IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
ECG analysis part (1) \ Mohammad Al-me`ani. , MSN, RN. almaani
The document discusses ECG analysis and provides details about:
1. The ECG records the electrical activity of the heart through electrodes placed on the skin. A 12-lead ECG provides views from 12 reference points.
2. The ECG traces the heart's electrical impulses on graph paper. It displays depolarization and repolarization processes and is used to diagnose various cardiac conditions.
3. The heart's conductive system includes the sinoatrial node, atrioventricular node, bundle of His, left and right bundle branches, and Purkinje fibers which coordinate heart rhythm and contractions.
Rule Based Identification of Cardiac Arrhythmias from Enhanced ECG Signals Us...CSCJournals
The detection of abnormal cardiac rhythms, automatic discrimination from rhythmic heart activity, became a thrust area in clinical research. Arrhythmia detection is possible by analyzing the electrocardiogram (ECG) signal features. The presence of interference signals, like power line interference (PLI), Electromyogram (EMG) and baseline drift interferences, could cause serious problems during the recording of ECG signals. Many a time, they pose problem in modern control and signal processing applications by being narrow in-band interference near the frequencies carrying crucial information. This paper presents an approach for ECG signal enhancement by combining the attractive properties of principal component analysis (PCA) and wavelets, resulting in multi-scale PCA. In Multi-Scale Principal Component Analysis (MSPCA), the PCA’s ability to decorrelate the variables by extracting a linear relationship and wavelet analysis are utilized. MSPCA method effectively processed the noisy ECG signal and enhanced signal features are used for clear identification of arrhythmias. In MSPCA, the principal components of the wavelet coefficients of the ECG data at each scale are computed first and are then combined at relevant scales. Statistical measures computed in terms of root mean square deviation (RMSD), root mean square error (RMSE), root mean square variation (RMSV) and improvement in signal to noise ratio (SNRI) revealed that the Daubechies based MSPCA outperformed the basic wavelet based processing for ECG signal enhancement. With enhanced signal features obtained after MSPCA processing, the detectable measures, QRS duration and R-R interval are evaluated. By using the rule base technique, projecting the detectable measures on a two dimensional area, various arrhythmias are detected depending upon the beat falling into particular place of the two dimensional area.
Comparison of Normal and Ventricular Tachyarrhythmic Electrocardiograms using...IDES Editor
Ventricular tachyarrhythmia is a cardiac disease in which
the electrocardiogram shows occurrence of ventricular tachycardia,
flutter and fibrillation. This tachycardia is often precursor to
ventricular fibrillation or stoppage of heart beats. In this paper a
comparison has been shown between the RdR maps of the RR
intervals of normal and ventricular tachyarrhythmic ECGs. RdR
maps are a scatter plot of RR intervals and change in the RR
intervals of ECGs. This plot has been chosen because of its
computational simplicity.
This document discusses using electrocardiogram (ECG) signals to detect heart diseases. It describes using wavelet transforms to extract features from ECG signals that can be used to detect peaks corresponding to heart activity. The algorithm involves applying a discrete wavelet transform, detecting R peaks, then P, Q, S, and T peaks. Heart conditions can then be identified by analyzing the timing of peaks. Daubechies wavelets are discussed as being effective for feature extraction from ECG signals due to their similarity in shape to the QRS complex.
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.
This document provides an overview of electrocardiography (ECG). It defines an ECG as a tracing of the heart's electrical activity. The objectives are to learn how to perform an ECG, interpret the results, and recognize various pathologies. Key points covered include electrode placement, components of the ECG wave, the physiology of cardiac conduction, interpreting the rate, rhythm, axis, and analyzing P, QRS, and T waves. Causes of axis deviations and details on analyzing the P wave are also summarized.
This document provides an overview of electrocardiogram (ECG) interpretation. It discusses the components of ECG complexes and intervals, including the P wave, QRS complex, and T wave. It correlates the ECG tracings with the electrical events in the heart during excitation and recovery. Key points include that the P wave represents atrial depolarization, the QRS complex represents ventricular depolarization, and the T wave represents ventricular recovery. It also discusses normal values for amplitude and duration of the various complexes.
Classification of ecg signal using artificial neural networkGaurav upadhyay
An electrocardiogram (ECG) is a bio-electrical signal which is used to record the heart's electrical activity with respect to time. Early and accurate detection is important in detecting heart diseases and choosing appropriate treatment for a patient. ECG signals are used as the parameter for detection of Cardiac diseases and most of the data comes from PhysioDataNet and MIT-BIH database .The pre-processing of ECG signal is performed with help of Wavelet toolbox and also used for feature extraction of ECG signal. The complete project is implemented on MATLAB platform. The performance of the algorithm is evaluated on MIT–BIH Database. This paper presents the application of Probabilistic Neural Networks (PNN) for the classification and detection of Electrocardiogram (ECG).
ECG analysis part (1) \ Mohammad Al-me`ani. , MSN, RN. almaani
The document discusses ECG analysis and provides details about:
1. The ECG records the electrical activity of the heart through electrodes placed on the skin. A 12-lead ECG provides views from 12 reference points.
2. The ECG traces the heart's electrical impulses on graph paper. It displays depolarization and repolarization processes and is used to diagnose various cardiac conditions.
3. The heart's conductive system includes the sinoatrial node, atrioventricular node, bundle of His, left and right bundle branches, and Purkinje fibers which coordinate heart rhythm and contractions.
Rule Based Identification of Cardiac Arrhythmias from Enhanced ECG Signals Us...CSCJournals
The detection of abnormal cardiac rhythms, automatic discrimination from rhythmic heart activity, became a thrust area in clinical research. Arrhythmia detection is possible by analyzing the electrocardiogram (ECG) signal features. The presence of interference signals, like power line interference (PLI), Electromyogram (EMG) and baseline drift interferences, could cause serious problems during the recording of ECG signals. Many a time, they pose problem in modern control and signal processing applications by being narrow in-band interference near the frequencies carrying crucial information. This paper presents an approach for ECG signal enhancement by combining the attractive properties of principal component analysis (PCA) and wavelets, resulting in multi-scale PCA. In Multi-Scale Principal Component Analysis (MSPCA), the PCA’s ability to decorrelate the variables by extracting a linear relationship and wavelet analysis are utilized. MSPCA method effectively processed the noisy ECG signal and enhanced signal features are used for clear identification of arrhythmias. In MSPCA, the principal components of the wavelet coefficients of the ECG data at each scale are computed first and are then combined at relevant scales. Statistical measures computed in terms of root mean square deviation (RMSD), root mean square error (RMSE), root mean square variation (RMSV) and improvement in signal to noise ratio (SNRI) revealed that the Daubechies based MSPCA outperformed the basic wavelet based processing for ECG signal enhancement. With enhanced signal features obtained after MSPCA processing, the detectable measures, QRS duration and R-R interval are evaluated. By using the rule base technique, projecting the detectable measures on a two dimensional area, various arrhythmias are detected depending upon the beat falling into particular place of the two dimensional area.
