Texture Classification and Visualization of Time Series of Gait Dynamics in Patients With Neuro-Degenerative Diseases

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 26; no. 1; pp. 188 - 196
• Main Author: Pham, Tuan D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2018
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; amyotrophic lateral sclerosis; Amyotrophic Lateral Sclerosis - physiopathology; Biomechanical Phenomena; Feature extraction; Female; Fuzzy Logic; fuzzy recurrence plots; Gait; Gait Disorders, Neurologic - classification; Gait Disorders, Neurologic - physiopathology; Gait dynamics; High definition video; Humans; Huntington Disease - physiopathology; Huntington’s disease; Limit of Detection; Male; Middle Aged; Neurodegenerative Diseases - classification; Neurodegenerative Diseases - physiopathology; Parkinson Disease - physiopathology; Parkinson's disease; pattern classification; Pattern Recognition, Automated; Reproducibility of Results; Support Vector Machine; texture analysis; time series; Time series analysis; Visualization; Young Adult
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2017.2732448

The analysis of gait dynamics is helpful for predicting and improving the quality of life, morbidity, and mortality in neuro-degenerative patients. Feature extraction of physiological time series and classification between gait patterns of healthy control subjects and patients are usually carried out on the basis of 1-D signal analysis. The proposed approach presented in this paper departs itself from conventional methods for gait analysis by transforming time series into images, of which texture features can be extracted from methods of texture analysis. Here, the fuzzy recurrence plot algorithm is applied to transform gait time series into texture images, which can be visualized to gain insight into disease patterns. Several texture features are then extracted from fuzzy recurrence plots using the gray-level co-occurrence matrix for pattern analysis and machine classification to differentiate healthy control subjects from patients with Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. Experimental results using only the right stride-intervals of the four groups show the effectiveness of the application of the proposed approach.