SincPD: An Explainable Method based on Sinc Filters to Diagnose Parkinson's Disease Severity by Gait Cycle Analysis

[2502.17463] SincPD: An Explainable Method based on Sinc Filters to Diagnose Parkinson's Disease Severity by Gait Cycle Analysis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2502.17463 (eess) [Submitted on 10 Feb 2025] Title: SincPD: An Explainable Method based on Sinc Filters to Diagnose Parkinson's Disease Severity by Gait Cycle Analysis Authors: Armin Salimi-Badr , Mahan Veisi , Sadra Berangi View a PDF of the paper titled SincPD: An Explainable Method based on Sinc Filters to Diagnose Parkinson's Disease Severity by Gait Cycle Analysis, by Armin Salimi-Badr and 2 other authors View PDF HTML (experimental) Abstract: In this paper, an explainable deep learning-based classifier based on adaptive sinc filters for Parkinson's Disease diagnosis (PD) along with determining its severity, based on analyzing the gait cycle (SincPD) is presented. Considering the effects of PD on the gait cycle of patients, the proposed method utilizes raw data in the form of vertical Ground Reaction Force (vGRF) measured by wearable sensors placed in soles of subjects' shoes. The proposed method consists of Sinc layers that model adaptive bandpass filters to extract important frequency-bands in gait cycle of patients along with healthy subjects. Therefore, by considering these frequencies, the reasons behind the classification a person as a patient or healthy can be explained. In this method, after applying some preprocessing processes, a large model equipped with many filters is first trained. Next, to prune the extra units and reach a more explainable and parsimonious structure, the extracted filters are clusters based on their cut-off frequencies using a centroid-based clustering approach. Afterward, the medoids of the extracted clusters are considered as the final filters. Therefore, only 15 bandpass filters for each sensor are derived to classify patients and healthy subjects. Finally, the most effective filters along with the sensors are determined by comparing the energy of each filter encountering patients and healthy subjects. Subjects: Signal Processing (eess.SP) ; Machine Learning (cs.LG) Cite as: arXiv:2502.17463 [eess.SP] (or arXiv:2502.17463v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2502.17463 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Discover Artificial Intelligence, 2026 Related DOI : https://doi.org/10.1007/s44163-026-01993-8 Focus to learn more DOI(s) linking to related resources Submission history From: Armin Salimi-Badr PhD [ view email ] [v1] Mon, 10 Feb 2025 17:52:26 UTC (15,095 KB) Full-text links: Access Paper: View a PDF of the paper titled SincPD: An Explainable Method based on Sinc Filters to Diagnose Parkinson's Disease Severity by Gait Cycle Analysis, by Armin Salimi-Badr and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2025-02 Change to browse by: cs cs.LG eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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