Key result
A detection model merging a 1-D CNN and bi-LSTM using 21 temporal ECG features achieved an overall accuracy of 99.25% for classifying healthy controls, MI, and non-MI subjects.
Why the study?
Deep learning approaches typically use whole ECG signals for binary detection of MI, but face redundancy and class imbalance when classifying cardiac abnormalities.
Does a temporal feature-based CNN and bi-LSTM model accurately classify ECG signals into MI, non-MI, and healthy controls?
Does a temporal feature-based CNN and bi-LSTM model accurately classify ECG signals into MI, non-MI, and healthy controls?
Answers are grounded in this paper and cite their sources.
A novel deep learning model using temporal ECG features demonstrated high accuracy in distinguishing myocardial infarction from other cardiovascular diseases and healthy controls.
Heart attack else wise termed as myocardial infarction (MI) causes irreparable death of cardiac muscles yielding the focal reason for most casualties among all cardiovascular diseases (CVDs’). A 12-lead electrocardiogram (ECG) generally depicts cardiac abnormalities and so customary deep learning (DL) approaches use the whole signal for binary detection purposes, that is separating healthy control (HC), and MI classes. This paper proposes an alternative approach where 21 temporal features in lieu of the temporal signal are collected from the 12 lead data to reduce redundancy and class imbalance keeping the vital information intact. Then these extracted features are fed into a detection model consisting of a one dimensional (1-D) convolutional neural network (CNN) and a bidirectional long short-term memory (bi-LSTM) layer which classifies into three classes, namely: HC, MI, and non-myocardial infarction (non-MI) subjects for a realistic and reliable assessment. The model’s performance is evaluated using 517 records acquired from the Physikalisch-Technische Bundesanstalt (PTB) database and a state-of-art overall accuracy of 99.246%, kappa of 0.983, and macro averaged F1 score of 98.86% were achieved using stratified 5-fold cross-validation. DL methods suffer to make unbiased decisions in the case of class imbalance due to an insufficient amount of data for a particular class and thus temporal features are employed to inherently reduce this problem. The successful performance of the extracted features depends on the precise detection of fiducial points, and so multiple novel algorithms have been introduced in this paper.
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Dey et al. (2021) studied Myocardial infarction and other cardiovascular diseases (n=517). 1-D CNN and bi-LSTM model using 21 temporal ECG features was evaluated on Overall accuracy for classification into healthy control, MI, and non-MI subjects. A detection model merging a 1-D CNN and bi-LSTM using 21 temporal ECG features achieved an overall accuracy of 99.25% for classifying healthy controls, MI, and non-MI subjects.
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