F-SVD based algorithm for variability and stability measurement of bio-signals, feature extraction and fusion for pattern recognition
By Anil Hazarika, M. Barthakur, L. Dutta, Manabendra Bhuyan. 2019
15 citations recorded by the source corpus.
Abstract A support system with efficient learning framework helps eliciting complete knowledge of underlying phenomena of interest. It makes the analysis less-onerous, time-consuming and error-prone and thus promotes large scale applications. Such modeling requires profound understanding of available information and its appropriate utilization. Albeit success of electromyogram (EMG) support systems, challenges still exits specifically in early phase of design mainly due to inherent variations and complex data distribution patterns of signals. In this article, a frame singular value decomposition (F-SVD) based method-generalizing Canonical correlation analysis for automatic classification of EMG signals to diagnose amyotrophic lateral sclerosis (ALS), myopathy and normal subjects, is proposed. At first, signals are decomposed to formulate a set of vectors and performed subspace transformation to demonstrate the variability and stability of signals base on correlations between pairs of vectors. Besides, discrete Wavelet transformation is applied on generated vectors and correlation analysis is performed. Afterwards, taking highly correlated statistical measures a set of compact feature distributions are estimated and fused via two recently proposed parallel and serial feature fusion models. Finally two global descriptors for effective classifications of various EMG patterns are proposed. The efficacy of derived feature space is validated by intuitive, graphical and statistical analysis. The model performances are investigated over two datasets. It achieves accuracy of 98.10% and 97.60% over two and three-class groups of first dataset receptively. Accuracy over second dataset is 100% with a specificity of 100% and sensitivities of 100%. This is first time that F-SVD is employed for automatic classification of EMG. Experiments results on various datasets evince adequacy of our method. Further comparison of performance with state-of-the-art methods depicts that our method comparable or superior in terms of various performance metrics.
Subfields: Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction
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Citations in the corpus
- A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, methodology): “rete wavelet package transform (MODWPT) also applied on EEG data for epilepsy classification. Frame singular value decomposition (F-SVD) is introduced for classifications of electromyography (EMG) data [113]. It is a pipeline involving a number of processing that includes DWT, CCA and SVD, achieving a”