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A new Cepstral-based biomarker of reward positivity evaluated in Parkinson’s disease detection | ||
| مجله مهندسی برق دانشگاه تبریز | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 01 اردیبهشت 1403 | ||
| نوع مقاله: علمی-پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/tjee.2024.60087.4798 | ||
| نویسندگان | ||
| یاسمین اعزازی1؛ پیوند قادریان* 2 | ||
| 1دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران | ||
| 2عضو هیات علمی/گروه بیوالکتریک، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند | ||
| چکیده | ||
| Parkinson’s disease (PD) is one of the central nervous system disorders that affect dopaminergic neurons in the substantia nigra, leading to impairments in midbrain dopaminergic functions. The development of efficient detection is required to control the impairments. In previous PD detection works, either the detection cost or complexity is high or the robustness of the method to clinical parameters or individual differences is low. This article provides a reliable PD detection method proposing a new marker of reward positivity using a Cepstral decomposition of electroencephalogram (EEG) signals discriminating oscillation and excitation components and providing amplitude and phase information while minimizing the number of analyzed coefficients. Cepstral analysis has been used for extracting a more effective representation of spectral information of the quasi-periodic signals using source-filter separation. The capability of this method has been evaluated using 28 patients on both ON and OFF medication states and 28 healthy control individuals during the reinforcement-learning task. It has achieved an average accuracy rate of 99.79% by minimizing the real Cepstrum coefficients to 250 from 4000 ones. It has also obtained satisfactory results on medication states and frontal channels (85% channel reduction) indicating the efficiency, robustness, and cost-effectiveness of the method. | ||
| کلیدواژه‌ها | ||
| Neurodegenerative disease؛ reinforcement-learning task؛ Cepstrum analysis؛ automatic diagnosis؛ EEG signal processing؛ machine learning | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 47 |
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