCI databases. In addition to this, this paper also
provides a comparative study of various methods proposed
via different techniques. The proposed technique used helped
us process, analyze and classify the ECG signals with an
accuracy of 97% and with good convenience.
Classification of ECG signals into different types of arrhythmias using ML
-In this study, an intellectual based electrocardiogram (ECG) signal classification approach utilizing Deep Learning (DL) is being developed. ECG plays important role in diagnosing various Cardiac ailments. The ECG signal with irregular rhythm is known as Arrhythmia such as Atrial Fibrillation, Ventricular Tachycardia, Ventricular Fibrillation, and so on. The main aspire of this task is to screen and distinguish the patient with various cardio vascular arrhythmia
Classification and Detection of ECG-signals using Artificial Neural NetworksGaurav upadhyay
Electrocardiogram (ECG), a noninvasive technique is used as a primary diagnostic tool for
cardiovascular diseases. A cleaned ECG signal provides necessary information about the
electrophysiology of the heart diseases and ischemic changes that may occur. It provides
valuable information about the functional aspects of the heart and cardiovascular system. The
objective of the thesis is to automatic detection of cardiac arrhythmias in ECG signal.
Recently developed digital signal processing and pattern reorganization technique is used in
this thesis for detection of cardiac arrhythmias. The detection of cardiac arrhythmias in the
ECG signal consists of following stages: detection of QRS complex in ECG signal; feature
extraction from detected QRS complexes; classification of beats using extracted feature set
from QRS complexes. In turn automatic classification of heartbeats represents the automatic
detection of cardiac arrhythmias in ECG signal. Hence, in this thesis, we developed the
automatic algorithms for classification of heartbeats to detect cardiac arrhythmias in ECG
signal.QRS complex detection is the first step towards automatic detection of cardiac
arrhythmias in ECG signal. A novel algorithm for accurate detection of QRS complex in ECG
signal peak classification approach is used in ECG signal for determining various diseases . As
known the amplitudes and duration values of P-Q-R-S-T peaks determine the functioning of
heart of human. Therefore duration and amplitude of all peaks are found. R-R and P-R
intervals are calculated. Finally, we have obtained the necessary information for disease
detection .For detection of cardiac arrhythmias; the extracted features in the ECG signal will
be input to the classifier. The extracted features contain morphological l features of each
heartbeat in the ECG signal. This project is implemented by using MATLAB software. An
interface was created to easily select and process the signal. “.dat” format is used the for ECG
signal data. We have detected bradycardia and tachycardia. Massachusetts Institute of
Technology Beth Israel Hospital (MIT-BIH) arrhythmias database has been used for
performance analysis.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Project Report on ECG Transmitter using Agilent ADS (Advance System Design)Manu Mitra
In 1996, Intellidesign Pty Ltd (then IntelliMed) was approached by a cardiologist to design an ECG Holter monitor. This original device was a two or three lead, single channel ECG device, which could continuously record for a maximum of one hour. Additionally, the device had Polar Chest Strap capabilities for the added functionality as a Heart Rate monitor. The device could operate in four different modes: 1-hour ECG Recording Mode, in which the device would record one continuous hour of near diagnostic quality ECG trace during exercise; Event Recording Mode, in which the device would record up to 60, one minute segments around a recorded event, over a period of up to 24 hours; Heart rate Recording Mode, in which the unit would have the capacity to record up to 24 hours of heart rate information; and ECG Telemetry Mode, in which the unit would transmit, via a Radio Frequency (RF) link, a real-time ECG signal to a receiver unit. The purpose of this project is to design ECG transmitter using the software Agilent ADS.
Krammer P. et al.: Electrical impedance tomography Simulator.Hauke Sann
Swisstom Scientific Library; 16th International Conference on Biomedical Applications of Electrical Impedance Tomography, Neuchâtel Switzerland, June 2-5, 2015
Conduction system of the heart
Action potential of cardiac tissues
Cardiac cell architecture
Blood supply of conduction tissue of heart
Influence of autonomic nervous system on heart
Conduction of impulse
Cardiac cycle
Electrical vector
Recording and presentation of Electrical activity of the heart
Parts and components of a normal ECG
SA node
Ionic Basis of Electrical Activity in Pacemaker Cells
Secondary/ Latent pacemaker
How Atrial impulse is transmitted?