Comparison of Normal and Ventricular Tachyarrhythmic Electrocardiograms using...IDES Editor
Ventricular tachyarrhythmia is a cardiac disease in which
the electrocardiogram shows occurrence of ventricular tachycardia,
flutter and fibrillation. This tachycardia is often precursor to
ventricular fibrillation or stoppage of heart beats. In this paper a
comparison has been shown between the RdR maps of the RR
intervals of normal and ventricular tachyarrhythmic ECGs. RdR
maps are a scatter plot of RR intervals and change in the RR
intervals of ECGs. This plot has been chosen because of its
computational simplicity.
This document discusses using electrocardiogram (ECG) signals to detect heart diseases. It describes using wavelet transforms to extract features from ECG signals that can be used to detect peaks corresponding to heart activity. The algorithm involves applying a discrete wavelet transform, detecting R peaks, then P, Q, S, and T peaks. Heart conditions can then be identified by analyzing the timing of peaks. Daubechies wavelets are discussed as being effective for feature extraction from ECG signals due to their similarity in shape to the QRS complex.
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.
This document provides an overview of electrocardiography (ECG). It defines an ECG as a tracing of the heart's electrical activity. The objectives are to learn how to perform an ECG, interpret the results, and recognize various pathologies. Key points covered include electrode placement, components of the ECG wave, the physiology of cardiac conduction, interpreting the rate, rhythm, axis, and analyzing P, QRS, and T waves. Causes of axis deviations and details on analyzing the P wave are also summarized.
This document provides an overview of electrocardiogram (ECG) interpretation. It discusses the components of ECG complexes and intervals, including the P wave, QRS complex, and T wave. It correlates the ECG tracings with the electrical events in the heart during excitation and recovery. Key points include that the P wave represents atrial depolarization, the QRS complex represents ventricular depolarization, and the T wave represents ventricular recovery. It also discusses normal values for amplitude and duration of the various complexes.
Classification of ecg signal using artificial neural networkGaurav upadhyay
An electrocardiogram (ECG) is a bio-electrical signal which is used to record the heart's electrical activity with respect to time. Early and accurate detection is important in detecting heart diseases and choosing appropriate treatment for a patient. ECG signals are used as the parameter for detection of Cardiac diseases and most of the data comes from PhysioDataNet and MIT-BIH database .The pre-processing of ECG signal is performed with help of Wavelet toolbox and also used for feature extraction of ECG signal. The complete project is implemented on MATLAB platform. The performance of the algorithm is evaluated on MIT–BIH Database. This paper presents the application of Probabilistic Neural Networks (PNN) for the classification and detection of Electrocardiogram (ECG).
The document describes a thesis submitted for the degree of Bachelor of Technology in Electrical Engineering. The thesis aims to classify electrocardiogram (ECG) waveforms in real-time to diagnose cardiac diseases. It uses the discrete Daubechies wavelet transform to preprocess ECG signals and extract features. These features are then classified using a multilayer perceptron neural network. The classification model was implemented in SIMULINK software to simulate real-time detection and verify its performance. The thesis discusses ECG basics, wavelet transforms, neural networks, and presents results of signal decomposition, network training, and SIMULINK implementation.
A Survey on Ambulatory ECG and Identification of Motion ArtifactIJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
1) Unipolar electrograms provide information about substrate properties and wavefront dynamics through characteristics like voltage, morphology, and filtering that are not obtainable from bipolar recordings.
2) Specific morphologies and voltage amplitudes can indicate properties like isotropic or anisotropic conduction, sites of conduction block, and areas of slow conduction important for arrhythmia circuits.
3) High pass filtering unipolar signals between 4-16 Hz eliminates far-field components while maintaining morphology, approximating the local rate of change of voltage over time similar to bipolar recordings.
This document presents a method for detecting peaks in electrocardiogram (ECG) signals using wavelet transforms. The method first preprocesses the ECG signal to remove noise like baseline wandering and powerline interference. It then applies wavelet decomposition to the preprocessed ECG signal. The QRS complex is detected from the decomposed signal and the R peaks are located. Windows around the R peaks are used to detect the P, Q, S, and T peaks. ST segment analysis is also performed to determine if the ECG pattern indicates heart attack. The method is tested on ECG signals from a standard database and is able to accurately detect all the peaks.
The document discusses electrocardiograms (EKGs) and how they are used to analyze the electrical activity of the heart. It provides the following key points:
1. An EKG records the electrical signals produced by the heart during each beat and can be used to identify components like the P, QRS, and T waves that correspond to different phases of the heartbeat.
2. Abnormalities in the shape, timing, or presence of these components can provide clues about potential heart conditions like arrhythmias, damage to heart muscle, or blockages.
3. The experiment involves using EKG sensors to record a subject's heartbeat over time, identifying the normal waveform components and calculating heart rate,
This document discusses cardiac arrhythmias and techniques for arrhythmia monitoring. It covers topics such as arrhythmia monitors, QRS detection techniques, ambulatory monitoring, Holter monitoring, and details on signal processing, noise detection, QRS detection, morphology characterization, timing classification, beat labeling, and rhythm labeling in arrhythmia analysis systems. The key techniques discussed are template matching, filtering, detection of ventricular fibrillation using frequency analysis, and data compression using AZTEC coding. Ambulatory monitoring systems can provide either continuous multi-day recording or event recording triggered by symptoms.
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
The document discusses intracardiac electrograms (IEGMs) and catheter positions used in electrophysiology studies. It provides information on unipolar and bipolar recordings, including how bipolar signals are constructed and how they approximate the rate of change of the cardiac wavefront. The document also discusses how the direction of cardiac activation influences the electrocardiogram and how unipolar and bipolar recordings compare. Key factors that influence electrogram morphology such as conduction speed, myocardial mass, and tissue characteristics are also reviewed.
Patient monitoring systems continuously measure important physiological parameters of critically ill patients. There are several categories of patients who may need monitoring, including those recovering from surgery or serious illness in intensive care units. Patient monitoring systems organize and display information, correlate parameters, process data to detect abnormalities, provide therapy information, and ensure better care with fewer staff. Key parameters monitored include ECG, heart rate, blood pressure, temperature, and respiratory rate. Cardioscopes specifically monitor heart rate and ECG morphology to detect arrhythmias or changes indicative of serious conditions. They have slower sweep speeds and long persistence screens to enable observation of waveforms.
