نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشکده‌ی مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

2 استادیار، آزمایشگاه علوم اعصاب محاسباتی، دانشکده‌ی مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

10.22041/ijbme.2022.534243.1706

چکیده

بیماری پارکینسون یکی از شایع­ترین انواع زوال مغزی بوده که با اختلالات حرکتی و کاهش مهارت­های اجرایی مانند نوشتن همراه است. راه‌کارهای تشخیصی این بیماری­ اغلب به کمک روش‌های تصویربرداری مغزی انجام شده که پرهزینه بوده و یا به صورت تهاجمی قابل اجرا می‌باشند و هم­چنین صحت تشخیصی آن­ها به تجربه و مهارت پزشک وابسته است. از این رو ارائه‌ی یک سیستم تشخیصی خودکار، کم­هزینه و در عین حال قابل اعتماد، مورد توجه محققان قرار دارد. در این مطالعه از سیگنال­ دست‌خط شامل اجزای شناختی و حرکتی-ادراکی، به عنوان یک مشخصه‌ی غیرتهاجمی، کم­هزینه­ و قابل اعتماد در تشخیص اختلالات شناختی و حرکتی حاصل از بیماری پارکینسون استفاده شده است. بدین منظور از الگوریتم پیگیری تطبیقی با رزولوشن زمانی- فرکانسی بالا جهت تجزیه­ی مشخصه­های x-y بهره گرفته شده است. این روش یک نمایش تنک از سیگنال دست‌خط را فراهم آورده و اطلاعات پایه­ای از تغییرات محلی نوشتار را به کمک تعداد ضرایب کم کمی­سازی می­نماید. روش پیشنهادی روی یک پایگاه داده با 31 نمونه­ی سالم و 29 نمونه­ی پارکینسون، به کمک طبقه­بند ماشین بردار پشتیبان مورد ارزیابی قرار گرفته است. نتایج به دست آمده، قدرت تشخیصی بالای روش پیشنهادی با  صحت تشخیص 90%، حساسیت 59/91% و  اختصاصیت 90% را نشان می­دهد. هم‌چنین مقایسه‌ی تکالیف نوشتاری مختلف، عمل‌کرد برتر نگارش جمله را در تشخیص پارکینسون به اثبات رسانیده است. 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

An Efficient Method for Parkinson’s Disease Detection using Handwriting Features

نویسندگان [English]

  • Elham Dehghanpur Deharab 1
  • Peyvand Ghaderyan 2

1 M.Sc. Student, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

2 Assistant Professor, Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

چکیده [English]

Parkinson's disease (PD) is one of the most common types of dementia associated with motor impairments and affected performance of motor skills such as writing. Brain imaging techniques are the common methods used to diagnose PD, which are expensive or invasive, and their accuracy depends on the experience and the skill of the physician. Therefore, the development of an automated, low cost, and reliable diagnostic system is desirable for researchers. In this study, a handwriting signal including cognitive and motor-perceptual components has been used as a non-invasive, cost effective and reliable characteristic in identifying PD-related cognitive and motor dysfunctions. For this purpose, the matching pursuit algorithm with high time-frequency resolution has been employed to decompose X-Y coordinates. It provides a sparse representation of the handwriting signals and quantifies the basic information about the local changes in the handwriting signals. The proposed method is evaluated on a database with 31 healthy samples and 29 Parkinson's samples using the support vector machine classifier and obtained results yields an average accuracy rate of 90%, sensitivity rate of 91.59% and specificity rate of 90%. Comparing different writing tasks has also demonstrated superior performance of writing an entire sentence for PD detection.

کلیدواژه‌ها [English]

  • Parkinson's Disease
  • Handwriting
  • Matching Pursuit
  • Support Vector Machine
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