STAL: Spike Threshold Adaptive Learning Encoder for Classification of Pain-Related Biosignal Data
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2024-07-06
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en
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Abstract
This paper presents the first application of spiking neutral 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.
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Faculteit der Sociale Wetenschappen