Key result
An LSTM network with oversampling technique achieved an ECG classification accuracy of 99.54%, which is considerably better than the 98% accuracy yielded by traditional approaches.
Why the study?
Automated ECG classification for heart disease diagnosis is challenged by nonstationarity, high variability, and imbalanced, vague datasets.
Absolute Event Rate: 99.54% vs 98%
A novel bidirectional LSTM network with oversampling techniques achieves 99.54% accuracy in ECG arrhythmia classification, outperforming traditional methods.
Hypothesis-generating for LSTM ECG classification; requires prospective validation before clinical integration.
Segmentation of ECG to obtain significant and relevant features has been a significant and inevitable step in achieving dimensionality reduction in automated heart disease diagnosis systems. Reduction in mortality due to the cardiovascular issues rate can only be achieved through accurate and rapid classification. Nonstationarity and high variability augment the complexity of the detection process in the domains of time and frequency. These difficulties are further enhanced due to imbalanced and vague datasets. In this paper, we propose to use a deep learning module to tackle the imbalance in the datasets by applying recurrent neural networks using long short-term memory layers (LSTM) to classify ECG into two classes. It has been seen that LSTM networks can effectively extract sequential timing information in the input ECG samples. To remove the imbalance in the datasets, oversampling and focal lossbased weight balancing techniques have been used, which eventually enhance the accuracy of classification. The proposed approach, an LSTM network with oversampling technique, provides an accuracy of 99.54%, which is considerably better than the traditional approaches that yield an accuracy of around 98%. Moreover, this method is insensitive to the quality of the ECG signal due to the fuzzification process followed in the initial stages of processing the dataset. Deployment of the proposed method for bio-signal telemetry or pharmaceutical research to assist physicians in their work is the most promising advancement in this domain.
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Ratnaparkhi et al. (2021) studied Arrhythmia. LSTM network with oversampling technique vs. Traditional approaches was evaluated on Classification accuracy. An LSTM network with oversampling technique achieved an ECG classification accuracy of 99.54%, which is considerably better than the 98% accuracy yielded by traditional approaches.
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