ارائه یک نشانگر پاداش مثبت زیستی جدید مبتنی بر کپستروم ارزیابی شده در تشخیص بیماری پارکینسون

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

2 عضو هیات علمی/گروه بیوالکتریک، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند

چکیده

پارکینسون یک بیماری سیستم عصبی مرکزی است که بر نورون های دوپامین در جسم سیاه اثر گذاشته و سبب اختلال در عملکردهای دوپامینرژیک مغز میانی می گردد. توسعه روشهای موثر تشخیص این بیماری جهت کنترل و مدیریت اختلالات ایجاد شده موثر می باشد. در روش های پیشین ارائه شده جهت تشخیص این بیماری، پیچیدگی محاسباتی یا هزینه تشخیص بالا بوده و یا مقاومت آنها در برابر پارامترهای کلینیکی و یا تفاوت های بین فردی پایین می باشد. از این رو در مقاله حاضر یک روش تشخیصی قابل اعتماد از طریق ارائه یک نشانگر زیستی پاداش مثبت با استفاده از تجزیه کپستروم سیگنال الکتروانسفالوگرام از طریق تفکیک اجزای نوسانس و تحریکی سیگنال و فراهم نمودن اطلاعات دامنه و فاز در عین کم نمودن تعداد ضرایب تحلیلی ارائه شده است. تحلیل کپستروم جهت استخراج نمایش موثرتری از اطلاعات طیفی سیگنال های گوسی-متناوب با استفاده از تفکیک منبع-فیلتر مورد استفاده قرار گرفته است. قابلیت این روش پیشنهادی با کمک داده های ثبت شده در حین اجرای تکلیف یادگیری تقویتی مربوط به 28 فرد بیمار در حالت با و بدون مصرف دارو و 28 فرد سالم مورد ارزیابی قرار گرفته است. روش پیشنهادی توانسته است به میانگین صحت 99/79 درصد به کمک ضرایب حقیقی کپستروم کاهش یافته به 250 ضریب از تعداد کل 4000 ضریب دست یابد. همچینی نتایج رضایت بخشی در هر دو حالت دارویی و لوب فرونتال در عین کاهش 85 درصدی تعداد کانال های موردبررسی، به دست آمده است که نشان دهنده کارایی، مقاومت و مقرون به صرفه بودن روش پیشنهادی می باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A new Cepstral-based biomarker of reward positivity evaluated in Parkinson’s disease detection

نویسندگان [English]

  • Yasamin Ezazi 1
  • Peyvand Ghaderyan 2
1 Faculty of biomedical engineering_Sahand university of technology_Tabriz_Iran
2 Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
چکیده [English]

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 [English]

  • Neurodegenerative disease
  • reinforcement-learning task
  • Cepstrum analysis
  • automatic diagnosis
  • EEG signal processing
  • machine learning

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