Advanced detection of cardiac arrhythmias using a three-stage CBD filter and a multi-scale approach in a combined deep learning model

In this paper, we present a new method based on deep learning for the detection of cardiac arrhythmias. The proposed method is based on three fundamental components: an inception-ResNet structure, bidirectional LSTM layers and a multi-scale signal analysis technique. To improve processing of the MIT-BIH arrhythmia dataset, we have developed a three-stage CBD filter that successively incorporates Chebyshev, Butterworth and Daubechies filters. This filter is designed meticulously to denoise the ECG signal while preserving its relevant features. By combining this filter with our deep learning model and multi-scale approach, we have significantly improved the quality of the ECG signal. Such integration not only enhances the accuracy and reliability of arrhythmia detection, but also contributes to the identification of complex patterns in various temporal and spatial dimensions. In terms of performance, our model is remarkably robust, with 98.94% accuracy and 97.20% precision.

Automated summary

In this paper, a new method based on deep learning is proposed for the detection of cardiac arrhythmias. The method combines a deep learning model, multi-scale approach, and a carefully designed CBD filter to improve the quality of the ECG signal, enhance the accuracy and reliability of arrhythmia detection, and identify complex patterns in various temporal and spatial dimensions.