انتخاب یک فضای ویژگی بهینه در تشخیص حملات صرعی بر پایه آنالیز کمی‌سازی بازگشتی و الگوریتم ژنتیک

محورهای موضوعی : پردازش سیگنالهای پزشکی

صالح لشکری ¹ (دانشجوی دکترا - دانشکده مهندسی برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران)
مهدی آذرنوش ² (استادیار - دانشکده مهندسی برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران)

تاریخ دریافت : 1394/05/01 تاریخ پذیرش : 1395/02/06 تاریخ انتشار : 1395/04/11

کلید واژه: الگوریتم ژنتیک, انتخاب ویژگی, الکتروانسفالوگرام, تشخیص حمله صرعی, آنالیز کمی‌سازی بازگشتی,

چکیده مقاله :

در طبقه‌بندی داده‌ها انتخاب فضای ویژگی متناسب با ماهیت پدیده و قدرت تفکیک بالا بسیار حائز اهمیت است. قابلیت نگاشت‌ بازگشتی در تحلیل دادگان غیرایستا موجب می‌شود در تشخیص حملات صرعی نیز مورد توجه قرار گیرد. در این پژوهش به تشخیص حملات صرعی توسط آنالیز کمی‌سازی بازگشتی بر پایه ترکیب الگوریتم ژنتیک و طبقه‌بند بیزین ‌پرداخته شده است. در ابتدا نگاشت بازگشتی سیگنال EEG دو گروه صرعی و نرمال هریک شامل 100 نمونه، بازای پنج نوع معیار فاصله (ماکزیمم فاصله، مینیمم فاصله، اقلیدوسی، ماهالانوبیس، منهتن) و 10 حد آستانه(ε) مختلف تشکیل و بهترین مجموعه ویژگی‌ بازای 50 تکرار الگوریتم ژنتیک بر اساس نرخ طبقه‌بندی بیزین انتخاب گردید. نتایج، نشانگر کارایی بالای روش پیشنهادی بوده به گونه‌ای که با انتخاب معیار مینیمم فاصله و حدآستانه 1˂ε˂ 1/0 تفکیک 100 % است. همچنین روش نسبت به حد آستانه (ε) و معیار فاصله حساسیت پایینی دارد. ویژگی Trans با بیشترین مشارکت در انتخاب ویژگی و بالاترین صحت، به عنوان ویژگی بهینه معرفی می‌شود.

چکیده انگلیسی:

Selecting optimal features based on nature of the phenomenon and high discriminant ability is very important in the data classification problems. Since it doesn't require any assumption about stationary condition and size of the signal and the noise in Recurrent Quantification Analysis (RQA), it may be useful for epileptic seizure Detection. In this study, RQA was used to discriminate ictal EEG from the normal EEG where optimal features selected by combination of algorithm genetic and Bayesian Classifier. Recurrence plots of hundred samples in each two categories were obtained with five distance norms in this study: Euclidean, Maximum, Minimum, Normalized and Fixed Norm. In order to choose optimal threshold for each norm, ten threshold of ε was generated and then the best feature space was selected by genetic algorithm in combination with a bayesian classifier. The results shown that proposed method is capable of discriminating the ictal EEG from the normal EEG where for Minimum norm and 0.1˂ε˂1, accuracy was 100%. In addition, the sensitivity of proposed framework to the ε and the distance norm parameters was low. The optimal feature presented in this study is Trans which it was selected in most feature spaces with high accuracy.

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