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A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution-Pooling

Rui Zhang¹, Ranran Zhou¹, Zuting Zhong¹

¹School of Integrated Circuits, Shandong University, Jinan 250101, China.

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

This study introduces a novel binarized depthwise separable convolutional neural network (bDSCNN) for accurate, low-power cardiac arrhythmia classification from ECG signals. The system achieves high accuracy while significantly reducing hardware resource usage.

Keywords:

ECG FPGA binarized depthwise separable convolutional neural network (bDSCNN)blockwise incremental calculation merged convolution–pooling method multi-classifier

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Area of Science:

Biomedical Engineering
Artificial Intelligence
Cardiology

Background:

Binarized convolutional neural networks (bCNNs) offer efficient solutions for cardiac arrhythmia classification due to high compression rates.
However, bCNNs face accuracy challenges in multi-class ECG signal classification stemming from the binarization process.

Purpose of the Study:

To develop an effective multi-classifier system for ECG signals using a binarized depthwise separable convolutional neural network (bDSCNN).
To address accuracy loss in multi-class ECG classification by integrating R peak interval data with P-QRS-T features.

Main Methods:

Employed a binarized depthwise separable convolutional neural network (bDSCNN) with a merged convolution-pooling (MCP) method.
Utilized blockwise incremental calculation to minimize hardware resource consumption.
Integrated R peak interval data with P-QRS-T features to enhance classification performance.

Main Results:

Achieved a five-class classification accuracy of 96.61% and a macro-F1 score of 89.08% for ECG signals.
Demonstrated a low dynamic power dissipation of 20 μW for five-category ECG signal classification.
Reduced hardware resource usage (BRAM, LUTs+REGs) by at least 2.94 and 1.74 times, respectively, compared to existing bCNN ECG classifiers.

Conclusions:

The proposed bDSCNN with MCP method effectively improves multi-class ECG signal classification accuracy while reducing computational complexity and hardware resources.
This approach presents a promising low-power, low-storage solution for real-time cardiac arrhythmia detection on embedded systems.