Key result
A novel deep learning architecture combining a convolutional neural network and bidirectional long short-term memory with weight-based loss achieved an average accuracy of 99.33% and F1 score of 91.65% for classifying five types of ECG heartbeats.
Why the study?
Manual ECG interpretation is workload-demanding and subjective, while existing deep learning models struggle with heartbeat class imbalance and computationally heavy preprocessing unsuitable for portable sensors.
Absolute Event Rate: 99.33% vs 98.26%
A novel lightweight deep learning architecture using CNN and Bi-LSTM with weight-based loss achieved high accuracy in classifying ECG heartbeats, showing potential for use in portable ECG monitors.
May support portable ECG monitors; leaves open prospective clinical validation.
Background: Analysis of electrocardiogram (ECG) provides a straightforward and non-invasive approach for cardiologists to diagnose and classify the nature and severity of variant cardiac diseases including cardiac arrhythmia. However, the interpretation and analysis of ECG are highly working-load demanding, and the subjective may lead to false diagnoses and heartbeats classification. In recent years, many deep learning works showed an excellent role in accurate heartbeats classification. However, the imbalance of heartbeat classes is universal in most of the available ECG databases since abnormal heartbeats are always relatively rare in real life scenarios. In addition, many existing approaches achieved prominent results by removing noise and extracting features in data preprocessing, which relies heavily on powerful computers. It is a pressing need to develop efficient and automatic light weighted algorithms for accurate heartbeats classification that can be used in portable ECG sensors. Objective: This study aims at developing a robust and efficient deep learning method, which can be embedded into wearable or portable ECG monitors for classifying heartbeats. Methods: We proposed a novel and light weighted deep learning architecture with weight-based loss based on a convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) that can automatically identify five types of ECG heartbeats according to the AAMI EC57 standard. It was also true that the raw ECG signals were simply segmented without noise removal and other feature extraction processing. Moreover, to tackle the challenge of classification bias due to imbalanced ECG datasets for different types of arrhythmias, we introduced a weight-based loss function to reduce the influence of over-weighted categories in the ECG dataset. For avoiding the influence of the division of validation dataset, k-fold method was adopted to improve the reliability of the model. Results: The proposed algorithm is trained and tested on MIT-BIH Arrhythmia Database, and achieves an average of 99.33% accuracy, 93.67% sensitivity, 99.18% specificity, 89.85% positive prediction, and 91.65% F1 score.
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Yang et al. (2022) studied Cardiac arrhythmia (n=47). CNN and Bidirectional LSTM with weight-based loss vs. Standard models without weight-based loss was evaluated on Average classification accuracy for 5 types of ECG heartbeats. A novel deep learning architecture combining a convolutional neural network and bidirectional long short-term memory with weight-based loss achieved an average accuracy of 99.33% and F1 score of 91.65% for classifying five types of ECG heartbeats.
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