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Monfared, Negar, Maleki, Ali. (1404). Short-time Classification of Neurodegenerative Diseases Based on Cross-Recurrence Quantification Analysis and Statistical Features. نشریه هنرهای کاربردی, 5(3), 1-11. doi: 10.22075/mseee.2025.37723.1213
Negar Monfared; Ali Maleki. "Short-time Classification of Neurodegenerative Diseases Based on Cross-Recurrence Quantification Analysis and Statistical Features". نشریه هنرهای کاربردی, 5, 3, 1404, 1-11. doi: 10.22075/mseee.2025.37723.1213
Monfared, Negar, Maleki, Ali. (1404). 'Short-time Classification of Neurodegenerative Diseases Based on Cross-Recurrence Quantification Analysis and Statistical Features', نشریه هنرهای کاربردی, 5(3), pp. 1-11. doi: 10.22075/mseee.2025.37723.1213
Monfared, Negar, Maleki, Ali. Short-time Classification of Neurodegenerative Diseases Based on Cross-Recurrence Quantification Analysis and Statistical Features. نشریه هنرهای کاربردی, 1404; 5(3): 1-11. doi: 10.22075/mseee.2025.37723.1213

Short-time Classification of Neurodegenerative Diseases Based on Cross-Recurrence Quantification Analysis and Statistical Features

Modeling and Simulation in Electrical and Electronics Engineering
دوره 5، شماره 3 - شماره پیاپی 21، دی 2025، صفحه 1-11    اصل مقاله (650.98 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/mseee.2025.37723.1213
نویسندگان
Negar Monfared1؛ Ali Maleki* 2
1Department of Biotechnology, Faculty of New Sciences and Technologies, Semnan University, Semnan, Iran.
2Biomedical Engineering Department, Semnan University, Semnan, Iran.
تاریخ دریافت: 21 اردیبهشت 1404،  تاریخ بازنگری: 24 شهریور 1404،  تاریخ پذیرش: 02 مهر 1404 
چکیده
Neurodegenerative diseases refer to progressive disorders of the nervous system that impair motor functions. Using machine learning techniques to analyze and classify gait data can lead to early diagnosis and better management of treatments. This paper aims to classify neurodegenerative patients and healthy individuals by analyzing a three-second gait signal using a combination of effective features from cross-recurrence quantification analysis (CRQA) and statistical analysis. The dataset includes force signals from the left and right feet of 16 healthy individuals (HC), 13 with amyotrophic lateral sclerosis (ALS), 15 with Parkinson's disease (PD), and 20 with Huntington's disease (HD). The CRQA features extracted include recurrence rate, determinism, averaged diagonal length, length of longest diagonal length, entropy of diagonal length, laminarity, trapping time, length of longest vertical line, recurrence time of 1st type, recurrence time of 2nd type, recurrence period density entropy, clustering coefficient, and transitivity. Statistical features include mean, variance, skewness, and kurtosis. A sequential feature selection algorithm was used to select effective features. The classification accuracy for the Ensemble (Bagged Trees) classifier was obtained using 10-fold cross-validation for the groups HC vs. PD, HC vs. HD, HC vs. ALS, ALS vs. PD, ALS vs. HD, PD vs. HD, NDD vs. HC, and ALS vs. PD vs. HD vs. HC, with the respective accuracy values of 98.3%, 94.8%, 97.7%, 98.2%, 98.4%, 95%, 94%, and 93.5%. The results indicate that the effective fusion of features and the ability of cross-recurrence quantification analysis to quantify the synergistic relationship of the dynamic movements of the left and right feet provide an effective means of diagnosing diseases during the short 3-second walking period.
کلیدواژه‌ها
Cross-Recurrence Quantification Analysis؛ Statistical Features؛ Force Signal؛ Gait؛ Classification
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