  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.