AV node
Bundle of His
Purkinje fibers/ventricular myocardium
Heart is the most important organ of a human body. It circulates oxygen and other vital nutrients through blood to different parts of the body and helps in the metabolic activities. Apart from this it also helps in removal of the metabolic wastes. Thus, even minor problems in heart can affect the whole organism. Researchers are diverting a lot of data analysis work for assisting the doctors to predict the heart problem. So, an analysis of the data related to different health problems and its functioning can help in predicting with a certain probability for the wellness of this organ. In this paper we have analysed the different prescribed data of 1094 patients from different parts of India. Using this data, we have built a model which gets trained using this data and tries to predict whether a new out-of-sample data has a probability of having any heart attack or not. This model can help in decision making along with the doctor to treat the patient well and creating a transparency between the doctor and the patient. In the validation set of the data, it’s not only the accuracy that the model has to take care, rather the True Positive Rate and False-Negative Rate along with the AUC-ROC helps in building/fixing the algorithm inside the model.
Classification of Arrhythmia from ECG Signals using MATLABDr. Amarjeet Singh
An Electrocardiogram (ECG) is defined as a test
that is performed on the heart to detect any abnormalities in
the cardiac cycle. Automatic classification of ECG has
evolved as an emerging tool in medical diagnosis for effective
treatments. The work proposed in this paper has been
implemented using MATLAB. In this paper, we have
proposed an efficient method to classify the ECG into normal
and abnormal as well as classify the various abnormalities.
To brief it, after the collection and filtering the ECG signal,
morphological and dynamic features from the signal were
obtained which was followed by two step classification
method based on the traits and characteristic evaluation.
ECG signals in this work are collected from MIT-BIH, AHA,
ESC, UCI databases. In addition to this, this paper also
provides a comparative study of various methods proposed
via different techniques. The proposed technique used helped
us process, analyze and classify the ECG signals with an
accuracy of 97% and with good convenience.
Classification of ECG signals into different types of arrhythmias using ML
-In this study, an intellectual based electrocardiogram (ECG) signal classification approach utilizing Deep Learning (DL) is being developed. ECG plays important role in diagnosing various Cardiac ailments. The ECG signal with irregular rhythm is known as Arrhythmia such as Atrial Fibrillation, Ventricular Tachycardia, Ventricular Fibrillation, and so on. The main aspire of this task is to screen and distinguish the patient with various cardio vascular arrhythmia
Classification and Detection of ECG-signals using Artificial Neural NetworksGaurav upadhyay
Electrocardiogram (ECG), a noninvasive technique is used as a primary diagnostic tool for
cardiovascular diseases. A cleaned ECG signal provides necessary information about the
electrophysiology of the heart diseases and ischemic changes that may occur. It provides
valuable information about the functional aspects of the heart and cardiovascular system. The
objective of the thesis is to automatic detection of cardiac arrhythmias in ECG signal.
Recently developed digital signal processing and pattern reorganization technique is used in
this thesis for detection of cardiac arrhythmias. The detection of cardiac arrhythmias in the
ECG signal consists of following stages: detection of QRS complex in ECG signal; feature
extraction from detected QRS complexes; classification of beats using extracted feature set
from QRS complexes. In turn automatic classification of heartbeats represents the automatic
detection of cardiac arrhythmias in ECG signal. Hence, in this thesis, we developed the
automatic algorithms for classification of heartbeats to detect cardiac arrhythmias in ECG
signal.QRS complex detection is the first step towards automatic detection of cardiac
arrhythmias in ECG signal. A novel algorithm for accurate detection of QRS complex in ECG
signal peak classification approach is used in ECG signal for determining various diseases . As
known the amplitudes and duration values of P-Q-R-S-T peaks determine the functioning of
heart of human. Therefore duration and amplitude of all peaks are found. R-R and P-R
intervals are calculated. Finally, we have obtained the necessary information for disease
detection .For detection of cardiac arrhythmias; the extracted features in the ECG signal will
be input to the classifier. The extracted features contain morphological l features of each
heartbeat in the ECG signal. This project is implemented by using MATLAB software. An
interface was created to easily select and process the signal. “.dat” format is used the for ECG
signal data. We have detected bradycardia and tachycardia. Massachusetts Institute of
Technology Beth Israel Hospital (MIT-BIH) arrhythmias database has been used for
performance analysis.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
WAVELET SCATTERING TRANSFORM FOR ECG CARDIOVASCULAR DISEASE CLASSIFICATIONijaia
Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this
field is increasingly on prediction, with a growing dependence on machine learning techniques. This study
aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet
database by employing machine learning (ML). The study proposed several multi-class classification
models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart
rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG
signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral
features, and wavelet scattering, to extract features and capture unique characteristics from the ECG
dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work
provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease.
