STAL: Spike Threshold Adaptive Learning Encoder for Classification of Pain-Related Biosignal Data

TL;DR

This paper introduces STAL, a novel spike threshold adaptive encoder, and an ensemble of spiking RNNs for classifying pain-related biosignals, achieving superior accuracy and robustness over traditional methods, with implications for energy-efficient wearable pain management.

Contribution

The paper presents the first application of SNNs for CLBP classification, introducing STAL encoder and an ensemble SRNN approach that improves performance on biosignal data.

Findings

Achieved 80.43% accuracy in pain classification.

Outperformed traditional encoding methods in MCC.

Demonstrated effectiveness of STAL in preserving temporal dynamics.

Abstract

This paper presents the first application of spiking neural networks (SNNs) for the classification of chronic lower back pain (CLBP) using the EmoPain dataset. Our work has two main contributions. We introduce Spike Threshold Adaptive Learning (STAL), a trainable encoder that effectively converts continuous biosignals into spike trains. Additionally, we propose an ensemble of Spiking Recurrent Neural Network (SRNN) classifiers for the multi-stream processing of sEMG and IMU data. To tackle the challenges of small sample size and class imbalance, we implement minority over-sampling with weighted sample replacement during batch creation. Our method achieves outstanding performance with an accuracy of 80.43%, AUC of 67.90%, F1 score of 52.60%, and Matthews Correlation Coefficient (MCC) of 0.437, surpassing traditional rate-based and latency-based encoding methods. The STAL encoder shows superior performance in preserving temporal dynamics and adapting to signal characteristics. Importantly, our approach (STAL-SRNN) outperforms the best deep learning method in terms of MCC, indicating better balanced class prediction. This research contributes to the development of neuromorphic computing for biosignal analysis. It holds promise for energy-efficient, wearable solutions in chronic pain management.