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A Neuromorphic Processing System With Spike-Driven SNN Processor for Wearable ECG Classification.

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    Summary
    This summary is machine-generated.

    This study introduces a low-power neuromorphic system for wearable electrocardiogram (ECG) classification using a spike-driven spiking neural network (SNN). The system achieves high accuracy with efficient energy consumption for continuous health monitoring.

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    Area of Science:

    • Neuromorphic Engineering
    • Biomedical Signal Processing
    • Artificial Intelligence

    Background:

    • Wearable electrocardiogram (ECG) monitoring requires efficient, always-on processing for continuous health analysis.
    • Existing systems often face challenges with power consumption and data representation for real-time classification.

    Purpose of the Study:

    • To develop a novel neuromorphic processing system for always-on wearable ECG classification.
    • To design a spike-driven spiking neural network (SNN) processor optimized for low-power operation and high accuracy.

    Main Methods:

    • Utilized level crossing (LC) sampling for native temporal coding and single-bit ECG data representation.
    • Implemented a hardware-aware spatio-temporal backpropagation (STBP) training scheme for lightweight SNN models.
    • Designed a specialized SNN processor with spike-driven flow and hierarchical memory access.

    Main Results:

    • Achieved 98.22% classification accuracy on the MIT-BIH database for 5-category ECG classification.
    • Demonstrated high energy efficiency with 0.75 μJ/classification.
    • Validated through Field Programmable Gate Arrays (FPGA) and simulated in 40 nm CMOS technology.

    Conclusions:

    • The proposed neuromorphic system offers a highly accurate and energy-efficient solution for always-on wearable ECG classification.
    • The developed SNN processor and training scheme are suitable for application-specific integrated circuit (ASIC) design.
    • This technology enables advanced, low-power continuous cardiac monitoring.