Key result
A proposed deep learning model using 1D Convolution-LSTM neural networks and Discrete Wavelet Transform classified four types of ECG arrhythmias with an average accuracy of 97%.
Why the study?
Automatic classification of arrhythmia signals can provide faster and more accurate results, prompting the application of machine learning approaches to enhance accuracy, speed, and robustness.
A deep Convolutional-LSTM neural network combined with Discrete Wavelet Transform achieved 97% accuracy in classifying arrhythmias from ECG data without manual feature extraction.
An electrocardiogram (ECG) can be dependablyused as a measuring device to monitor cardiovascular function. The abnormal heartbeat appears in the ECG pattern and these abnormal signals are called arrhythmias. Classification and automatic arrhythmia signals can provide a faster and more accurate result. Several machine learning approaches have been applied to enhance the accuracy of results and increase the speed and robustness of models. This paper proposes a method based on Time-series Classification using deep Convolutional -LSTM neural networks and Discrete Wavelet Transform to classify 4 different types of Arrhythmia in the MIT-BIH Database. According to the results, the suggested method gives predictions with an average accuracy of 97% without needing to do feature extraction or data augmentation.
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A 2021 study studied Arrhythmia (n=47). 1D Convolution-LSTM Neural Networks and Discrete Wavelet Transform vs. Other machine learning models was evaluated on Average classification accuracy. A proposed deep learning model using 1D Convolution-LSTM neural networks and Discrete Wavelet Transform classified four types of ECG arrhythmias with an average accuracy of 97%.
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