Sparse representation of ECG signals for automated recognition of cardiac arrhythmias

2018 ◽  
Vol 105 ◽  
pp. 49-64 ◽  
Author(s):  
Sandeep Raj ◽  
Kailash Chandra Ray
Keyword(s):  
Sparse Representation ◽  
Cardiac Arrhythmias ◽  
Automated Recognition ◽  
Ecg Signals

Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals: A Survey

IRBM ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 185-194 ◽  
Author(s):  
S. Sahoo ◽  
M. Dash ◽  
S. Behera ◽  
S. Sabut
Keyword(s):  
Machine Learning ◽  
Cardiac Arrhythmias ◽  
Learning Approach ◽  
Ecg Signals ◽  
Machine Learning Approach

A CNN Model for Cardiac Arrhythmias Classification Based on Individual ECG Signals

2022 ◽  
Author(s):  
Yuan Zhang ◽  
Sen Liu ◽  
Zhihui He ◽  
Yuwei Zhang ◽  
Changming Wang
Keyword(s):  
Cardiac Arrhythmias ◽  
Ecg Signals

scholarly journals Multi-lead ECG Compression Based on Compressive Sensing

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Javad Afshar Jahanshahi
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Matching Pursuit ◽  
Optimization Technique ◽  
Orthogonal Matching Pursuit ◽  
Wavelet Basis ◽  
Basis Matrix ◽  
Ecg Signals ◽  
Gaussian Kernels ◽  
Ecg Compression

Compressed Sensing (CS) has been considered a very effective means of reducing energy consumption at the energy-constrained wireless body sensor networks for monitoring the multi-lead Electrocardiogram (MECG) signals. This paper develops the compressed sensing theory for sparse modeling and effective multi-channel ECG compression. A basis matrix with Gaussian kernels is proposed to obtain the sparse representation of each channel, which showed the closest similarity to the ECG signals. Thereafter, the greedy orthogonal matching pursuit (OMP) method is used to obtain the sparse representation of the signals. After obtaining the sparse representation of each ECG signal, the compressed sensing theory could be used to compress the signals as much as possible. Following the compression, the compressed signal is reconstructed utilizing the greedy orthogonal matching pursuit (OMP) optimization technique to demonstrate the accuracy and reliability of the algorithm. Moreover, as the wavelet basis matrix is another sparsifying basis to sparse representations of ECG signals, the compressed sensing is applied to the ECG signals using the wavelet basis matrix. The simulation results indicated that the proposed algorithm with Gaussian basis matrix reduces the reconstruction error and increases the compression ratio.

Human Identification From ECG Signals Via Sparse Representation of Local Segments

2013 ◽  
Vol 20 (10) ◽  
pp. 937-940 ◽  
Author(s):  
Jin Wang ◽  
Mary She ◽  
Saeid Nahavandi ◽  
Abbas Kouzani
Keyword(s):  
Sparse Representation ◽  
Human Identification ◽  
Ecg Signals

An mHealth System for Monitoring of Children with Suspected Cardiac Arrhythmias

2013 ◽  
Vol 1 (2) ◽  
pp. 54-71
Author(s):  
E. Kyriacou ◽  
D. Hoplaros ◽  
P. Chimonidou ◽  
G. Matheou ◽  
M. Millis ◽  
...  
Keyword(s):  
Communication Networks ◽  
Cardiac Arrhythmias ◽  
Continuous Monitoring ◽  
Environmental Parameters ◽  
Recording Device ◽  
Design Development ◽  
Ecg Signals ◽  
First Case ◽  
The Subject ◽  
Time Acquisition

Arrhythmia is one of the most difficult problems in cardiology and especially in pediatric cardiology. In this study, the authors present a mobile health (m-health) system that can be used for continuous monitoring of children (ages 0-16 years) with suspected cardiac arrhythmias. The system is able to carry-out real-time acquisition and transmission of ECG signals, and facilitate an alarm scheme able to identify possible arrhythmias so as to notify the on-call doctor and the relatives of the child that an event or something that denotes malfunction is happening. In general the problem has been divided into two cases. The first case is called “in-house case”, where the subject is located in his/her house. While the second case is called “moving-patient case”, where the subject might be located anywhere else. The authors’ goal is the continuous 24 hours, monitoring of the child. During the “in house case”, a sensor network installed in the child’s house is used in order to continuously record ECG signals from the patient as well as environmental parameters. The second case is more general. For this case, the child is monitored using the same ECG recording device but the signals are transmitted, through a mobile device, directly to the central monitoring system. The transmission is performed through the use of 2.5G, or 3G, mobile communication networks. The system design, development and technical tests (using an ECG simulator and 20 volunteers) are reported in this paper. The future steps will be the further evaluation of the system on children with suspected cardiac arrhythmias.

scholarly journals ECG-Based Identification of Sudden Cardiac Death through Sparse Representations

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7666
Author(s):  
Josue R. Velázquez-González ◽  
Hayde Peregrina-Barreto ◽  
Jose J. Rangel-Magdaleno ◽  
Juan M. Ramirez-Cortes ◽  
Juan P. Amezquita-Sanchez
Keyword(s):  
Sudden Cardiac Death ◽  
Sparse Representation ◽  
Cardiac Death ◽  
Heart Function ◽  
Reference Signal ◽  
Extraction Process ◽  
Electrical Signal ◽  
Dynamic Feature ◽  
Ecg Signals ◽  
Representation Technique

