Key result
An automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models achieved 100% sensitivity and 96.46% positive predictive value for category S.
Why the study?
Existing cardiac arrhythmia classification methods lack acceptable performance for detecting different heart conditions, particularly when handling imbalanced datasets.
Does a deep learning sequence-to-sequence model improve ECG heartbeat classification for arrhythmia detection compared to existing methods?
Does a deep learning sequence-to-sequence model improve ECG heartbeat classification for arrhythmia detection compared to existing methods?
Answers are grounded in this paper and cite their sources.
A novel deep learning sequence-to-sequence model demonstrates high sensitivity and positive predictive value for automated ECG heartbeat classification, outperforming existing methods on the MIT-BIH database.
Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. In this paper, we propose a solution to address this limitation of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models. We evaluated the proposed method on the MIT-BIH arrhythmia database, considering the intra-patient and inter-patient paradigms, and the AAMI EC57 standard. The evaluation results for both paradigms show that our method achieves the best performance in the literature (a positive predictive value of 96.46% and sensitivity of 100% for the category S, and a positive predictive value of 98.68% and sensitivity of 97.40% for the category F for the intra-patient scheme; a positive predictive value of 92.57% and sensitivity of 88.94% for the category S, and a positive predictive value of 99.50% and sensitivity of 99.94% for the category V for the inter-patient scheme.).
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Mousavi et al. (2019) studied Cardiac arrhythmia. Deep convolutional neural networks and sequence to sequence models vs. Current classification approaches was evaluated on Heartbeat classification performance (positive predictive value and sensitivity). An automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models achieved 100% sensitivity and 96.46% positive predictive value for category S.
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