A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis. (May 2020)

• Record Type: Journal Article
• Authors: Marziyeh Ghoreshi Beyrami, Seyede; Ghaderyan, Peyvand
• Is Part Of: Measurement. Volume 156(2020)
• Publication Date: 2020-05
• Subjects: Amyotrophic lateral sclerosis -- Huntington's disease -- Parkinson's disease -- Vertical ground reaction force of gait signals; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107579
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A robust diagnosis method is proposed based on vertical reaction forces of gait. The efficiency of time-domain features and different classifiers is investigated. The proposed method offers good tradeoff between accuracy and cost. It is robust against severity of diseases and the right and left foot parameters. Abstract: One of the challenges of computer-aided diagnostic systems is to propose a reliable algorithm detecting different types of neurodegenerative diseases using cost-effective procedures. To tackle the challenge, this study developed a new methodology based on statistical and entropic features of vertical ground reaction forces of gait and sparse coding classification technique. The effect of individual differences on the proposed and standard machine learning methods was also explored with emphasize on the severity and duration of diseases as well as the right and left foot parameters. This method was evaluated using a publicly available dataset, which contains 16 healthy control subjects, 13 patients with Amyotrophic lateral sclerosis (ALS), 15 patients with Parkinson's disease (PD), and 20 patients with Huntington's disease (HD). It achieved the best average accuracy rates of 100%, 99.78%, and 99.90% for ALS, PD, and HD detection, respectively. The results confirmed that the proposed algorithm can identify all diseases at both early and advanced stages using either left or right foot features.