نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی پزشکی بیوالکتریک، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

2 استادیار، گروه بیوالکتریک، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

10.22041/ijbme.2020.116028.1530

چکیده

بیماری پارکینسون بعد از آلزایمر به عنوان رایج‌ترین بیماری مخرب سیستم عصبی شناخته می‌شود. یکی از عوارض شایع این بیماری، به وجود آمدن اختلالات گفتاری است. با توجه به این که تولید گفتار در انسان شامل تولید صوت در اثر ارتعاش تارهای صوتی (بخش آوایی) و سپس عبور آن از فیلتر لوله‌ی صوتی (بخش تلفظی) است، انتظار می‌رود هر کدام از این دو بخش دچار اختلال شوند. در این تحقیق با استفاده از یک روش غیرتهاجمی و به کمک سیگنال گفتار فرد، به تشخیص بیماری پارکینسون پرداخته شده است. بدین منظور از گویش 3 واکه‌ی کشیده‌ی زبان فارسی توسط 48 نفر (27 نفر مبتلا به بیماری پارکینسون و 21 نفر سالم) استفاده شده است تا میزان تخریب دو بخش تلفظی و آوایی ارزیابی شود. از ویژگی­های مرتبط با بخش آوایی تولید گفتار می­توان به جیتر، شیمر، فرکانس گام و طول زمانی باز و بسته شدن پالس­های چاکنایی و از ویژگی­های بخش تلفظی گفتار می­توان به فرمنت­های اول، دوم و سوم، نرخ عبور از صفر، MFCC و LPC اشاره کرد. در این تحقیق، در مجموع 38 دسته‌ی ویژگی استخراج شده و چهار پارامتر آماری میانگین، انحراف معیار، ضریب چولگی و ضریب کشیدگی از روی آن­ها محاسبه شده است. در ادامه از الگوریتم ژنتیک برای شناسایی ویژگی­های بهینه استفاده شده و شناسایی بیماری پارکینسون با به کارگیری طبقه‌بندهای SVM، KNN و درخت تصمیم‌گیر انجام شده است. به عنوان شاخصه‌ی اصلی این پژوهش، نتایج مربوط به دو بخش آوایی و تلفظی مورد مقایسه و چالش قرار گرفته است. نتایج حاصل از این مطالعه نشان داده که ویژگی­های آوایی با صحت 2/1±1/96% نسبت به ویژگی­های تلفظی در تشخیص بیماری پارکینسون نقش مفیدتری داشته و هم‌چنین واکه‌ی /او/ با میزان صحت 6/97% بهترین عمل‌کرد را در تشخیص بیماری پارکینسون  نسبت به سایر واکه­ها داشته است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Investigating the Phonatory and Articulatory Features in Diagnosis of Parkinson's Disease using Optimized Extracted Features by Genetic Algorithm

نویسندگان [English]

  • Mohammad Bahador Najafi 1
  • Mansour Vali 2

1 M.Sc. Student, Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran

2 Assistant Professor, Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran

چکیده [English]

After Alzheimer, Parkinson's disease is known as the most common malignant disease of the nervous system. One of the common obstacles of this disease is the expansion of speech disorders. Since the speech production in humans is made by combination of vibration of the vocal cords (phonatory section) and then passage through the resonator in vocal tract (articulatory section), it is expected that both of these sections to be impaired. In this study, by using a noninvasive method, it is intended to diagnose Parkinson's disease from speech signal of each subject; for this purpose, using 3 sustain vowels in Persian language recorded from 48 people (27 people with Parkinson's disease and 21 healthy people), it has been evaluated to assess the extent of damage to both phonatory and articulatory sections. The phonatory model can include features such as jitter, shimmer, fundamental frequencies, opening and closing cycling time of the glottal pulses. On the other hand, for the articulatory section, features such as first, second, and third formmants, zero crossing rates, MFFCs, and LPC are investigated. In this study, 38 feature categories were extracted and four statistical parameters of mean, standard deviation, skewness and kurtosis were calculated. Genetic Algorithm was used to identify the optimum features. Then, using the SVM, KNN and the Decision Tree classifiers, the optimum extracted features are classified to determine whether a person is patient or healthy. Finally for the main aim of this study, the results of both phonatory and articulatory sections were compared and challenged. The results of this study showed that phonatory features with accuracy of 96.1±1.2% were more useful than articulatory section in diagnosing of Parkinson. Also it was proved that vowel /u/ has more significant role in the diagnosis of Parkinson's disease compared to other vowels by accuracy of 97.6%.

کلیدواژه‌ها [English]

  • Parkinson's Disease
  • Phonatory Features
  • Articulatory Features
  • genetic algorithm

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