Combining homomorphic filtering and recurrent neural network in gait signal analysis for neurodegenerative diseases detection

• Autorzy: Saljuqi Masume , Ghaderyan Peyvand

Identyfikatory

DOI

10.1016/j.bbe.2023.04.001

• Języki publikacji: EN

Abstrakty

EN

Automatic, cost-effective, and reliable detection of neurodegenerative diseases (NDs) is one of the important issues in clinical practice. The main idea of the proposed method in this study is to utilize the advantages of gait time series that can provide low-cost and noninvasive measures, homomorphic filtering that can effectively separate muscle activity from body dynamic and recurrent neural network or cascade forward neural network that can learn sequential time-varying data. Experimental results on gait time series of 16 healthy control subjects, 13 patients with amyotrophic lateral sclerosis, 15 patients with Parkinson’s disease and 20 patients with Huntington’s disease have demonstrated high detection performance with an accuracy rate of 100 % using K-fold cross validation for all three types of NDs outperforming other existing methods. The results have also indicated that the use of real cepstral coefficients, oscillation components, or basic statistics feature set has improved the detection performance.

Słowa kluczowe

EN

recurrent neural network; cascade forward neural network; neurodegenerative diseases detection; cepstrum analysis

PL

sieć neuronowa rekurencyjna; choroba neurodegeneracyjna; analiza cepstralna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 2
• Strony: 476--493
• Opis fizyczny: Bibliogr. 80 poz., rys., tab., wykr.

Twórcy

autor

Saljuqi Masume

• Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

autor

Ghaderyan Peyvand

• Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)