مشخصات مقاله | |
ترجمه عنوان مقاله | یک رویکرد جدید مبتنی بر آنالیز موجک و برنامه نویسی حسابی برای تشخیص خودکار و تشخیص تشنج صرعی در سیگنال های EEG با استفاده از تکنیک های یادگیری ماشین |
عنوان انگلیسی مقاله | A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques |
انتشار | مقاله سال ۲۰۲۰ |
تعداد صفحات مقاله انگلیسی | ۱۰ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
۳٫۸۳۰ در سال ۲۰۱۹ |
شاخص H_index | ۵۱ در سال ۲۰۲۰ |
شاخص SJR | ۰٫۷۱۱ در سال ۲۰۱۹ |
شناسه ISSN | ۱۷۴۶-۸۰۹۴ |
شاخص Quartile (چارک) | Q2 در سال ۲۰۱۹ |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | کامپیوتر، پزشکی، مهندسی پزشکی |
گرایش های مرتبط | مغز و اعصاب، هوش مصنوعی، مهندسی نرم افزار، برنامه نویسی، بیوالکتریک، پردازش تصاویر پزشکی |
نوع ارائه مقاله |
ژورنال |
مجله | پردازش و کنترل سیگنال زیست پزشکی – Biomedical Signal Processing And Control |
دانشگاه | Centre for Intelligent Signal & Imaging Research (CISIR), Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia |
کلمات کلیدی | الکتروانسفالوگرافی (EEG)، تشنج صرعی، تبدیل موجک گسسته (DWT)، برنامه نویسی حسابی، طبقه بندی کننده یادگیری ماشین، بازشناختی به کمک رایانه |
کلمات کلیدی انگلیسی | Electroencephalography (EEG)، Epileptic seizure، Discrete wavelet transform (DWT)، Arithmetic coding، Machine learning classifiers، Computer-aided diagnostic |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.bspc.2019.101707 |
کد محصول | E14902 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract
۱- Introduction ۲- Related work ۳- Materials and method ۴- Experimental results ۵- Discussion ۶- Conclusion and future direction References |
بخشی از متن مقاله: |
Abstract Epilepsy, a common neurological disorder, is generally detected by electroencephalogram (EEG) signals. Visual inspection and interpretation of EEGs is a slow, time consuming process that is vulnerable to error and subjective variability. Consequently, several efforts to develop automatic epileptic seizure detection and classification methods have been made. The present study proposes a novel computer aided diagnostic technique (CAD) based on the discrete wavelet transform (DWT) and arithmetic coding to differentiate epileptic seizure signals from normal (seizure-free) signals. The proposed CAD technique comprises three steps. The first step decomposes EEG signals into approximations and detail coefficients using DWT while discarding non-significant coefficients in view of threshold criteria; thus, limiting the number of significant wavelet coefficients. The second step converts significant wavelet coefficients to bit streams using arithmetic coding to compute the compression ratio. In the final step, the compression feature set is standardized, whereupon machine-learning classifiers detect seizure activity from seizure-free signals. We employed the widely used benchmark database from Bonn University to compare and validate the technique with results from prior approaches. The proposed method achieved a perfect classification performance (100% accuracy) for the detection of epileptic seizure activity from EEG data, using both linear and non-liner machine-learning classifiers. This CAD technique can thus be considered robust with an extraordinary detection capability that discriminates epileptic seizure activity from seizure-free and normal EEG activity with simple linear classifiers. The method has the potential for efficient application as an adjunct for the clinical diagnosis of epilepsy. Introduction Epilepsy is a common brain disorder that affects people of all ages. It is a chronic neurological disorder in which recurrent seizures occurs due to abnormal neuronal activities within the human brain and affects the sensorium, mood and/or movement of the human body [1]. World Health Organization statistics indicate that approximately 50 million people currently live with epilepsy worldwide and an estimated 2.4 million people are diagnosed with epilepsy each year [2]. The incidence of the malady is higher in developing countries;i.e., between 7 and 14 per 1000 people. Treatment mostly comprises antiepileptic drugs and/or surgery [3]. The electroencephalogram (EEG) commonly detects seizure activity as it reflects electrophysiological conditions of the brain at a given time [4] and is widely used for diagnostic due to its low cost. EEG signals, enhanced with physiological and pathological data, are employed to evaluate and assess the treatment and progress of epileptic patients. Typically, clinicians evaluate EEG signals for three types of activity: (i) normal EEG activity that records healthy subjects with eyes open or closed; (ii) inter-ictal/seizure-free EEG activity that may contain small spikes and/or subclinical seizures that occur between two clinical episodes in epileptic patients; (iii) and ictal EEG activity containing sudden spikes. |