An electrocardiogram (ECG) records the electrical activity of the heart over time via electrodes placed on the skin. It detects the heart's electrical dipole by measuring the potential differences between electrodes. The ECG can be used to diagnose conditions like heart attacks, arrhythmias, electrolyte imbalances, and more by analyzing features of the P, QRS, and T waves as well as intervals between them. To perform an ECG, electrodes are attached to the patient's limbs after cleaning the skin. The ECG machine then records the signals from multiple leads to analyze the heart's rate, rhythm, and electrical axis.
Electrophysiologic studies use pacing techniques like programmed electrical stimulation (PES) to evaluate cardiac properties. PES involves pacing the heart with drive trains and extrastimuli to measure refractory periods, conduction dynamics, and induce arrhythmias. Pacing can be unipolar or bipolar, and incremental, decremental, or with extrastimuli. Refractory periods like the effective refractory period and relative refractory period are measured using premature extrastimuli during pacing. These techniques provide important information about normal cardiac function and arrhythmia mechanisms.
This document summarizes two cases of patients with accessory pathways. In the first case, the patient presented with a delta wave on their ECG indicating a right-sided accessory pathway. Mapping and ablation were successful in isolating the pathway located in the posterior septal region of the right atrium. In the second case, the patient had a normal ECG but ablation of a left-sided anterior septal pathway terminated the arrhythmia induced by pacing. Both cases demonstrate the mapping and successful ablation of concealed accessory pathways.
An electrocardiogram (EKG or ECG) records the electrical activity of the heart over time. It shows different wave components including the P wave, QRS complex, and T wave that represent the spread of electrical impulses through the heart's chambers and their contraction and relaxation. By analyzing the timing and appearance of these waves, doctors can detect abnormalities that may indicate heart conditions. In this experiment, students will use an EKG sensor to record their own heart activity, identify the wave components, and calculate their heart rate. They will also compare recordings from standard and alternate lead placements.
An electrocardiogram (ECG or EKG) records and measures the electrical activity of the heart. It detects abnormalities in heart rate and rhythm. An ECG uses electrodes placed on the skin to pick up electrical signals produced with each heartbeat. The signals are converted into waveforms that can be printed or monitored. ECGs are used to diagnose arrhythmias, heart attacks, heart failure and monitor patients with heart conditions in the hospital. They provide information on heart rate, rhythm and electrical conduction through the heart.
This document outlines a student project to detect heart rate from an ECG signal. It involves loading and manipulating an ECG signal, designing IIR notch filters to remove 50Hz, 100Hz and 150Hz noise, designing a band pass filter to remove muscle noise, and using a zero crossing algorithm to determine heart rate from the filtered ECG signal. The document provides background on ECG signals and the QRS complex used to calculate heart rate. It describes cascading the notch filters and plotting the original, corrupted and filtered signals. It also lists the tasks of adding noise to the ECG signal, designing the notch filters, filtering the corrupted signal and using zero crossing to obtain heart rate.
This document discusses electrical testing of pacemakers and pacemaker complications. It describes the components of a pacemaker including the battery, pacing impedance, pulse generator, and modes and mode switching. It then discusses testing various aspects of the pulse generator including output circuit, sensing circuit, timing circuit, and rate responsive pacing. Finally, it briefly outlines some common pacemaker complications such as pocket complications, lead issues, infections, and device malfunctions.
The document discusses the electrocardiogram (ECG or EKG), which records the electrical activity of the heart. It describes how the ECG can be used to measure the heart's rate and rhythm as well as detect issues like arrhythmias, defects, valve problems, and heart attacks. The document outlines the history of the ECG, from its invention by Willem Einthoven in 1895 to his receiving the Nobel Prize in 1924 for the device. It also explains the different lead configurations used to record the heart's electrical activity from multiple locations on the body simultaneously.
The document provides an overview of pacemaker components, physiology, and programming. It discusses the basic hardware components of pacemakers including the pulse generator, leads, and electrodes. It then covers pacing and sensing principles such as capture, impedance, and sensing thresholds. The remainder summarizes various pacing modes and algorithms for managing arrhythmias, rate response, and minimizing ventricular pacing.
The document provides information about electrocardiograms (ECGs), including what an ECG is, the types of pathology that can be identified from ECGs, ECG paper specifications, heart anatomy and the normal ECG signal, ECG leads, determining heart rate from ECGs, common rhythms, P waves, the PR interval, the QRS complex, identifying left and right bundle branch block, identifying left and right ventricular hypertrophy, Q waves, the ST segment and T waves. Key details are provided about normal ECG measurements and the signs of various cardiac conditions.
Este poema trata sobre un hombre que está enamorado y escribe el nombre de su amada en la oscuridad para expresar su amor sin que nadie se entere. Repite su nombre una y otra vez a altas horas de la noche, asegurado de que el amanecer traerá un nuevo día para expresar abiertamente sus sentimientos.
Los sistemas de numeración más antiguos como el egipcio, sumerio y maya eran aditivos, donde cada símbolo se añadía para representar las unidades, decenas, centenas, etc. Más tarde surgen los sistemas híbridos que combinaban lo aditivo con lo posicional. Finalmente, los sistemas posicionales como el indio resultaron más efectivos, donde la posición de cada cifra indica el orden de magnitud. El sistema posicional indio con el cero se convirtió en la base del sistema numérico moderno.
El documento describe la historia y el funcionamiento del multímetro. Su invención se atribuye a Donald Macadie en 1923 como un dispositivo que unificaba las mediciones de voltaje, corriente y resistencia. Se comercializó bajo la marca AVO y su diseño se ha mantenido prácticamente sin cambios hasta la actualidad. Un multímetro permite medir magnitudes eléctricas como voltaje, corriente y resistencia mediante el uso de un galvanómetro, bornas y un selector de funciones. Explica cómo realizar medidas de voltaje
The document describes a thesis submitted for the degree of Bachelor of Technology in Electrical Engineering. The thesis aims to classify electrocardiogram (ECG) waveforms in real-time to diagnose cardiac diseases. It uses the discrete Daubechies wavelet transform to preprocess ECG signals and extract features. These features are then classified using a multilayer perceptron neural network. The classification model was implemented in SIMULINK software to simulate real-time detection and verify its performance. The thesis discusses ECG basics, wavelet transforms, neural networks, and presents results of signal decomposition, network training, and SIMULINK implementation.
A Survey on Ambulatory ECG and Identification of Motion ArtifactIJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
1) Unipolar electrograms provide information about substrate properties and wavefront dynamics through characteristics like voltage, morphology, and filtering that are not obtainable from bipolar recordings.
2) Specific morphologies and voltage amplitudes can indicate properties like isotropic or anisotropic conduction, sites of conduction block, and areas of slow conduction important for arrhythmia circuits.
