Key result
A 5-layered hybrid neural network combining CNN, RNN, and ANN architectures achieved an accuracy of about 98% in detecting heart arrhythmias from ECG recordings in the MIT-BIH corpus.
Why the study?
The study aimed to increase the accuracy and precision of heart arrhythmia classification using neural networks while conserving generalized pre-processing methods.
A hybrid neural network approach combining CNN, RNN, and ANN architectures can achieve high accuracy in detecting heart arrhythmias from single-lead ECG recordings.
Hypothesis-generating for AI arrhythmia detection on ECG; leaves open prospective clinical validation before any practice change.
This research presents a new approach to Heart Arrhythmia classification with the help of neural networks. A victim diagnosed with heart arrhythmia has an irregular beat pattern. Heart arrhythmia is harmless in most situations however, beyond a certain limit of irregularity, it causes severe and critical complications. Heart arrhythmia can be detected at the early stages. Doctors can determine symptoms of the patient and prevent further damage. Tests have been done to check the validity of neural networks for this job, and the attempted tool used in this paper is the same. The purpose of this research is to increase the accuracy, precision classifying ability while conserving the generalized pre-processing method. This research attempts to find a correlation-based feature selection. Our proposed algorithm exceeds a range of acceptance, in detecting an array of heart arrhythmias from an electrocardiogram recording used with a single lead wearable monitor. The data set used is the MIT-BIH corpus which contains ECG recordings of unique patients. On this data set, the 5-layered neural network which was trained maps a sequence of ECG samples to a sequence of rhythm classes. In this neural network, the layers were stacked such that the abilities of a convolution neural network, the recurrent neural network, and the artificial neural network work in tandem to concatenate the weights. Through the stacking of the neural networks, we were able to achieve an accuracy of about ninety-eight percent which was the best-case scenario.
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Kommireddy et al. (2020) studied Heart Arrhythmia. Hybrid neural network (CNN, RNN, ANN) was evaluated on Classification accuracy. A 5-layered hybrid neural network combining CNN, RNN, and ANN architectures achieved an accuracy of about 98% in detecting heart arrhythmias from ECG recordings in the MIT-BIH corpus.
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