A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders

TL;DR

This paper introduces GraphTS, a novel network-based method using visibility graphs to analyze EMG time series for diagnosing neuromuscular disorders, achieving high accuracy and improved diagnostic features.

Contribution

The paper presents a new approach, GraphTS, that effectively maps EMG signals to complex networks, enhancing feature extraction and classification accuracy in neuromuscular disorder diagnosis.

Findings

Achieved 99.30% training accuracy and 99.18% testing accuracy.

Effectively differentiates healthy, myopathy, and neuropathy EMG signals.

Improves diagnostic feature representation and classification performance.

Abstract

Electromyography (EMG) refers to a biomedical signal indicating neuromuscular activity and muscle morphology. Experts accurately diagnose neuromuscular disorders using this time series. Modern data analysis techniques have recently led to introducing novel approaches for mapping time series data to graphs and complex networks with applications in diverse fields, including medicine. The resulting networks develop a completely different visual acuity that can be used to complement physician findings of time series. This can lead to a more enriched analysis, reduced error, more accurate diagnosis of the disease, and increased accuracy and speed of the treatment process. The mapping process may cause the loss of essential data from the time series and not retain all the time series features. As a result, achieving an approach that can provide a good representation of the time series while maintaining essential features is crucial. This paper proposes a new approach to network development named GraphTS to overcome the limited accuracy of existing methods through EMG time series using the visibility graph method. For this purpose, EMG signals are pre-processed and mapped to a complex network by a standard visibility graph algorithm. The resulting networks can differentiate between healthy and patient samples. In the next step, the properties of the developed networks are given in the form of a feature matrix as input to classifiers after extracting optimal features. Performance evaluation of the proposed approach with deep neural network shows 99.30% accuracy for training data and 99.18% for test data. Therefore, in addition to enriched network representation and covering the features of time series for healthy, myopathy, and neuropathy EMG, the proposed technique improves accuracy, precision, recall, and F-score.