3) High pass filtering unipolar signals between 4-16 Hz eliminates far-field components while maintaining morphology, approximating the local rate of change of voltage over time similar to bipolar recordings.
This document presents a method for detecting peaks in electrocardiogram (ECG) signals using wavelet transforms. The method first preprocesses the ECG signal to remove noise like baseline wandering and powerline interference. It then applies wavelet decomposition to the preprocessed ECG signal. The QRS complex is detected from the decomposed signal and the R peaks are located. Windows around the R peaks are used to detect the P, Q, S, and T peaks. ST segment analysis is also performed to determine if the ECG pattern indicates heart attack. The method is tested on ECG signals from a standard database and is able to accurately detect all the peaks.
The document discusses electrocardiograms (EKGs) and how they are used to analyze the electrical activity of the heart. It provides the following key points:
1. An EKG records the electrical signals produced by the heart during each beat and can be used to identify components like the P, QRS, and T waves that correspond to different phases of the heartbeat.
2. Abnormalities in the shape, timing, or presence of these components can provide clues about potential heart conditions like arrhythmias, damage to heart muscle, or blockages.
3. The experiment involves using EKG sensors to record a subject's heartbeat over time, identifying the normal waveform components and calculating heart rate,
This document discusses cardiac arrhythmias and techniques for arrhythmia monitoring. It covers topics such as arrhythmia monitors, QRS detection techniques, ambulatory monitoring, Holter monitoring, and details on signal processing, noise detection, QRS detection, morphology characterization, timing classification, beat labeling, and rhythm labeling in arrhythmia analysis systems. The key techniques discussed are template matching, filtering, detection of ventricular fibrillation using frequency analysis, and data compression using AZTEC coding. Ambulatory monitoring systems can provide either continuous multi-day recording or event recording triggered by symptoms.
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
The document discusses intracardiac electrograms (IEGMs) and catheter positions used in electrophysiology studies. It provides information on unipolar and bipolar recordings, including how bipolar signals are constructed and how they approximate the rate of change of the cardiac wavefront. The document also discusses how the direction of cardiac activation influences the electrocardiogram and how unipolar and bipolar recordings compare. Key factors that influence electrogram morphology such as conduction speed, myocardial mass, and tissue characteristics are also reviewed.
Patient monitoring systems continuously measure important physiological parameters of critically ill patients. There are several categories of patients who may need monitoring, including those recovering from surgery or serious illness in intensive care units. Patient monitoring systems organize and display information, correlate parameters, process data to detect abnormalities, provide therapy information, and ensure better care with fewer staff. Key parameters monitored include ECG, heart rate, blood pressure, temperature, and respiratory rate. Cardioscopes specifically monitor heart rate and ECG morphology to detect arrhythmias or changes indicative of serious conditions. They have slower sweep speeds and long persistence screens to enable observation of waveforms.
An electrocardiogram (ECG) records the electrical activity of the heart over time via electrodes placed on the skin. It detects the heart's electrical dipole by measuring the potential differences between electrodes. The ECG can be used to diagnose conditions like heart attacks, arrhythmias, electrolyte imbalances, and more by analyzing features of the P, QRS, and T waves as well as intervals between them. To perform an ECG, electrodes are attached to the patient's limbs after cleaning the skin. The ECG machine then records the signals from multiple leads to analyze the heart's rate, rhythm, and electrical axis.
Electrophysiologic studies use pacing techniques like programmed electrical stimulation (PES) to evaluate cardiac properties. PES involves pacing the heart with drive trains and extrastimuli to measure refractory periods, conduction dynamics, and induce arrhythmias. Pacing can be unipolar or bipolar, and incremental, decremental, or with extrastimuli. Refractory periods like the effective refractory period and relative refractory period are measured using premature extrastimuli during pacing. These techniques provide important information about normal cardiac function and arrhythmia mechanisms.
This document summarizes two cases of patients with accessory pathways. In the first case, the patient presented with a delta wave on their ECG indicating a right-sided accessory pathway. Mapping and ablation were successful in isolating the pathway located in the posterior septal region of the right atrium. In the second case, the patient had a normal ECG but ablation of a left-sided anterior septal pathway terminated the arrhythmia induced by pacing. Both cases demonstrate the mapping and successful ablation of concealed accessory pathways.
An electrocardiogram (EKG or ECG) records the electrical activity of the heart over time. It shows different wave components including the P wave, QRS complex, and T wave that represent the spread of electrical impulses through the heart's chambers and their contraction and relaxation. By analyzing the timing and appearance of these waves, doctors can detect abnormalities that may indicate heart conditions. In this experiment, students will use an EKG sensor to record their own heart activity, identify the wave components, and calculate their heart rate. They will also compare recordings from standard and alternate lead placements.
An electrocardiogram (ECG or EKG) records and measures the electrical activity of the heart. It detects abnormalities in heart rate and rhythm. An ECG uses electrodes placed on the skin to pick up electrical signals produced with each heartbeat. The signals are converted into waveforms that can be printed or monitored. ECGs are used to diagnose arrhythmias, heart attacks, heart failure and monitor patients with heart conditions in the hospital. They provide information on heart rate, rhythm and electrical conduction through the heart.
This document outlines a student project to detect heart rate from an ECG signal. It involves loading and manipulating an ECG signal, designing IIR notch filters to remove 50Hz, 100Hz and 150Hz noise, designing a band pass filter to remove muscle noise, and using a zero crossing algorithm to determine heart rate from the filtered ECG signal. The document provides background on ECG signals and the QRS complex used to calculate heart rate. It describes cascading the notch filters and plotting the original, corrupted and filtered signals. It also lists the tasks of adding noise to the ECG signal, designing the notch filters, filtering the corrupted signal and using zero crossing to obtain heart rate.
This document discusses electrical testing of pacemakers and pacemaker complications. It describes the components of a pacemaker including the battery, pacing impedance, pulse generator, and modes and mode switching. It then discusses testing various aspects of the pulse generator including output circuit, sensing circuit, timing circuit, and rate responsive pacing. Finally, it briefly outlines some common pacemaker complications such as pocket complications, lead issues, infections, and device malfunctions.
The document discusses the electrocardiogram (ECG or EKG), which records the electrical activity of the heart. It describes how the ECG can be used to measure the heart's rate and rhythm as well as detect issues like arrhythmias, defects, valve problems, and heart attacks. The document outlines the history of the ECG, from its invention by Willem Einthoven in 1895 to his receiving the Nobel Prize in 1924 for the device. It also explains the different lead configurations used to record the heart's electrical activity from multiple locations on the body simultaneously.
The document provides an overview of pacemaker components, physiology, and programming. It discusses the basic hardware components of pacemakers including the pulse generator, leads, and electrodes. It then covers pacing and sensing principles such as capture, impedance, and sensing thresholds. The remainder summarizes various pacing modes and algorithms for managing arrhythmias, rate response, and minimizing ventricular pacing.
