نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی پزشکی، گروه مهندسی پزشکی، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران

2 دانشیار، گروه مهندسی کنترل، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران

چکیده

طبقه‌بندی خودکار مراحل خواب به منظور تشخیص به موقع اختلالات و مطالعات مرتبط با خواب امری ضروری است. در این مقاله یک الگوریتم مبتنی بر EEG تک‌کاناله برای شناسایی خودکار مراحل خواب با استفاده از تبدیل موجک گسسته و مدل ترکیبی الگوریتم کلونی مورچگان و شبکه‌ی عصبی مبتنی بر طبقه‌بند RUSBoost ارائه شده است. سیگنال با استفاده از تبدیل موجک گسسته به چهار سطح تجزیه‌ شده و ویژگی‌های آماری از هر یک از این سطوح استخراج شده است. جهت بهینه‌سازی و کاهش ابعاد بردارهای ویژگی، از یک مدل ترکیبی الگوریتم کلونی مورچگان و شبکه‌ی عصبی چندلایه‌ی پس‌انتشار خطا استفاده شده و سپس از آزمون ANOVA برای تایید صحت ویژگی‌های بهینه بهره گرفته شده است. طبقه‌بندی نهایی روی این ویژگی‌های بهینه شده توسط طبقه‌بند RUSBoost صورت گرفته و مشاهده شده است که به طور میانگین صحت طبقه‌بندی 2 تا 6-کلاس مراحل مختلف خواب بالای 90% بوده که نشان دهنده‌ی درصد موفقیت بالاتر روش پیشنهادی در طبقه‌بندی مراحل خواب نسبت به پژوهش‌های پیشین می‌باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Automatic Stage Scoring of Single-Channel Sleep EEG using Discrete Wavelet Transform and a Hybrid Model of Ant Colony Optimizer and Neural Network based on RUSBoost Classifier

نویسندگان [English]

  • Sobhan Sheykhivand 1
  • Sehraneh Ghaemi 2

1 Ph.D. Student, Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

2 Associate Professor, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

چکیده [English]

The automatic classification of sleep stages is essential for the timely detection of disorders and sleep-related studies. In this paper, a single-channel EEG-based algorithm is used to automatically identify sleep stages using discrete wavelet transform and a hybrid model of ant colony optimizer and neural network based on RUSBoost. The signal is decomposed using a discrete wavelet transform into four levels and statistical properties of each level are calculated. To optimize and reduce the dimensions of feature vectors, hybrid model of ant colony optimizer algorithm and multi-layered neural network are used. Then ANOVA test is applied to validate the selected features. Finally, the classification is performed on RUSBoost, which provides an average of 90% classification accuracy for 2 to 6-class classification of different steps of sleep EEG. Suggesting that the proposed method has a higher degree of success in classifying sleep stages compared to the existing methods.

کلیدواژه‌ها [English]

  • Discrete Wavelet Transform
  • Automatic Sleep Stage Detection
  • Ant Colony Optimization Algorithm
  • Rusboost
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