Key result
Bi-LSTM, a deep learning method, outperformed classical machine learning methods in the automated remote classification of cardiac arrhythmia using instantaneous heart rates.
Why the study?
Cardiac arrhythmia causes serious health risks, yet many individuals remain undiagnosed or undertreated, presenting an opportunity to leverage wireless health trackers and AI to identify the best technique for automated arrhythmia diagnosis.
Does the Bi-LSTM deep learning method outperform classical machine learning methods for the automated remote classification of cardiac arrhythmia using instantaneous heart rates?
Does the Bi-LSTM deep learning method outperform classical machine learning methods for the automated remote classification of cardiac arrhythmia using instantaneous heart rates?
Deep learning methods such as Bi-LSTM demonstrate superior performance compared to classical machine learning techniques for the automated remote classification of cardiac arrhythmias using instantaneous heart rate data.
May support Bi-LSTM for automated arrhythmia screening; leaves open real-world validation before clinical use.
Cardiac arrhythmia can cause serious health risks including sudden cardiac events, and a significant number of vulnerable individuals are undiagnosed or undertreated. Noting the widespread use of wireless health trackers and the efficacy of Artificial intelligence (AI) methods in processing a large amount of time-series sequential data, in this study we aim to find the best AI technique for diagnosing an arrhythmia. Various AI models have been trained and tested by utilizing publicly available medical data. From the confusion matrix, the accuracy, recall, F1-score, Area Under the Curve (AUC), and precision of each AI model have been evaluated and compared. This analysis and Friedman Tests indicate that Bi-LSTM – the deep learning method outperformed the classical machine learning methods. The process of remotely classifying arrhythmia provided in this study can be generalized for automatic diagnosis of many health risks.
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Panindre et al. (2021) studied Cardiac arrhythmia. Bi-LSTM (deep learning method) vs. Classical machine learning methods was evaluated on Accuracy, recall, F1-score, AUC, and precision. Bi-LSTM, a deep learning method, outperformed classical machine learning methods in the automated remote classification of cardiac arrhythmia using instantaneous heart rates.
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