Augmenting Neuromuscular Disease Detection Using Optimally Parameterized Weighted Visibility Graph.

In this contribution, we propose a novel neuromuscular disease detection framework employing weighted visibility graph (WVG) aided analysis of electromyography signals. WVG converts a time series into an undirected graph, while preserving the signal properties. However, conventional WVG is sensitive to noise and has high computational complexity which is problematic for lengthy and noisy time series analysis. To address this issue in this article, we investigate the performance of WVG by varying two important parameters, namely penetrable distance and scale factor, both of which have shown promising results by eliminating the problem of signal adulteration and decreasing the computational complexity, respectively. We also aim to unfold the combined effect of these two aforesaid parameters on the WVG performance to discriminate between myopathy, amyotrophic lateral sclerosis (ALS) and healthy EMG signals. Using graph theory based features we demonstrated that the discriminating capability between the three classes increased significantly with the increase in both penetrable distance and scale factor values. Three binary (healthy vs. myopathy, myopathy vs. ALS and healthy vs. ALS) and one multiclass problems (healthy vs. myopathy vs. ALS) have been addressed in this study and for each problem, we obtained optimum parameter values determined on the basis of F-value computed using one way analysis of variance (ANOVA) test. Using optimal parameter values, we obtained mean accuracy of 98.57%, 98.09% and 99.45%, respectively for three binary and 99.05% for the multi-class classification problem. Additionally, the computational time was reduced by 96% with optimally selected WVG parameters compared to traditional WVG.