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
چکیده
در سال‌های اخیر تشخیص بیماری‌های عصبی پیشروندهی تدریجی به یکی از چالش‌برانگیزترین مسائل در حوزه‌ی پزشکی تبدیل شده است. اسکلروز جانبی آمیوتروفیک، پارکینسون و هانتینگتون مجموعه‌ای از شایع‌ترین بیماری‌های عصبی پیشروندهی تدریجی بوده که بر کیفیت زندگی بیماران تاثیر به سزایی میگذارند. وقوع این بیماریها به دلیل زوال سلولهای حرکتی سیستم عصبی است که می‌تواند منجر به اختلال در راه رفتن و عدم تقارن بین دو سمت بدن شود. از این رو در این مطالعه، در ابتدا با روش الگوریتم پیگیری تطبیقی سیگنالهای سری زمانی پای چپ و راست در فواصل گام، ایستایی و نوسانی تجزیه و تنک شده، سپس میزان تطابق و همسانی ضرایب به دست آمده توسط یک سری ویژگیهای دینامیکی و دیفرانسیلی ارزیابی شده و اجزای اصلی این ویژگیها به عنوان ورودی به طبقه‌بند کم‌ترین مربعات غیرمنفی تنک داده شده است. الگوریتم پیشنهادی به کمک پایگاه داده‌ی سیگنال راه رفتن شامل ۱۶ فرد سالم، ۱۵ فرد مبتلا به پارکینسون، ۲۰ فرد مبتلا به هانتینگتون و ۱۳ فرد مبتلا به اسکلروز جانبی آمیوتروفیک، آزمایش شده است. نتایج نشان داده که روش پیشنهادی قادر است به ترتیب برای هر سه بیماری اسکلروز جانبی آمیوتروفیک، پارکینسون و هانتینگتون میانگین صحتهای 10/84، 67/86 و 43/91 درصد را ارائه دهد.
کلیدواژه‌ها
عنوان مقاله [English]
Detection of Neurodegenerative Diseases using Time-Frequency Symmetric Features of Gait Signal
نویسندگان [English]
- Masume Saljuqi 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]
In the recent years, the diagnosis of Neurodegenerative Diseases (NDDs) has been one of the most challenging problems in the medical fields. Amyotrophic Lateral Sclerosis (ALS), Parkinson's Disease (PD) and Huntington's Disease (HD) are a group of neurological disorders affecting the quality of patient’s life. Occurrence of these diseases is due to the deterioration of motor neurons, causing human gait disturbance and asymmetry between the right and left limbs. For this purpose, in this paper various gait signals namely stride, swing, and stance intervals (from both legs) have been decomposed using a Matching Pursuit (MP) algorithm. Then, two sets of differential and dynamic features have been extracted from the MP coefficients in order to quantify the amount of divergence between both limbs. Finally, the principal components of these features have been fed as an input to sparse Non-Negative Least Squares (NNLS) classifier. The proposed algorithm has been evaluated using the gait signals of 16 healthy control subjects, 13 patients with Amyotrophic Lateral Sclerosis (ALS), 15 patients with Parkinson’s Disease (PD) and 20 patients with Huntington’s Disease (HD). The results showed that the proposed method has achieved high average accuracy rates of 84.10%, 86.67%, and 91.43% for ALS, PD, and HD detection, respectively.
کلیدواژه‌ها [English]
- Neurodegenerative Diseases
- Gait Analysis
- Time-Frequency Domain
- Symmetry Features
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