Furthermore, it could prove to be a valuable resource for future medical research projects aimed at
improving the diagnosis and treatment of cardiovascular diseases.
WAVELET SCATTERING TRANSFORM FOR ECG CARDIOVASCULAR DISEASE CLASSIFICATIONgerogepatton
Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this
field is increasingly on prediction, with a growing dependence on machine learning techniques. This study
aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet
database by employing machine learning (ML). The study proposed several multi-class classification
models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart
rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG
signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral
features, and wavelet scattering, to extract features and capture unique characteristics from the ECG
dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work
provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease.
Furthermore, it could prove to be a valuable resource for future medical research projects aimed at
improving the diagnosis and treatment of cardiovascular diseases.
WAVELET SCATTERING TRANSFORM FOR ECG CARDIOVASCULAR DISEASE CLASSIFICATIONgerogepatton
Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this
field is increasingly on prediction, with a growing dependence on machine learning techniques. This study
aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet
database by employing machine learning (ML). The study proposed several multi-class classification
models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart
rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG
signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral
features, and wavelet scattering, to extract features and capture unique characteristics from the ECG
dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work
provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease.
Furthermore, it could prove to be a valuable resource for future medical research projects aimed at
improving the diagnosis and treatment of cardiovascular diseases
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.
A Novel Approach For Detection of Neurological Disorders through Electrical P...IJECEIAES
This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots
Classification of Cardiac Arrhythmia using WT, HRV, and Fuzzy C-Means ClusteringCSCJournals
The classification of the electrocardiogram registration into different pathologies disease devises is a complex pattern recognition task. In this paper, we propose a generic feature extraction for classification of ECG arrhythmias using a fuzzy c-means (FCM) clustering and Heart Rate variability (HRV). The traditional methods of diagnosis and classification present some inconveniences; seen that the precision of credit note one diagnosis exact depends on the cardiologist experience and the rate concentration. Due to the high mortality rate of heart diseases, early detection and precise discrimination of ECG arrhythmia is essential for the treatment of patients. During the recording of ECG signal, different forms of noise can be superimposed in the useful signal. The pre-treatment of ECG imposes the suppression of these perturbation signals. The row date is preprocessed, normalized and then data points are clustered using FCM technique. In this work, four different structures, FCM-HRV, PCM-HRV, FCMC-HRV and FPCM-HRV are formed by using heart rate variability technique and fuzzy c-means clustering. In addition, FCM-HRV is the new method proposed for classification of ECG. This paper presents a comparative study of the classification accuracy of ECG signals by using these four structures for computationally efficient diagnosis. The ECG signals taken from MIT-BIH ECG database are used in training to classify 4 different arrhythmias (Atrial Fibrillation Termination). All of the structures are tested by using the same ECG records. The test results suggest that FCMC-HRV structure can generalize better and is faster than the other structures.
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.