The document provides information about electrocardiograms (ECGs), including what an ECG is, the types of pathology that can be identified from ECGs, ECG paper specifications, heart anatomy and the normal ECG signal, ECG leads, determining heart rate from ECGs, common rhythms, P waves, the PR interval, the QRS complex, identifying left and right bundle branch block, identifying left and right ventricular hypertrophy, Q waves, the ST segment and T waves. Key details are provided about normal ECG measurements and the signs of various cardiac conditions.
Este poema trata sobre un hombre que está enamorado y escribe el nombre de su amada en la oscuridad para expresar su amor sin que nadie se entere. Repite su nombre una y otra vez a altas horas de la noche, asegurado de que el amanecer traerá un nuevo día para expresar abiertamente sus sentimientos.
Los sistemas de numeración más antiguos como el egipcio, sumerio y maya eran aditivos, donde cada símbolo se añadía para representar las unidades, decenas, centenas, etc. Más tarde surgen los sistemas híbridos que combinaban lo aditivo con lo posicional. Finalmente, los sistemas posicionales como el indio resultaron más efectivos, donde la posición de cada cifra indica el orden de magnitud. El sistema posicional indio con el cero se convirtió en la base del sistema numérico moderno.
El documento describe la historia y el funcionamiento del multímetro. Su invención se atribuye a Donald Macadie en 1923 como un dispositivo que unificaba las mediciones de voltaje, corriente y resistencia. Se comercializó bajo la marca AVO y su diseño se ha mantenido prácticamente sin cambios hasta la actualidad. Un multímetro permite medir magnitudes eléctricas como voltaje, corriente y resistencia mediante el uso de un galvanómetro, bornas y un selector de funciones. Explica cómo realizar medidas de voltaje
La ley regula cuatro aspectos principales de la publicidad en Ecuador:
1) La propiedad intelectual sobre los contenidos publicitarios.
2) La producción nacional de la publicidad difundida en medios ecuatorianos, la cual debe ser realizada mayormente por ecuatorianos.
3) La difusión de publicidad producida en el extranjero está limitada a ciertos casos específicos.
4) Los contenidos del mensaje publicitario no pueden violar derechos fundamentales.
YouTube es un sitio web que permite a los usuarios subir y ver videos en streaming sin necesidad de descargarlos. Fue creado en 2005 y adquirido por Google en 2006. Scribd permite compartir documentos en varios formatos e incrustarlos en páginas web. SlideShare es un sitio para compartir presentaciones de diapositivas en varios formatos. About.me ofrece una tarjeta de visita online personalizable con enlaces a perfiles sociales. Delicious permite agregar y categorizar páginas web visitadas mediante etiquetas.
El documento presenta las pautas para el uso del certificado y sello de empresas e instituciones certificadas en materia ambiental en el estado de Nayarit, México. Establece que sólo las empresas certificadas pueden usar el sello como reconocimiento de su cumplimiento normativo ambiental. Además, especifica las versiones, colores, tipografía, escalas permitidas y restricciones para el correcto uso del sello certificado.
Las comunicaciones son fundamentales en la sociedad moderna y han evolucionado desde el teléfono fijo hasta los celulares. El primer celular, el Motorola DynaTAC de 1983, era analógico, pesado y caro. Los celulares luego adoptaron la tecnología digital y se hicieron más asequibles, extendiendo su uso a más personas. Hoy en día existen celulares de gama baja, media y alta con diferentes capacidades y precios.
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An Electrocardiograph based Arrythmia Detection SystemDr. Amarjeet Singh
Cardiac disorders turn out to be a serious disease if
not diagnosed and treated at the earliest. Arrhythmia is a
cardiac disorder that exists as a result of irregular heart beat
conditions. There are several variants in this type of disorder
which can be only diagnosed only when patient is under an
intensive care conditions and also the patient with such
disorder do not experience and physical symptoms. Such
diseases turn out to be deadly if not treated early. A detection
system is thus required which is capable of detecting these
arrhythmias in real time and aid in the diagnosis. An FPGA
based arrhythmia detection system is designed and
implemented here which can detect second degree AV block
type of arrhythmia. The designed system was simulated and
tested with ECG signal from MIT-BH database and the
results revealed that a robust arrhythmia detection system
was implemented.
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.
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.
Iaetsd a review on ecg arrhythmia detectionIaetsd Iaetsd
This document proposes a method to detect cardiac arrhythmias from electrocardiogram (ECG) signals using double density discrete wavelet transformation (DD-DWT). The method involves preprocessing the ECG signal, extracting features using DD-DWT, and classifying rhythms using support vector machines (SVM). Features extracted include temporal intervals between peaks and morphological characteristics. DD-DWT decomposes the ECG into sub-bands, allowing subtle changes to be detected. SVM is used for classification. The method is tested on the MIT-BIH Arrhythmia database and is found to provide better arrhythmia detection compared to existing methods.
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.
This document discusses algorithms for detecting QRS complexes in electrocardiogram (ECG) signals. It describes the wavelet transform-based algorithm developed by the authors, which involves denoising the ECG signal using wavelet coefficients and detecting QRS complexes. This algorithm is compared to existing AF2 and Pan-Tompkins algorithms, and is found to produce better results for ECG signal denoising and QRS detection. The document provides details on the wavelet transform algorithm and existing algorithms.
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.
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.
This document presents a novel algorithm for automated detection of heartbeats in an electrocardiogram (ECG) signal using morphological filtering and Daubechies wavelet transform. The algorithm consists of three stages: 1) preprocessing using mathematical morphology operations to remove noise and baseline wander, 2) Daubechies wavelet transform decomposition to facilitate heartbeat detection, and 3) feature extraction to identify the QRS complex and detect heartbeats by analyzing the wavelet coefficients. Morphological filtering preserves the original ECG signal shape while removing impulsive noise, and wavelet transform aids in analyzing the non-stationary ECG signal. The algorithm aims to provide accurate and reliable heartbeat detection for diagnosing cardiac conditions.
The document discusses ECG signal analysis and abnormality detection using artificial neural networks. It defines normal and abnormal ECG signals, describing abnormalities like bradycardia and tachycardia. Two algorithms are described for detecting abnormalities: one analyzes heart rate and the other detects general heart diseases. An ANN system is used for ECG analysis and classification, taking spectral entropy, Poincare plot geometry, and largest Lyapunov exponent as inputs to classify eight cardiac conditions.
