نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی پزشکی، گروه مهندسی پزشکی، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز

2 استادیار، گروه مهندسی پزشکی، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز

3 دانشجوی دکتری مکانیک، گروه مهندسی مکانیک، دانشکده‌ی مهندسی مکانیک، دانشگاه تبریز، تبریز

10.22041/ijbme.2018.82011.1327

چکیده

در سال‌های اخیر، استفاده از روشی هوشمند برای تشخیص خودکار مراحل خواب در کاربردهای پزشکی، برای کاهش حجم کار پزشکان در تجزیه و تحلیل داده‌های خواب از طریق بازرسی بصری، یکی از چالش‌های مهم به حساب می‌آید. در این مقاله، الگوریتمی مبتنی بر EEG تک‌کاناله برای شناسایی خودکار مراحل خواب، با استفاده از تبدیل موجک گسسته و مدل ترکیبی الگوریتم تبرید و شبکه‌ی عصبی ارائه می‌شود. سیگنال با استفاده از تبدیل موجک گسسته به 7 سطح تجزیه‌ شده و ویژگی‌های آماری از هر یک از سطوح تجزیه شده، استخراج ‌می‌گردد. جهت بهینه‌سازی و کاهش ابعاد بردارهای ویژگی، از یک مدل ترکیبی الگوریتم تبرید و شبکه‌ی عصبی چندلایه‌ی پس انتشار خطا استفاده شده، و سپس از آزمون ANOVA برای تائید صحت ویژگی‌های بهینه استفاده می‌شود. طبقه‌بندی نهایی روی این ویژگی‌های بهینه‌شده توسط یک شبکه‌ی عصبی پرسپترون با یک‌لایه‌ی پنهان انجام می‌شود، که به طور میانگین برای طبقه‌بندی 2-کلاس تا 6-کلاس مراحل مختلف خواب دقت بالای 90% را فراهم کرده و نشان می‌دهد که روش پیشنهادی درصد موفقیت بالاتری در طبقه‌بندی مراحل خواب نسبت به پژوهش‌های پیشین دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Automatic Stage Scoring of Single-Channel Sleep EEG using Discrete Wavelet Transform and a Hybrid Model of Simulated Annealing Algorithm and Neural Network

نویسندگان [English]

  • Sobhan Sheykhivand 1
  • Tohid Yousefi Rezaii 2
  • Zohreh Mousavi 3
  • Saeed Meshgini 2

1 M.Sc. Student, Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

2 Assistant Professor, Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

3 Ph.D Student, Department of Mechanical Engineering, Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran

چکیده [English]

Using an intelligent method to automatically detect sleep patterns in medical applications is one of the most important challenges in recent years to reduce the workload of physicians in analyzing sleep data through visual inspection. 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 simulated annealing and neural network. The signal is decomposed using a discrete wavelet transform into seven levels and statistical properties of each level is calculated. To optimize and reduce the dimensions of feature vectors, hybrid model of simulated annealing algorithm and multi-layered neural network are used. Then ANOVA test is applied to validate the selected features. Finally the classification is performed on the validated features by a perceptron neural network with a hidden layer, which provides an average of 90% classification ccuracy for 2 to 6-class classification of different steps of sleep EEG. Suggesting that the proposed method has higher degree of success in classifying sleep stages compared to the existing methods.

کلیدواژه‌ها [English]

  • Discrete wavelet transform
  • Automatic Sleep Stage Detection
  • Simulated Annealing Algorithm
  • Neural network

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