Regression Model Employing Spiking Neural Network for Bio-Signal Analysis With Hardware Integration

Abstract

Spiking neural networks, known for mimicking the brain’s functionality resulting in efficient algorithms, are gaining attention across various problems and applications. However, their potential in regression tasks remains relatively unexplored. This study focuses on leveraging the spiking neural architecture in conjunction with Fourier analysis and support vector regression to estimate heart rates from electrocardiogram signal. We evaluated the regression errors of our model using three distinct elctrocardiogram datasets and assessed its performance on neuromorphic hardware by embedding spike-based layers. Our findings reveal that, compared to the conventional deep learning models, the proposed spiking neural system achieves a computational efficiency improvement while maintaining the competitive regression accuracy. Finally, we discuss the regression performance, energy efficiency, biological plausibility, and potential applications of the proposed neuromorphic system.

Choongseop Lee

Researcher, LG PRI

His research interests include machine learning and computational neuroscience.

Geunbo Yang

Researcher, KG Steel

His research interests include machine learning algorithms and spiking neural networks.

Yuntae Park

Researcher, Infinitree

His research interests include machine learning algorithms, deep learning, and reinforcement learning.

Cheolsoo Park

Professor

His research interests include machine learning, adaptive signal processing, computational neuroscience, and wearable technology.