Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection

Zhang, Yuwei; Lo, Benny; Xu, Wenyao

doi:10.1111/exsy.12432

Cited by 17 publications

(8 citation statements)

References 31 publications

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“…The problem in this particular case using the accuracy as prediction metric is that normal class has much greater number of samples than arrhythmic samples. Then different types of arrhythmias ventricular, supraventricular, atrial pathologies and their subtypes have 4,8,20,24,26,27,37,53,55,58,68,71,77,80,84,85,86,90,91,94,104,109,118,125,142,143,155,158,166,168,174,176,183,187,190,201,215,224,236,247 different frequency of occurrence some of them rare than others. Accuracy in this case does not put higher importance to the prediction quality of minority classes, which in our case or in the case of disease analysis in general opposes the design objective.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, researchers have presented different feature reduction methods to reduce input dimensions of ECG signals for neural classifiers. To name a few, Zhang et al [20] extracted statistical features applying the combined method of frequency analysis and Shannon entropy and used information gain criterion to select 10 highly effective features to obtain a good classification on five types of heartbeats. Yildrim et al [21] implemented a convolutional auto-encoder based nonlinear compression structure to reduce the feature size of arrhythmic beats.…”

Section: Introductionmentioning

confidence: 99%

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An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

et al. 2022

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Potentially lethal heart abnormalities can be detected/spotted with recent evolution in continuous, long-term cardiac health monitoring using wearable sensors. However, the huge data accumulated presents a challenge in terms of storage, knowledge extraction and computing time. Moreover, manual examination of long-term ECG recordings presents various problems like huge time and work demand, inter-observer variations and difficulty classifying complex non-linear single-lead ECG signal. To address these problems, we propose an automatic heartbeat classification system that uses the optimized minimum number of features using ECG time-series amplitude directly as input, without feature extraction and provides a primary classification and diagnosis for 1 normal and 14 types of arrhythmic heartbeats. Multi-objective particle swarm optimization (MOPSO) is used to achieve the best feature fitness. A novel fitness function is designed to be the sum of macro F1 loss and normalized dimension, with the optimization objective calculated as the minimum of the fitness function. Multi-layer perceptron (MLP), k-nearest neighbor, support vector machine, random forest and extra decision tree classifiers are trained using the selected features. For the targeted 15-class classification problem, MOPSO-optimized features with MLP consistently performed best with significantly reduced number of features. The proposed method proves to be an efficient and effective arrhythmia identification system for continuous, long-term cardiac health monitoring using single-lead ECG signal.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

et al. 2022

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“…Filter methods evaluate each feature independently of the classifier, rank the features according to some evaluation criterion and select the best ones [11]. This evaluation can be performed by using entropy for instance [12]. Wrappers methods evaluate the classifier's performance on various subsets of features and select the subset with maximum performance.…”

Section: Motivation Towards Ensemble Learningmentioning

confidence: 99%

An Outlier Detection and Feature Ranking based Ensemble Learning for ECG Analysis

Ardeti¹,

Kolluru²,

Varghese³

et al. 2022

IJACSA

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Automated classification of each heartbeat class from the ECG signal is important to diagnose cardiovascular diseases (CVDs) more quickly. ECG data acquired from the realtime or clinical databases contains exceptional values or extreme values called outliers. The separation and removal of outliers is very much useful for improving the data quality. The presence of outliers will influence the results of machine learning (ML) methods such as classification and regression. Outlier identification and removal plays a significant role in this area of research and is a part of signal denoising. Also, most of the traditional ECG-signal processing methods are facing the difficulty in finding the essential key features of recorded signal. In this work, an extreme outlier detection technique known as improved inter quartile range (IIQR) filtering method is used to find the outliers of the signal for the feature ranking process. In addition, an optimized random forest (ORF) based heterogenous ensemble classification model is proposed to improve the true positive and runtime on the ECG data. The classification of each heartbeat type is classified with majority voting technique. Ensemble learning and majority voting rule is used to enhance the accuracy of heart disease prediction. The proposed feature ranking based ORF ensemble classification model (LR + SVM + ORF + XGBoost + KNN) is evaluated on the MITBIH arrhythmia database and produces an overall accuracy of 99.45% which significantly outperforms the state-of-the-art methods such as, (LR + SVM + RF + XGBoost + KNN) with 96.17% accuracy, ensemble deep learning accuracy of 95.81% and ensemble SVM accuracy of 94.47%.

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“…To evaluate classification performance, tenfold cross-validation was used to verify the effectiveness of our method. Experimental results showed that the Random Forest classifier demonstrates significant performance with the SPE of 99.5%, the highest SEN of 98.1%, and the ACC of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification [67]. Kora et al (2019) showed that an algorithm to detect atrial fibrillation (AF) in the ECG signal is developed.…”

Section: Dwtmentioning

confidence: 99%

A Review Study for Electrocardiogram Signal Classification

Abdulla

Al-Ani

2020

UHD J SCI TECH

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An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.

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Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection

Cited by 17 publications

References 31 publications

An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

An Outlier Detection and Feature Ranking based Ensemble Learning for ECG Analysis

A Review Study for Electrocardiogram Signal Classification

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