Detection of neurodegenerative diseases using hybrid MODWT and adaptive local binary pattern

• Published in: Neural computing & applications Vol. 36; no. 31; pp. 19417 - 19433
• Main Authors: Prasanna, J., George, S. Thomas, Subathra, M. S. P.
• Format: Journal Article
• Language: English
• Published: London Springer London 01-11-2024; Springer Nature B.V
• Subjects: Amyotrophic lateral sclerosis; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Discrete Wavelet Transform; Disease control; Feature extraction; Gait; Image Processing and Computer Vision; Original Article; Parkinson's disease; Pattern analysis; Probability and Statistics in Computer Science; Signal classification; Time series; Walking; Wavelet transforms; Long short-term memory; Local binary pattern; Maximal overlapped discrete wavelet transform; Gait; Neurodegenerative diseases; Classification
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-10222-1

Neurodegenerative diseases cause significant irregularities in walking patterns, impacting gait dynamics and rhythms analyzed through gait time series. Human gait analysis is a promising avenue for identifying unique walking patterns. Automated computer-aided techniques show potential in tracking pathological progression, particularly through non-invasive methods using football contact sensors. In this study, wavelet coefficients extracted via the maximal overlapped discrete wavelet transform from gait time series provide valuable insights into neurological deficiencies and deviations in gait. We employed various local binary patterns (LBPs), including inverse LBP, adaptive right-shifted LBP, and adaptive left-shifted LBP (ALS-LBP) on wavelet coefficients for classifying neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), Parkinson’s disease (PD), and healthy control (HC). Histogram-oriented features were extracted using different binary pattern techniques on gait time series. The feature subset was classified using the long short-term memory classifier. The study achieved maximum accuracy across all experimental cases, analyzing signals from left, right, and both feet during stride, swing, and stance. This approach demonstrated 100% classification accuracy for tasks involving HC versus PD, ALS versus HD, and ALS versus PD. The proposed method could open avenues for early-stage diagnosis of neurodegenerative diseases.