Electrocardiogram (ECG) flag is the electrical action of the human heart. The ECG contains imperative data about the general execution of the human heart framework. In this way, exact examination of the ECG flag is extremely critical however difficult undertaking. ECG flag is regularly low adequacy and polluted with various kinds of commotions due to its estimation procedure e.g. control line obstruction, amplifier clamor and standard meander. Benchmark meander is a sort of organic commotion caused by the arbitrary development of patient amid ECG estimation and misshapes the ST fragment of the ECG waveform. In this paper, we present a far reaching near investigation of five generally utilized versatile filtering calculations for the evacuation of low recurrence clamor. We perform broad investigations on the Physionet MIT BIH ECG database and contrast the flag with commotion proportion (SNR), combination rate, and time many-sided quality of these calculations. It is discovered that modified LMS has better execution than others regarding SNR and assembly rate.
PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR CARDIAC ARRHYTHMIA C...IAEME Publication
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automatic Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. The authors have employed 3 neural network classifiers to classify three types of beats of ECG signal, namely Normal (N), and two abnormal beats Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC). The classifiers used in this paper are K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC) and Multi-Class Support Vector Machine (MSVM). The performance of the classifiers is evaluated using 5 parametric measures namely Sensitivity (Se), Specificity (Sp), Precision (Pr), Bit Error Rate (BER) and Accuracy (A). Hence MSVM classifier using Crammers method is very effective for proper ECG beat classification.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
By Design, not by Accident - Agile Venture Bolzano 2024
Whitepaper
1. The Multiphase Functional Cardiogram
A Technical Overview
Abstract
MCG, the Multifunction Cardiogram, is a revolutionary approach to diagnosing cardiac conditions.
It goes beyond a traditional ECG by incorporating aspects of computational biology and systems theory
to build a functional mathematical model of the cardiac system – the heart, blood and circulatory system
– from which a more accurate diagnosis can be drawn.
This white paper discusses the theoretical background underlying the MCG system and presents
supporting results from Premier Heart’s clinical trials. Additional technical and clinical information
regarding the MCG system can be found at Premier Heart’s web site (http://www.premierheart.com)
Overview
MCG (Multifunction Cardiogram) analysis process is part of an emerging trend in medicine: the clinical
application of advances in computational biology. By combining mathematical modeling and functional
measurements of the heart’s electrical activity with an extensive empirical digital database MCG is able
to detect coronary ischemia within 90% of the accuracy of an angiogram1 , using a method that is rapid,
non-invasive, and does not expose the patient to radiation or physical stress.
Where traditional ECG technology has taken a reductionistic approach – simplifying the cardiac system by
plotting ECG signals as a virtual dipole in a voltage-over-time graph – MCG takes an integrative approach,
building upon a mathematical model that embraces the complexity of the cardiac system, including the
interaction between heart muscle and blood flow. The MCG model, founded upon a LaGrange-Euler complex,
considers all the physical properties of the heart2 and blood3 embraces the complexity of the cardiac system.
The MCG model is combined with ECG signals from two left ventricular leads (V5 and II), and via
digital signal transformations a sequence of indices is produced which quantify abnormalities in the ECG.
Clusters of these indexes have been correlated to cardiac conditions and represent potential diagnoses, and
a statistical analysis these index clusters is used to determine the likelyhood that a condition is present and
produce a final diagnosis.
Systems Analysis and ECG Signals
The Premier Heart approach is based on systems theory, in which mathematical modeling is used in the
analysis of complex systems. The mathematical modeling of organ systems is based on computational
physiology research such as the Physiome Project4 . This approach is becoming increasingly popular as
advances in computer processing power make the analysis of large datasets, such as those produced by
medical devices, more feasible. Efforts in computational physiology, such as the Physiome Project, and
computational electrophysiology, such as MCG for surface resting ECG, have proven useful in academia and
clinical practice.
An analog signal such as an ECG can be digitized and then processed by standard digital signal processing
techniques. When two signals are recorded from the same source, these techniques can be used to examine
the relationships between the signals and infer information about the source. This type of analysis allows
1 Based upon Premier Heart’s clinical trials. See http://www.premierheart.com/trials
2 viscoelasticity
of the muscle, kinetic energy of cells/groups of cells, the irregularly shaped chambers of the heart and the
complex electrical signals generated along the heart’s conduction pathways
3 non-Newtonian (semi-compressible) nature, viscosity & how blood flow is influenced by its environment
4 http://www.physiome.org/
1
2. a complex system emitting signals to be modeled as mathematical functions, where one signal is treated as
the input and the other the output of the system. The functions represent a virtual or idealized system that
embodies the relationship between the two signals, and is used to examine the relationship as a meaningful
component of the more complex system.
