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

• URL: http://arxiv.org/abs/2407.08362v1
• Date: Thu, 11 Jul 2024 10:15:52 GMT
• Title: STAL: Spike Threshold Adaptive Learning Encoder for Classification of Pain-Related Biosignal Data
• Authors: Freek Hens, Mohammad Mahdi Dehshibi, Leila Bagheriye, Mahyar Shahsavari, Ana Tajadura-Jiménez,
• Abstract summary: This paper presents the first application of spiking neural networks (SNNs) for the classification of chronic lower back pain (CLBP) using the EmoPain dataset. We introduce Spike Threshold Adaptive Learning (STAL), a trainable encoder that effectively converts continuous biosignals into spike trains. We also propose an ensemble of Spiking Recurrent Neural Network (SRNN) classifiers for the multi-stream processing of sEMG and IMU data.
• Score: 2.0738462952016232
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Electroencephalogram Emotion Recognition via AUC Maximization [0.0]
  Imbalanced datasets pose significant challenges in areas including neuroscience, cognitive science, and medical diagnostics. This study addresses the issue class imbalance, using the Liking' label in the DEAP dataset as an example.
  arXiv  Detail & Related papers  (2024-08-16T19:08:27Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus [46.1292414445895]
  Paranasal anomalies can present with a wide range of morphological features. Current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. We investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary (MS) and MS with polyps or cysts.
  arXiv  Detail & Related papers  (2023-03-31T09:23:27Z)
• HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN [3.8791511769387625]
  We have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.
  arXiv  Detail & Related papers  (2023-03-06T13:26:29Z)
• Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA with Less Data using Swin Transformer [3.6695403836792493]
  We developed an efficient workflow for biomarkers in colorectal cancers using Shifted Windows (Swin-T) Swin-T was extremely efficient using small training datasets and exhibits robust predictive performance with only 200-500 training samples. These data indicate that Swin-T may be 5-10 times more efficient than the current state-of-the-art algorithms for MSI.
  arXiv  Detail & Related papers  (2022-08-22T02:32:30Z)
• Parameter estimation for WMTI-Watson model of white matter using encoder-decoder recurrent neural network [0.0]
  In this study, we evaluate the performance of NLLS, the RNN-based method and a multilayer perceptron (MLP) on datasets rat and human brain. We showed that the proposed RNN-based fitting approach had the advantage of highly reduced computation time over NLLS.
  arXiv  Detail & Related papers  (2022-03-01T16:33:15Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Multi-Sample Online Learning for Spiking Neural Networks based on Generalized Expectation Maximization [42.125394498649015]
  Spiking Neural Networks (SNNs) capture some of the efficiency of biological brains by processing through binary neural dynamic activations. This paper proposes to leverage multiple compartments that sample independent spiking signals while sharing synaptic weights. The key idea is to use these signals to obtain more accurate statistical estimates of the log-likelihood training criterion, as well as of its gradient.
  arXiv  Detail & Related papers  (2021-02-05T16:39:42Z)
• Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks [77.34726150561087]
  We introduce Gradient Markov Descent (SMGD), a discrete optimization method applicable to training quantized neural networks. We provide theoretical guarantees of algorithm performance as well as encouraging numerical results.
  arXiv  Detail & Related papers  (2020-08-25T15:48:15Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.