Cardio Logical Signal Processing for Arrhythmia Detection with Comparative An...IRJET Journal
This document summarizes research on detecting cardiac arrhythmias by analyzing electrocardiogram (ECG) signals. ECG signals are often contaminated with power line interference that must be removed using a notch filter before features can be extracted. The researchers compare the impact of different Q-factor values for the notch filter on the QRS complex of the ECG. They detect the QRS complex using difference operation method and then calculate features of the R-peak like sharpness and slope. A linear classifier is then used to classify signals as normal or arrhythmic based on these features.
Identification of Myocardial Infarction from Multi-Lead ECG signalIJERA Editor
Electrocardiogram (ECG) is the cheap and noninvasive method of depicting the heart activity and abnormalities.
It provides information about the functionality of the heart. It is the record of variation of bioelectric potential
with respect to time as the human heart beats. The classification of ECG signals is an important application since
the early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through
appropriate treatment. Since the ECG signals, while recording are contaminated by several noises it is necessary
to preprocess the signals prior to classification. Digital filters are used to remove noise from the signal. Principal
component analysis is applied on the 12 lead signal to extract various features. The present paper shows the
unique feature, point score calculated on the basis of the features extracted from the ECG signal. The point
score calculation is tested for 40 myocardial infarction ECG signals and 25 Normal ECG signals from the PTB
Diagnostic database with 94% sensitivity.
QRS Detection Algorithm Using Savitzky-Golay FilterIDES Editor
This paper presents a modification to the Pan-Tompkins algorithm for QRS detection in electrocardiogram (ECG) signals. The Pan-Tompkins algorithm uses a high pass filter and differentiator to detect QRS complexes. This paper replaces the high pass filter and differentiator with a Savitzky-Golay filter. The modified algorithm and original Pan-Tompkins algorithm are applied to normal and diseased ECG data showing ventricular tachyarrhythmia. The results show that the modified algorithm can detect QRS complexes with higher amplitudes compared to the original algorithm, without requiring a high pass filter or differentiator.
The document discusses the electrocardiogram (ECG), which measures and records the electrical activity of the heart. An ECG represents the atrial and ventricular depolarization and repolarization that occurs with each heartbeat. ECGs are obtained via electrodes placed on the skin and measure the potential differences. The ECG signal is analyzed in both the time and frequency domains to study cardiac features. A typical ECG consists of P, Q, R, S, and T waves that represent different electrical events in the heart. Digital signal processing is used to filter noise and detect the heart rate from the ECG signal.
Heart rate detection using hilbert transformeSAT Journals
Abstract The electrocardiogram (ECG) is a well known method that can be used to measure Heart Rate Variability (HRV). This paper describes a procedure for processing electrocardiogram signals (ECG) to detect Heart Rate Variability (HRV). In recent years, there have been wide-ranging studies on Heart rate variability in ECG signals and analysis of Respiratory Sinus Arrhythmia (RSA). Normally the Heart rate variability is studied based on cycle length variability, heart period variability, RR variability and RR interval tachogram. The HRV provides information about the sympathetic-parasympathetic autonomic stability and consequently about the risk of unpredicted cardiac death. The heart beats in ECG signal are detected by detecting R-Peaks in ECG signals and used to determine useful information about the various cardiac abnormalities. The temporal locations of the R-wave are identified as the locations of the QRS complexes. In the presence of poor signal-to-noise ratios or pathological signals and wrong placement of ECG electrodes, the QRS complex may be missed or falsely detected and may lead to poor results in calculating heart beat in turn inter-beat intervals. We have studied the effects of number of common elements of QRS detection methods using MIT/BIH arrhythmia database and devised a simple and effective method. In this method, first the ECG signal is preprocessed using band-pass filter; later the Hilbert Transform is applied on filtered ECG signal to enhance the presence of QRS complexes, to detect R-Peaks by setting a threshold and finally the RR-intervals are calculated to determine Heart Rate. We have implemented our method using MATLAB on ECG signal which is obtained from MIT/BIH arrhythmia database. Our MATLAB implementation results in the detection of QRS complexes in ECG signal, locate the R-Peaks, computes Heart Rate (HR) by calculating RR-internal and plotting of HR signal to show the information about HRV. Index Terms: ECG, QRS complex, R-Peaks, HRV, Heart Rate signal, RSA, Hilbert Transform, Arrhythmia, MIT/BIH, MATLAB and Lynn’s filters
Automatic Detection of Heart Disease Using Discreet Wavelet Transform and Art...Editor IJMTER
ECG plays an important role for analysis and diagnosis of heart disease. ECG signals are
affected by different noises. These noises can be removed by de noise the ECG signal. After de
noising ECG signals, a pure ECG signal is used to detect ECG parameters. Then Feature extraction
of ECG signal is carried out by DWT techniques which are applied to ANN for classification to
detect cardiac arrhythmia. This paper introduces the Electrocardiogram (ECG) pattern recognition
method based on wavelet transform and neural network technique has been used to classify two
different types of arrhythmias, namely, Left bundle branch block (LBBB), Right bundle Branch
block (RBBB) with normal ECG signal. The MIT-BIH arrhythmias ECG Database has been used for
training and testing our neural network based classifier. The simulation results given at the end.
Classification and Detection of ECG-signals using Artificial Neural NetworksGaurav upadhyay
An electrocardiogram (ECG) is a bio-electrical signal which is used to record the heart's electrical activity with respect to time. Early and accurate detection is important in detecting heart diseases and choosing appropriate treatment for a patient. ECG signals are used as the parameter for detection of Cardiac diseases and most of the data comes from PhysioDataNet and MIT-BIH database .The pre-processing of ECG signal is performed with help of Wavelet toolbox and also used for feature extraction of ECG signal. The complete project is implemented on MATLAB platform. The performance of the algorithm is evaluated on MIT–BIH Database. This paper presents the application of Probabilistic Neural Networks (PNN) for the classification and detection of Electrocardiogram (ECG).
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.
IRJET- Classification and Identification of Arrhythmia using Machine Lear...IRJET Journal
1) The document discusses a study that uses machine learning techniques to classify and identify different types of arrhythmias based on electrocardiography (ECG) data.
2) It proposes a system that uses the Pan-Tompkins algorithm to preprocess ECG signals and remove noise, extracts 11 features from the filtered signals, and trains a feedforward neural network classifier using the Levenberg-Marquardt algorithm to classify heartbeats into 4 arrhythmia classes.
3) The results show that the trained neural network achieves an accuracy of 96.1% based on evaluation using a confusion matrix on the test data.
IRJET- Classification and Identification of Arrhythmia using Machine Lear...