The system modeled by such a function could be a series of cardiac cycles, a flow of blood from chamber to
chamber, or a depolarization and repolarization cycle from one part of the heart to another part of the heart.
A conventional 2-D ECG plot is the summation of all of the complex periodic electrical activity throughout
a cardiac cycle of a human heart, and can be broken down into discernible components in systems analysis
approach. Once the complex waveform is broken into simpler mathematical functions it can be studied
quantitatively, obtaining and examining the functional characteristics of signals sampled from healthy and
diseased patients and revealing latent information not visible in the conventional 2-D plots.
Digital Signal Processing and ECG Analysis
In a traditional 12-lead ECG, six limb and six precordial leads represent the vectors of the heart as an
electrical power-generating source, reduced to a dipole with a pair of + & - signs. Conventionally, each lead
is sampled at a rate of 200-500Hz, and then analyzed individually and sequentially in the time domain. This
produces a simple model of the heart – a signal of millivolts over microseconds – which reveals arrhythmias
clearly, however the model neglects the dynamic multidimensional electrical field changes due to the stress
and strain of the interaction between the myocardium and the blood circulating through the cardiac cycle
of the heart5 .
In the MCG system two leads are used: the V5 lead, a precordial lead that represents electrical activity
in the left ventricle, and lead II, a limb lead that represents electrical activity from right arm to left ankle
along the left ventricular axis. The sampling rate is calculated to target frequency domain components that
fall between 1 Hz (a 60 bpm heart rate) and 35 Hz6 . Multiple cardiac cycles for the two leads are sampled
to provide a view of cardiac function over time, and mathematical models of the cardiac system are used
to determine which digital signal processing methods are appropriate, and where in the output significant
characteristics are likely to be found. For example, the ECG data on individual leads is well-suited to power
spectrum analysis, while systems models derived from the interactions of the two leads can be analyzed as
an impulse-response relationship.
The MCG system employs a multidimensional analysis examining the ECG signal in the frequency
domain in addition to the traditional time domain analysis. The frequency domain is commonly used in
analysis of mechanical systems to detect component wear or deterioration by monitoring a signal for changes
in amplitude at a specific frequency. The approach is easily adapted to signals from biological systems by
considering the system as a mechanism whose components oscillate at specific frequencies. In the specific
case of the heart, the components of interest are the left and right atria and ventricles.
The ECG is a periodic waveform, and therefore can be represented in the frequency domain as a Fourier
series with the heart rate as its fundamental frequency (or first harmonic). The harmonics of the heart rate
frequency are the basis for further analysis: as with all periodic waveforms each harmonic component has
a characteristic amplitude and phase angle. Correlation analysis uses the amplitudes and phase rotation
angles between two leads to determine the relationship to those leads.
Empirically Derived Clinical Indices
MCG Analysis performs DSP transformations on the Fourier series of the two leads, and using signal analysis
techniques derived from our research produces values for a set of empirically derived indices.
These quantifiable and reproducible indices are based on latent data not observable in conventional ECG
plots which becomes visible in frequency-domain analysis of individual and paired leads. The indices represent
clinically significant abnormalities can be identified in the ECG waveform which have been quantified through
5 FengG. EKG and EEG Multiphase Information Analysis. First Edition. New York: American Medical Publishers; 1992.
6 Premier Heart’s empirical research has shown that 85-90% of the power output of a normal human heart occurs below
35Hz. The sampling rate for MCG is targeted to capture frequencies up to 50Hz.
2
3. empirical research. The clinical relevance of individual indices alone is not normally significant, however
patterns or clusters of indices have been found to have strong correlation to specific diseases.
