Key result
The proposed deep learning framework using Vanilla LSTM and generalized gated pooling achieved an average accuracy of 99.14% and a 95% recall for diagnosing heart disease from ECG signals.
Why the study?
Existing ECG-based heart disease diagnostic systems employ various state-of-the-art methods, but improving these methods for remote disease diagnostics remains an open area of research.
A deep learning framework combining linear and time-series ECG features achieved 99.14% accuracy in diagnosing heart disease, demonstrating potential for remote cardiac monitoring.
Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics, which aid in the prevention of several diseases including heart-related abnormalities. In this context, regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram (ECG) signals has the potential to save many lives. In existing studies, several heart disease diagnostic systems are proposed by employing different state-of-the-art methods, however, improving such methods is always an intriguing area of research. Hence, in this research, a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals. The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently. The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units (GeLu) and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks (LSTM). Following on, the feature reduction of linear information is done with the help of ID Generalized Gated Pooling (GGP). In addition, data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique (SMOTE). The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database (MITDB) and PTB Diagnostic ECG database (PTBDB). The proposed framework achieves an average accuracy performance of 99.14% along with a 95% recall value.
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Bukhari et al. (2023) studied Heart disease (Arrhythmia) (n=337). Deep Vanilla LSTM with Generalized Gated Pooling and GeLu vs. Existing state-of-the-art methods was evaluated on Average accuracy performance. The proposed deep learning framework using Vanilla LSTM and generalized gated pooling achieved an average accuracy of 99.14% and a 95% recall for diagnosing heart disease from ECG signals.
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