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تحلیل جبری سیگنال نیروی عکس‌العمل عمودی زمین برای تشخیص و تفکیک شدت بیماری پارکینسون

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.2021.530841.1694

چکیده

بیماری پارکینسون یکی از رایج­ترین بیماری­های پیش­رونده­ی تدریجی است که با تاثیر بر سیستم عصبی مرکزی باعث بروز اختلالات راه­ رفتن می­شود. از آن‌جا که این بیماری قابل‌درمان نیست، تشخیص صحیح و به موقع آن می­تواند به آهسته کردن سیر پیش‌رفت بیماری، کاهش آسیب­های جسمی و ارتقای کیفیت زندگی بیماران کمک شایانی نماید. در این راستا توسعه­ی سیستم­های تشخیصی با عمل‌کرد سریع، کم‌هزینه و قابل اعتماد حائز اهمیت است. برای حل این مساله در این تحقیق  یک روش تشخیصی با استفاده از سیگنال نیروی عکس­العمل عمودی زمین که یک شاخص غیر­تهاجمی و مفید از نحوه‌ی کنترل حرکتی فراهم می­کند، ارائه شده است. این روش تشخیصی بر اساس تجزیه‌ی تعمیم­یافته‌ی مقدار تکین سیگنال و طبقه­بندهای k-نزدیک­تر­ین همسایگی (KNN) و شبکه‌ی عصبی احتمالی (PNN) است. عمل‌کرد این الگوریتم با استفاده از سیگنال راه رفتن 93 بیمار پارکینسون و 73 فرد سالم مورد ارزیابی قرار گرفته است. نتایج به دست آمده نشان می­دهد که ویژگی جدید متقارن ارائه شده قادر است بیماری پارکینسون را به کمک روش طبقه­بندی k-نزدیک‌ترین همسایگی و شبکه‌ی عصبی احتمالی به ترتیب با صحت 19/96% و 67/95%، حساسیت 02/97% و 35/93% و اختصاصیت 02/95% و 33/97% تشخیص دهد. از سوی دیگر این روش در تشخیص شدت بیماری نیز موفق به ارائه‌ی صحت 23/98% و 51/98%، حساسیت­ 5/93% و 100% و اختصاصیت 100% و 53/96% برای این دو طبقه­بند شده است. صحت بالای نتایج به دست آمده نشان دهنده‌ی قابلیت مناسب روش­ غیرتهاجمی و کم‌هزینه‌ی ارائه شده در تشخیص بیماری پارکینسون و تفکیک شدت آن است که استفاده از آن را در کاربردهای کلینیکی ممکن می­سازد.

کلیدواژه‌ها

عنوان مقاله [English]

Algebraic Analysis of Vertical Ground Reaction Force Signal for diagnosis and Differentiation of Parkinson's Disease Severity

نویسندگان [English]

  • Gisoo Fathi 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 neurodegenerative diseases that cause abnormal gait patterns by affecting central nervous system. Since this disease is incurable, the reliable diagnosis can lead to slowing disease progression, reducing the risk of physical injuries and improving the quality of patient's life. In this regard, the development of fast, cost-effective and reliable detection systems is essential. This study has therefore proposed a detection method using vertical ground reaction force signals, which provide a non-invasive and useful index of the motor control function. It is based on generalized singular value decomposition, K-Nearest Neighbor (KNN) and Probabilistic Neural Network (PNN). The performance of the algorithm has been evaluated by gait signal of 93 individuals with PD and 73 healthy controls. The results have demonstrated that the proposed new symmetric feature is able to achieve 96.19% and 95.67% accuracy rates, 97.22% and 93.35% sensitivity rates, 95.02% and 97.33% specificity rates using the KNN and PNN classifiers, respectively. Furthermore, average accuracy rates of 98.23% and 98.51%, sensitivity rates of 93.5% and 100%, specificity rates of 100% and 96.53% have been obtained for stage classification using these two classifiers. The obtained high average accuracy rates have confirmed the promising capability of the proposed non-invasive and cost-effective method in PD detection and stage classification, which makes it suitable for clinical applications.

کلیدواژه‌ها [English]

  • Parkinson’s Disease
  • K-Nearest Neighbor
  • Probabilistic Neural Network
  • Symmetric Feature
  • Vertical Ground Reaction Force
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فصل نامه علمی پژوهشی مهندسی پزشکی زیستی
دوره 15، شماره 2
شهریور 1400
صفحه 161-174
فایل ها
سابقه مقاله
  • تاریخ دریافت: 04 شهریور 1400
  • تاریخ پذیرش: 04 شهریور 1400
  • تاریخ اولین انتشار: 04 شهریور 1400
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