The results of this stage of analysis can indicate abnormalities of the heart that have been found empiri-
cally to represent early (i.e., sub-critical coronary artery narrowing due to atherosclerosis of as little as 40%
in single vessel disease) to later (severe multiple vessel disease due to critical stenosis) stages of myocardial
pathologies. In particular, the power spectra analysis, impulse response, phase shift, and cross correlation
data have been found to be highly sensitive to the changes in heart mechanical and/or electrical functions
as a result of ischemia due to coronary supply and myocardium demand imbalances. Empirical research has
lead to thresholds for each index, providing crossing points for normal versus abnormal patterns, and for
degrees of abnormality within an index where applicable.
Statistical Analysis and Weighting
The statistical analysis phase generates differential diagnoses and severity scores from the empirically derived
indices. The diagnoses are generated by comparing index clusters against a database population of index
patterns from patients with known disease diagnoses as well as healthy patients.
The potential differential diagnoses are weighted against the quality of the match (degree of conformance
to a disease pattern) and the quantity of confirmed cases matching the pattern. Diagnoses that exceed a set
confidence threshold are reported to the physician for further investigation.
Continual Improvement
In addition to the underlying techniques described above the MCG system relies upon a large-scale database
of empirically and clinically validated tests and results. This database is broken down into three overlapping
populations:
The General Population consists of all MCG tests ever performed. All other populations are subsets
of this group.
The Qualitative Population contains tests which have been exhaustively reviewed and whose pa-
tients have consented to their use in our ongoing research. This population is used for improving the
mathematical model and the analysis functions.
The Quantitative Population consists of tests from the Qualitative and General populations whose
results have been confirmed by two independent physician experts and whose patients have consented to
their use in our ongoing research. This population and is used to generate the statistical weights used in
diagnosis as well as for improving the mathematical model and analysis functions. The current Quantitative
Population includes approximately 40,000 patients between the ages of 14 and 95, of which 20% are clinically
normal and the remaining 80% have been diagnosed with various pathologies.
These three populations allow for the accumulation of a large body of clinical data on which to base the
mathematical models and subsequent statistical analysis. By permitting only verified and validated tests
in the quantitative population to be used in the generation of disease diagnoses we are able to continually
improve the accuracy of the technology. Furthermore both the quantitative and qualitative populations can
be mined to develop analysis functions to detect additional diseases or improve the accuracy of the system
on currently targeted conditions.
Reporting and Physician Follow-up
An MCG report consists of three classes of diagnosis (primary, secondary and tertiary), along with a disease
severity score representing the overall risk of heart disease for the patient. The Primary Diagnosis class
has been validated through extensive clinical trials and are considered extremely reliable. The Secondary
and Tertiary classifications have been strongly correlated, however as they have not been formally validated
through clinical trials they are considered suggestions for further investigation.
3
4. The Primary Diagnosis produces a result for coronary ischemia, which may be negative, borderline,
local or global.
The Severity Score combines with the primary diagnosis to indicate the severity of the patient’s
disease burden. Lower scores in asymptomatic patients can usually be managed through medication and
lifestyle changes with continued monitoring, while higher scores indicate the need for a thorough follow up
and possible interventional procedures.
The Secondary Diagnoses produce positive or negative results for myocardial infarction, ventricular
hypertrophy, myocarditis congenital heart disease, myocarditis, rheumatic heart disease,ventricular fibrilla-
tion, atrial-fibrillation, cardiomyopathy, and pulmonary heart disease.
The Tertiary Diagnoses produce positive or negative results for power failure, decreased ejection
fraction, bradycardia, tachycardia, increased and decreased myocardial compliance, myocardial remodeling,
and local or global asynchrony.
Clinical Accuracy
Premier Heart has developed the MCG technology through extensive in-house research and validated the
ability of MCG to detect coronary artery disease through rigorous double-blind clinical trials encompassing
over 1000 patients at leading heart centers around the world. An analysis of our trial data shows the
accuracy of MCG testing approaches that of coronary angiography, and when performed in accordance with
our clinical quality guidelines is unaffected by age, gender, testing center or ethnicity.
A summary of our trial data is presented in Table 1 below7 . For extensive information on Premier Heart’s
clinical trials, including published articles, please see http://www.premierheart.com/trials .
Table 1: Premier Heart Trial Results (Summary)
7 Data from International Journal of Medical Sciences, 2009; 6(4): 143-155
4