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1. Rameshwari S Mane, A N Cheeran, Vaibhav D Awandekar, Priya Rani / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.327-332
Cardiac Arrhythmia Detection By ECG Feature Extraction
Rameshwari S Mane1, A N Cheeran2, Vaibhav D Awandekar3 and Priya
Rani4
1,4
M. Tech. Student (Electronics), VJTI, Mumbai, Maharashtra
2
Associate Professor, VJTI, Mumbai, Maharashtra
3
A3 RMT Pvt. Ltd. SINE, IIT Mumbai, Maharashtra
ABSTRACT
Electrocardiogram (ECG) is a non- the wavelet decomposition. These coefficients are
invasive technique used as a primary diagnostic fed to the back-propagation neural network which
tool for detecting cardiovascular diseases. One of classifies the arrhythmias, [8] presents the
the important cardiovascular diseases is cardiac classification of cardiac arrhythmia based on the
arrhythmia. Computer-assisted cardiac signal variation characteristic of each beat type.
arrhythmia detection and classification can play Using the principal component analysis estimation,
a significant role in the management of cardiac the detection selects the class by searching the
disorders. This paper presents an algorithm minimal norm of the error vector obtained by basis
developed using Python 2.6 simulation tool for of each type, Autoregressive modeling (AR) has
the detection of cardiac arrhythmias e.g. been applied to ECG signals and the AR coefficients
premature ventricular contracture (PVC), right have been used for classification into arrhythmias in
bundle branch block (R or RBBB) and left [4], and in [9] the estimation of instantaneous
bundle branch block (L or LBBB) by extracting frequency (IF) of an ECG signal is used as a method
various features and vital intervals (i.e. RR, QRS, for carrying out detection of cardiac disorder. Based
etc) from the ECG waveform. The proposed on IF estimates, a classifier has been designed to
method was tested over the MIT-BIH differentiate a diseased signal from a normal one.
Arrhythmias Database. This paper presents a method for the recognition of
various categories of cardiac arrhythmias based on
Keywords - Cardiac Arrhythmia, ECG, left bundle their characteristics features extracted from ECG
branch block (L or LBBB), premature ventricular signals. For PVC beat detection RR interval ratio
contracture (PVC), right bundle branch block (R or and energy of beat is calculated. Dominant (slurred)
RBBB). or notched (M-shaped) R wave, dominant (slurred) S
wave, QRS duration and direction of T wave are
1. INTRODUCTION used for the detection of LBBB and RBBB.
An ECG is a graphic representation of the The rest of this paper is organized as follows:
electrical activity of the heart muscle. ECG is the Section 2, presents the ECG signal processing.
main diagnostic approach for cardiac rhythm Section 3 describes the detection of arrhythmia.
evaluation. Cardiac arrhythmia is the disturbance in Finally, the section 4 & 5 summarizes the result &
the regular rhythmic activity of the heart. Different conclusion of this work.
characteristics such as shapes, interval and
amplitudes of ECG reflect different arrhythmias. 2. ECG SIGNAL PROCESSING
Arrhythmia may be caused by irregular firing The proposed method includes processing
patterns from the SA node or due to abnormal and parameter calculation of ECG and then detection
activity from other parts of the heart and indicates a of cardiac arrhythmia using an algorithm developed
serious problem that may lead to stroke or sudden in Python 2.6 simulation tool. The algorithm is
cardiac death. The vital and weight bearing types of tested over MIT-BIH Arrhythmia database.
arrhythmia are ventricular tachycardia ventricular
fibrillation, premature ventricular contracture (PVC), 2.1 ECG Denoising
right bundle branch block (R or RBBB) and left ECG signals are usually corrupted by
bundle branch block (L or LBBB). several noises like 50 Hz power line interferences,
In the past few years, a lot of research has baseline wander and electro mayogram (EMG).
been carried out on the automatic classification of Therefore, the signal needs to be preprocessed
ECG. These attempted to characterize arrhythmia before applying any detection algorithm. Wavelet
using various features, including waveform shape denoising and S- Golay Filter is used for removal of
features i.e. using ECG morphology and heartbeat baseline wander and high frequency noise. ECG
interval [3], in [2] a set of discrete wavelet transform unfiltered data is passed through baseline wandering
(DWT) coefficient, which contain the maximum removal function, followed by wavelet based high
information about the arrhythmia, is selected from frequency noise removal. The data is then smoothed
327 | P a g e
2. Rameshwari S Mane, A N Cheeran, Vaibhav D Awandekar, Priya Rani / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.327-332
further using S-Golay filter. All the modules are over the time interval n1 ≤ n ≤ n2 is defined as
implemented and simulated in python. n2
E | x[n] |2 .The energy of ECG signal is
2.2. ECG Parameter Calculation n1
The purpose of the feature extraction calculated for each beat and RR interval ratio is also
process is to select and retain relevant information calculated. The threshold for energy is taken as 65%
from original signal. The Feature Extraction stage of maximum energy and for ratio 70% of maximum
extracts diagnostic information from the ECG signal. ratio value. If RR interval ratio and energy is less
In order to detect the peaks, specific details of the than threshold PVC beat is detected. Figure 1 shows
signal are selected. The detection of R peak is the the detection of PVC bigeminy and couplet. In
first step of feature extraction. R peak is detected by Figure 2 PVC trigeminy is detected.
using Pan-Tompkins algorithm [1].
The intervals QRS, PR and QT are
calculated by searching for corresponding onset and
offset points in the wave. The separate logic was
implemented for identifying P-onset, Q and S points,
once R-peak was located using Pan-Tompkins
algorithm. The window is selected around R-wave
and the minimum of the points within this window
are declared as Q and S points. In the differentiated
signal ECGDER, a window of 155 ms is defined
starting 225 ms before R-peak position. In this
window, we search for maximum and minimum
signal value. The P-wave peak is assumed to occur
at the zero-crossings between maximum and
minimum values within the selected window. Once Figure 1.Detection of PVC Bigeminy and couplet
P-wave peak is found, we proceed to locate
waveform boundary-P-wave onset. Similarly T wave
peak is obtained by changing the window size.
3. DETECTION OF ARRHYTHMIA
3.1 Detection of PVC
A premature ventricular contraction (PVC),
also known as a premature ventricular complex,
ventricular premature contraction (or complex or
complexes) (VPC), is a relatively common event
where the heartbeat is initiated by the heart
ventricles rather than by the sinoatrial node, the
normal heartbeat initiator.
ECG Characteristics of PVC patient
1. Broad QRS complex (≥ 120 ms) with abnormal Figure 2.Detection of PVC Trigeminy
morphology.
2. Premature — i.e. occurs earlier than would be 3.2. Detection of LBBB
expected for the next sinus impulse. Normally the septum is activated from left
3. Discordant ST segment and T wave changes. to right, producing small Q waves in the lateral
There are five different types Of PVC, first leads. In LBBB, the normal direction of septal
Bigeminy every other beat is a PVC, second depolarisation is reversed (becomes right to left), as
Trigeminy every third beat is a PVC, third the impulse spreads first to the RV via the right
Quadrigeminy every fourth beat is a PVC, forth bundle branch and then to the LV via the septum.
