An ECG-based feature selection and heartbeat classification model using a hybrid heuristic algorithm

Cardiac arrhythmia is an illness in which a heartbeat is erratic, either too slow or too rapid. It happens as a result of faulty electrical impulses that coordinate the heartbeats. Sudden cardiac death can occur as a result of certain serious arrhythmia disorders. As a result, the primary goal of electrocardiogram (ECG) investigation is to reliably perceive arrhythmias as life-threatening to provide a suitable therapy and save lives. ECG signals are waveforms that denote the electrical movement of the human heart (P, QRS, and T). The duration, structure, and distances between various peaks of each waveform are utilized to identify heart problems. The signals’ autoregressive (AR) analysis is then used to obtain a specific selection of signal features, the parameters of the AR signal model. Groups of retrieved AR characteristics for three various ECG kinds are cleanly separated in the training dataset, providing high connection classification and heart problem diagnosis to each ECG signal within the training dataset. A new technique based on two-event-related moving averages (TERMAs) and fractional Fourier transform (FFT) algorithms is suggested to better evaluate ECG signals. This study could help researchers examine the current state-of-the-art approaches employed in the detection of arrhythmia situations. The characteristic of our suggested machine learning approach is cross-database training and testing with improved characteristics.

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“…e study in [13] presents a hybrid approach for optimally classifying cardiac arrhythmias and selecting their properties.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems

Taloba

Rayan

Shahin

et al. 2021

Computational Intelligence and Neuroscience

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“…Feature extraction is the key step of ECG signal classification and recognition, and the extracted feature quality will affect the accuracy of ECG signal classification and recognition [ 6 ]. Generally, the features of ECG signals extracted by researchers mainly include morphological features [ 7 ], interphase features [ 8 , 9 ], wavelet transform features [ 10 ], higher-order statistics (HOS) [ 9 , 11 ], Hermite basis function (HBF) [ 12 ], QRS amplitude vector [ 13 ], and QRS composite wave area [ 14 ]. Then machine-learning algorithms are used for classification, such as the KNN algorithm [ 15 ], support vector machine (SVM) [ 7 ], and random forest [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

“…In this way, the neural network can be encouraged to generate simple decision trees and restricted to generate complex decision trees, which further makes the generated decision trees easier to be simulated by human beings. e decision tree generation formula can be expressed by ( 13) and (14), where x n is the sample feature of the training set, 􏽢 y n (x n , W) is the prediction label of the depth model, W is the weight matrix of the depth model, and 􏽣 y tn is the prediction label of the decision tree. e reason why 􏽢 y n is used as the input of the decision tree is to make 􏽣 y tn and 􏽢 y n as similar as possible so as to realize the purpose of using the decision tree to simulate the deep network.…”

Section: Bilstm Neural Networkmentioning

confidence: 99%

Combining Rhythm Information between Heartbeats and BiLSTM-Treg Algorithm for Intelligent Beat Classification of Arrhythmia

Yao

Shen

et al. 2021

Journal of Healthcare Engineering

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Arrhythmia is a cardiovascular disease that seriously affects human health. The identification and diagnosis of arrhythmia is an effective means of preventing most heart diseases. In this paper, a BiLSTM-Treg algorithm that integrates rhythm information is proposed to realize the automatic classification of arrhythmia. Firstly, the discrete wavelet transform is used to denoise the ECG signal, based on which we performed heartbeat segmentation and preserved the timing relationship between heartbeats. Then, different heartbeat segment lengths and the BiLSTM network model are used to conduct multiple experiments to select the optimal heartbeat segment length. Finally, the tree regularization method is used to optimize the BiLSTM network model to improve classification accuracy. And the interpretability of the neural network model is analyzed by analyzing the simulated decision tree generated in the tree regularization method. This method divides the heartbeat into five categories (nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), fused heartbeats (F), and unknown heartbeats (Q)) and is validated on the MIT-BIH arrhythmia database. The results show that the overall classification accuracy of the algorithm is 99.32%. Compared with other methods of classifying heartbeat, the BiLSTM-Treg network model algorithm proposed in this paper not only improves the classification accuracy and obtains higher sensitivity and positive predictive value but also has higher interpretability.

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“…in this research author has utilized the GA and DT strategies as another blend (Hybrid) model to take care of the arrhythmia characterization issue and to evaluate the presentation of the proposed model, UCi arrhythmia dataset was utilized to figure exact outcomes, affectability, particularity, and normal sen-spec measurements. [ Mehdi Ayar, 2018].…”

Section: Related Work: the Function Of Electrocardiogram (Ecg)mentioning

confidence: 99%

ECG-Based Heartbeat Classification using Machine Learning: Survey

Jaisinghani¹

2020

Biosci. Biotech. Res. Comm

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Cardiovascular diseases (CVDs) affecting millions of people around the world. Classification of heartbeat is very important step to determine cardiac functionality. An electrocardiogram (ECG), (a graphical representation of heart signals) is used to measure the electric signals of the heart and is widely used for detecting any abnormality lies within. By analyzing and studying the electrical signals generated from ECG with the help of electrodes, it is possible to detect some of the problems in heart. There are many types of classifiers available for Heartbeat classification. However in this paper we survey the methods used for automatic ECG-based heartbeat classification by discussing pre processing, Electrocardiogram dataset, feature extraction and types of classifiers available for automatic heartbeat classification.

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An ECG-based feature selection and heartbeat classification model using a hybrid heuristic algorithm

Cited by 33 publications

References 22 publications

Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems

Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems

Combining Rhythm Information between Heartbeats and BiLSTM-Treg Algorithm for Intelligent Beat Classification of Arrhythmia

ECG-Based Heartbeat Classification using Machine Learning: Survey

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