Classification of gait signals into different neurodegenerative diseases using statistical analysis and recurrence quantification analysis

• Published in: Pattern recognition letters Vol. 139; pp. 10 - 16
• Main Authors: Prabhu, Pooja, Karunakar, A.K., Anitha, H., Pradhan, N.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-11-2020; Elsevier Science Ltd
• Subjects: Abnormalities; Amyotrophic lateral sclerosis; Bladder; Classification; Disorders; Feature extraction; Fibrillation; Fourier analysis; Gait; Gait recognition; Heart rate; Indexing; Movement disorders; Nervous system; Neural networks; Neurodegenerative diseases; Neurological disorder; Parameters; Physicians; Probabilistic neural networks; Recurrence quantification analysis; Signal classification; Sports medicine; Statistical analysis; Statistics; Support vector machine; Support vector machines; Ventricle; Ventricular fibrillation; Wrist; Recurrence quantification analysis; Support vector machine; Gait; Neurological disorder; Probabilistic neural networks; Sports medicine
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2018.05.006

•Understanding the dynamics of gait using recurrence quantification analysis.•Classification of subjects using Support Vector Machine and Probabilistic Neural Network.•Incorporating algorithm in gait recording machine helps in automated classification.
Among all the biological signals, gait signal is one of the better features to detect movement disorders caused by a malfunction in parts of the brain and nervous system. Usually, identifying and evaluating movement disorders caused due to neurodegenerative diseases solely depends on a physicians experience. Different diseases having gait abnormalities generate a unique gait characteristic. Traditionally, Fourier analysis is used to understand the gait characteristic, thereby predicting potential diseases. Fourier analysis assumes the gait signal to be stationary, linear and noiseless which is not a reality. To overcome this, Recurrence Quantification Analysis (RQA) is used in this study to quantify gait parameters. RQA has proved to be one of the best tools for non-linear, non-stationary and short length data. It is used to quantify heart rate variability, ventricular fibrillation, wrist pulse and growth of bladder. This paper uses RQA in understanding the dynamics of human gait and the parameters obtained are used as a feature for classification using Support Vector Machine (SVM) and Probabilistic Neural Network (PNN). This study considered thirteen subjects for the classification of gait signals of patients with Neurodegenerative diseases (Amyotrophic Lateral Sclerosis, Huntington and Parkinson) and thirteen healthy control subjects using two different classification models like Support Vector Machine (SVM) and Probabilistic Neural Network (PNN). Features were extracted after statistical analysis and RQA, and Hill-climbing feature selection method was used to optimize the feature set. The accuracy deduced after binary classification using SVM and PNN ranged from 96% to 100%.