Couplet two consecutive PVCs and last Triplet three This sequence of activation extends the QRS
consecutive PVCs. duration to > 120 ms and eliminates the normal
The main characteristic of PVCs is its premature septal Q waves in the lateral leads. As the ventricles
occurrence. This characteristic is measured by are activated sequentially (right, then left) rather
relating the RR interval lengths of heart cycles than simultaneously, this produces a broad or
adjacent to the PVC. In case of a PVC, these lengths notched (‗M‘-shaped) R wave in the lateral leads [6]
should be notoriously different .The method for [11].
classifying the abnormal complexes from the normal ECG Characteristics LBBB patient:
ones is based on the concepts of RR interval ratio of 1. QRS duration greater than or equal to 120 ms in
detected R peaks and energy analysis of ECG signal adults
[7]. The total energy in a discrete time signal x[n]
328 | P a g e
3. Rameshwari S Mane, A N Cheeran, Vaibhav D Awandekar, Priya Rani / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.327-332
2. Broad notched or slurred R wave in leads I, aVL,
V5, and V6 and an occasional RS pattern in V5 and
V6 attributed to displaced transition of QRS
complex.
3. Absent q waves in leads I, V5, and V6, but in the
lead aVL, a narrow q wave may be present in the
absence of myocardial pathology.
4. T wave usually opposite in direction to QRS.
5. Dominant S wave in V1
Two leads lead V6 and lead V1 are mainly
considered for detection of LBBB.
3.2.1. Processing of lead V6
Lead V6 is processed for detection of dominant
(slurred) or notched (M-shaped) R wave, QRS
duration and direction of T wave.
The dominant R wave is detected by Figure 4.Dominent Rwave in LBBB
checking each point between Q and S is greater than
minimum value of S point for every beat. 3.2.2. Processing of lead V1
QS6[i] >smin Lead V1 is processed for detection of dominant
QRS duration is calculated by taking (slurred) S wave, QRS duration and direction of T
difference between S point and Q point and then wave.
averaging the difference. It should be greater than The dominant S wave is detected by
120 ms. checking each point between Q and S is less than
QRS>120 minimum value of S point for every beat.
To detect M-shaped R wave array of points QS1[i] <smin
between Q and R is obtained for every beat. If the QRS duration is calculated by taking
difference between every point and previous point is difference between S point and Q point and then
greater than zero it is normal R wave else notched R averaging the difference. It should be greater than
wave. 120 ms.
QR6[i]-QR6[i-1]<0 QRS1>120
If the average value of T peak is less than If tiny R wave is present then Rwave i.e
zero then direction of T wave is opposite to QRS duration between Q and R and Swave i.e. duration
complex of lead V6. between R and S is obtained. For dominant S wave
Avg(T peak6)<0 Rwave duration is less than Swave.
M-shaped and dominant Rwave is shown in Figure 3 Swave>Rwave
and 4. If the average value of T peak is greater
than zero then direction of T wave is opposite to
QRS complex of lead V1.
Avg(T peak1)>0
Figure 5 and 6 shows dominant Swave and Swave
with tiny Rwave in LBBB.
Figure 3.M-shaped Rwave in LBBB
Figure 5.Dominent Swave in LBBB
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4. Rameshwari S Mane, A N Cheeran, Vaibhav D Awandekar, Priya Rani / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.327-332
Figure 7 and 8 shows dominant Swave in RBBB.
Figure 6.Dominent Swave with tiny Rwave in LBBB
Figure 7.Dominent Swave in RBBB
3.3. Detection of RBBB
In RBBB, activation of the right ventricle is
delayed as depolarisation has to spread across the
septum from the left ventricle. The left ventricle is
activated normally, meaning that the early part of the
QRS complex is unchanged. The delayed right
ventricular activation produces a secondary R wave
(R‘) in the right precordial leads (V1-3) and a wide,
slurred S wave in the lateral leads [6] [11].
ECG Characteristics RBBB
1. QRS duration greater than or equal to 120 ms in
adults
2. RSR‘ pattern in V1-3 (‗M-shaped‘ QRS complex)
3. S wave of greater duration than R wave in the Figure 8.Dominent Swave in RBBB
lateral leads (I, aVL, V5-6)
Two leads lead V6 and lead V1 are mainly 3.3.2. Processing of lead V1
considered for detection of LBBB. Lead V1 is processed for detection of dominant
(slurred) or notched (M-shaped) R wave, QRS
3.3.1. Processing of lead V6 duration and direction of T wave.
Lead V6 is processed for detection of dominant The dominant R wave is detected by
(slurred) S wave, QRS duration and direction of T checking each point between Q and S is greater than
wave. minimum value of S point for every beat.
Rwave i.e duration between Q point and QS1[i] >smin
point of intersection of signal with isoelectric line QRS duration is calculated by taking
and Swave i.e. duration between point of difference between S point and Q point and then
intersection of signal with isoelectric line and S averaging the difference. It should be greater than
point is obtained. For dominant S wave Rwave 120 ms.
duration is less than Swave. QRS1>120
Swave>Rwave The notched R wave is detected by
QRS duration is calculated by taking detecting number of zero crossing between Q and S
difference between S point and Q point and then point. If it is greater than 3 R wave is notched [5].
averaging the difference. It should be greater than No. of zero crossing between Q and S >3
120 ms. If the average value of T peak is less than
QRS>120 zero then it is T wave inversion.
If the average value of T peak is greater Avg(T peak1)<0
than zero then there is no T wave inversion. In Figure 9 dominant Rwave is shown. Figure 10
Avg(T peak6)>0 gives notched Rwave in RBBB.
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5. Rameshwari S Mane, A N Cheeran, Vaibhav D Awandekar, Priya Rani / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.327-332
positive is that tests positive for a condition and is
positive (i.e; have the condition). False positive is
that tests positive but is negative. False negative is
that tests negative but is positive. Similarly
specificity and sensitivity are calculated for LBBB
and RBBB. The final result of detection is provided
in Table1.
Arrhythmia Sensitivity Specificity
PVC 96.15% 92.59%
LBBB 91.66% 95.65%
RBBB 96.15% 96.15%
Figure 9.Dominant Rwave in RBBB Table 1.Result of classification
5. CONCLUSION
This paper presents an efficient and simple
detection algorithm based on feature extraction of
ECG signal. The efficiency of detection depends on
the proper extraction of P-Q-R-S and T points for
ECG signal. The overall specificity above 92% and
sensitivity above 91% is obtained, which is
satisfactorily high considering simplicity. Because of
its simplicity it can be a better choice in clinical field
of cardiac arrhythmia detection. The efficiency can
be further improved by using higher order statistics
and support vector machine.
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of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
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