Sudden Cardiac Death (SCD) is an unexpected sudden death due to a loss of heart function and represents more than 50% of the deaths from cardiovascular diseases. Since cardiovascular problems change the features in the electrical signal of the heart, if significant changes are found with respect to a reference signal (healthy), then it is possible to indicate in advance a possible SCD occurrence. This work proposes SCD identification using Electrocardiogram (ECG) signals and a sparse representation technique. Moreover, the use of fixed feature ranking is avoided by considering a dictionary as a flexible set of features where each sparse representation could be seen as a dynamic feature extraction process. In this way, the involved features may differ within the dictionary’s margin of similarity, which is better-suited to the large number of variations that an ECG signal contains. The experiments were carried out using the ECG signals from the MIT/BIH-SCDH and the MIT/BIH-NSR databases. The results show that it is possible to achieve a detection 30 min before the SCD event occurs, reaching an an accuracy of 95.3% under the common scheme, and 80.5% under the proposed multi-class scheme, thus being suitable for detecting a SCD episode in advance.

scholarly journals Automatic Classification of Cardiac Arrhythmias based on ECG Signals Using Transferred Deep Learning Convolution Neural Network

2021 ◽  
Vol 2089 (1) ◽  
pp. 012058
Author(s):  
P. Giriprasad Gaddam ◽  
A Sanjeeva reddy ◽  
R.V. Sreehari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Automatic Classification ◽  
Ecg Signals ◽  
Major Interest

Abstract In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Four different categories of ECG waveform were selected from four PhysioNet MIT-BIH databases, namely arrhythmia database, Normal Sinus Rhythm database, Malignant Ventricular Ectopy database and BIDMC Congestive heart failure database to examine the proposed technique. The major interest of the present study is to develop a transferred deep learning algorithm for automatic categorization of the mentioned four different heart diseases. Final results proved that the 2-D scalogram images trained with a deep convolutional neural network CNN with transfer learning technique (AlexNet) pepped up with a prominent accuracy of 95.67%. Hence, it is worthwhile to say the above stated algorithm demonstrates as an effective automated heart disease detection tool

VT/VF Detection Method Based on ECG Signal Quality Assessment

2018 ◽  
Vol 27 (11) ◽  
pp. 1850169 ◽  
Author(s):  
Borisav Jovanović ◽  
Srdan Milenković ◽  
Milan Pavlović
Keyword(s):  
Cardiac Arrhythmias ◽  
Signal Analysis ◽  
Detection Method ◽  
Signal Quality ◽  
Ecg Signals ◽  
Battery Charge ◽  
Time Domain Signal ◽  
Signal Parameters ◽  
Life Threatening ◽  
Electrocardiogram Ecg

Artefacts which are present in electrocardiogram (ECG) recordings distort detection of life-threatening arrhythmias such as ventricular tachycardia and ventricular fibrillation. The method examines single ECG lead and exploits time domain signal parameters for real-time detection of severe cardiac arrhythmias. The method is dedicated to implementation in mobile ECG telemetry systems, which are designed by using low-power microcontrollers, operating more than a week on a single battery charge. The method has been validated on publicly available databases and the results are presented. We verified our method on ECG signals obtained without pre-selection meaning that the noisy intervals were not omitted from signal analysis.

ECG Classification Framework for Cardiac Disease Prediction Using Nonlinear Vector Decomposed Neural Network

2020 ◽  
Vol 17 (12) ◽  
pp. 5563-5569
Author(s):  
M. Mohamed Suhail ◽  
T. Abdul Razak
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Cardiovascular Disease ◽  
Research Work ◽  
Automated Recognition ◽  
High Productivity ◽  
Ecg Signals ◽  
Cad System ◽  
Classification Framework ◽  
Nonlinear Vector

Early detection of heart disease may prevent myocardial infarction. Electrocardiogram (ECG) is the most widely used signal in clinical practice for the diagnosis of cardiovascular diseases such as arrhythmias and myocardial infarction. Human interpretation is time-consuming, and long-term ECG records are difficult to detect in small differences.Therefore, automated recognition of myocardial infarction using a Computer-Aided Diagnosis (CAD) system is the research interest, which can be used effectively to reduce mortality among cardiovascular disease patients. The most important step in the analysis of complex R-peak/QRS signals using an automated process of ECG signal. To automate the cardiovascular disease detection process, an adequate mechanism is required to characterize ECG signals, which are unknown features according to the similarities between ECG signals. If the classification can find similarities accurately and the probability of arrhythmia detection increases, the algorithm can become an effective method in the laboratory. In this research work, a new classification strategy is proposed to all the more precisely order ECG signals dependent on a powerful model of ECG signals. In this proposed method, a Nonlinear Vector Decomposed Neural Network (NVDN) is developed, and its simulation results show that this classifier can isolate the ECGs with high productivity. This proposed technique expands the exactness of the ECG classification concerning increasingly exact arrhythmia discovery.

Sign in / Sign up

Export Citation Format

Share Document