Lossless Sparse Temporal Coding for SNN-based Classification of Time-Continuous Signals

Loh, Johnson; Gemmeke, Tobias

doi:10.23919/date56975.2023.10137112

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2024

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Cited by 3 publications

References 22 publications

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Review on spiking neural network-based ECG classification methods for low-power environments

Choi,

Park,

Lee

et al. 2024

Biomed. Eng. Lett.

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Review on spiking neural network-based ECG classification methods for low-power environments

Choi,

Park,

Lee

et al. 2024

Biomed. Eng. Lett.

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SNNAuth: Sensor-Based Continuous Authentication on Smartphones Using Spiking Neural Networks

Li,

Sun,

Yang

et al. 2024

IEEE Internet Things J.

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On-FPGA Spiking Neural Networks for Integrated Near-Sensor ECG Analysis

Scrugli,

Busia,

Leone

et al. 2024

2024 Design, Automation &Amp;amp; Test in Europe Conference &Amp;amp; Exhibition (DATE)

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The identification of cardiac arrhythmias is a significant issue in modern healthcare and a major application for Artificial Intelligence (AI) systems based on artificial neural networks. This research introduces a real-time arrhythmia diagnosis system that uses a Spiking Neural Network (SNN) to classify heartbeats into five types of arrhythmias from a singlelead electrocardiogram (ECG) signal. The system is implemented on a custom SNN processor running on a low-power Lattice iCE40-UltraPlus FPGA. It was tested using the MIT-BIH dataset, and achieved accuracy results that are comparable to the most advanced SNN models, reaching 98.4% accuracy. The proposed modules take advantage of the energy efficiency of SNNs to reduce the average execution time to 4.32 ms and energy consumption to 50.98 uJ per classification.

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Lossless Sparse Temporal Coding for SNN-based Classification of Time-Continuous Signals

Cited by 3 publications

References 22 publications

Review on spiking neural network-based ECG classification methods for low-power environments

Review on spiking neural network-based ECG classification methods for low-power environments

SNNAuth: Sensor-Based Continuous Authentication on Smartphones Using Spiking Neural Networks

On-FPGA Spiking Neural Networks for Integrated Near-Sensor ECG Analysis

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