Neuromuscular Disease Detection Employing Deep Feature Extraction from Cross Spectrum Images of Electromyography Signals

Samanta, Kaniska; Roy, Sayanjit Singha; Modak, Sudip; Chatterjee, Soumya; Bose, Rohit

doi:10.1109/embc44109.2020.9176464

Cited by 5 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such models can signifi-cantly benefit from the recent advance in computing power by deploying the model in parallel such as on the powerful graphics processing unit. Quivira et al [32] introduced CNNs to the diagnosis of neuromuscular disorders to extract wavelet features for detecting and classifying anomalous sEMG signals. Their experiment showed promising results that real-time detection of neuromuscular disorders is possible.…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

Movement Recognition via Channel-Activation-Wise sEMG Attention

Zhang

Matsuda

Fujimoto³

et al. 2023

Preprint

View full text Add to dashboard Cite

Context: Surface electromyography (sEMG) signals contain rich information recorded from muscle movements and therefore reflect the user's intention. sEMG has seen dominant applications in reha- bilitation, clinical diagnosis as well as human engineering, etc. However, current feature extraction methods for sEMG signals have been seriously limited by their stochasticity, transiency, and non-stationarity. Objective: Our objective is to combat the difficulties induced by the aforementioned downsides of sEMG and thereby extract representative features for various downstream movement recognition. Method: We propose a novel 3-axis view of sEMG features composed of temporal, spatial, and channel-wise summary. We leverage the state-of-the-art architecture Transformer to enforce efficient parallel search and to get rid of limitations imposed by previous work in gesture classification. The transformer model is designed on top of an attention-based module, which allows for the extraction of global contextual relevance among channels and the use of this relevance for sEMG recognition. Results: We compared the proposed method against existing methods on two Ninapro datasets consist- ing of data from both healthy people and amputees. Experimental results show the proposed method attains the state-of-the-art (SOTA) accuracy on both datasets. We further show that the proposed method enjoys strong generalization ability: a new SOTA is achieved by pretraining the model on a different dataset followed by fine-tuning it on the target dataset.

show abstract

Section: Deep Learning Based Methodsmentioning

confidence: 99%