Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning

Abstract

The success of arrhythmia classification tasks with Machine Learning (ML) algorithms is based on the handcrafting extraction of features from Electrocardiography (ECG) signals. However, feature extraction is a time-consuming trial-and-error approach. Deep Neural Network (DNN) algorithms bypass the process of handcrafting feature extraction since the algorithm extracts the features automatically in their hidden layers. However, it is important to have access to a balanced dataset for algorithm training. In this exploratory research study, we will compare the evaluation metrics among Convolutional Neural Networks (1D-CNN) and Support Vector Machines (SVM) using a dataset based on the merged public ECG signals database TNMG and CINC17 databases. Results: Both algorithms showed good performance using the new, merged ECG database. For evaluation metrics, the 1D-CNN algorithm has a precision of 93.04%, an accuracy of 93.07%, a recall of 93.20%, and an F1-score of 93.05%. The SVM classifier (λ = 10, C = 10e9) achieved the best classification metrics with two combined, handcrafted feature extraction methods: Wavelet transforms and R-peak Interval features, which achieved an overall precision of 89.04%, accuracy of 92.00%, recall of 94.20%, and F1-score of 91.54%. As an unique input feature and SVM (λ=10,C=100), wavelet transforms achieved precision, accuracy, recall, and F1-score metrics of 86.15%, 85.33%, 81.16%, and 83.58%. Conclusion: Researchers face a challenge in finding a broad dataset to evaluate ML models. One way to solve this problem, especially for deep learning models, is to combine several public datasets to increase the amount of data. The SVM and 1D-CNN algorithms showed positive results with the merge of databases, showing similar F1-score, precision, and recall during arrhythmia classification. Despite the favorable results for both of them, it should be considered that in the SVM, feature selection is a time-consuming trial-and-error process; meanwhile, CNN algorithms can reduce the workload significantly. The disadvantage of CNN algorithms is that it has a higher computational processing cost; moreover, in the absence of access to powerful computational processing, the SVM can be a reliable solution.

Analysis of Electrocardiogram Dynamic Features for Arrhythmia Classification

“…The test metric that will be used in this study is the multi-class confusion matrix test metric. Selection of test metrics because the number of classes is more than two, this test metric shows the proportion of the actual predicted results for each heart rhythm [22].…”

Detection of heart pathology using deep learning methods

“…The resource was created in 1999 by a group of scientists, physicians, and educators at the Beth Israel Medical Center, the Massachusetts Institute of Technology, Harvard Medical School, Boston University, and McGill University. During the experiment, to determine the effectiveness of deep learning methods such as convolutional neural network [14]- [16], recurrent neural network [17]- [19], long short-term memory [20]- [22], multilayer perceptron [23], [24], data from 50 patients were taken, classified according to three indicators, 13 abnormal, 24 abnormal and 1 healthy parameter.…”

Enhancing Electrocardiogram (ECG) Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification

“…Spectral features are derived from the frequency components of the ECG signal and are extracted, e.g., using Wavelet transform, Wavelet decomposition, and power spectral density analysis [18].…”