LSTM-based ECG Signal Classification with Multi-level One-hot Encoding for Wearable Applications

Jinhai Hu, Wang Ling Goh, Yuan Gao

IEEE Biomedical Circuits and Systems Conference (BioCAS) , 2024

This paper presents an electrocardiogram (ECG) signal classification method using one-hot coding scheme and Long Short-Term Memory (LSTM) neural network. Instead of the conventional analog to digital converter (ADC) with two’s complement binary output, one-hot encoding scheme is adopted in this design to convert the analog signal to 1D vector and then further processed by a LSTM network for classification. Our study shows that one-hot encoding can effectively represent the features of ECG signal with low data bit-width and sampling rate. The proposed method not only simplifies the ADC design, but also improves the classification accuracy. Simulation results show that the proposed design only requires 5-bit bit-width with 50 Hz sampling rate to achieve 96.9% validation accuracy on 5-class classification task using MIT-BIH ECG dataset.

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