تشخیص تشنج صرعی با استفاده از الکتروانسفالوگرافی
ترجمه نشده

تشخیص تشنج صرعی با استفاده از الکتروانسفالوگرافی

عنوان فارسی مقاله: مروری بر استخراج ویژگی و ارزیابی عملکرد در تشخیص تشنج صرعی با استفاده از الکتروانسفالوگرافی (EEG)
عنوان انگلیسی مقاله: A review of feature extraction and performance evaluation in epileptic seizure detection using EEG
مجله/کنفرانس: پردازش و کنترل سیگنال زیست پزشکی – Biomedical Signal Processing and Control
رشته های تحصیلی مرتبط: مهندسی پزشکی، پزشکی
گرایش های تحصیلی مرتبط: بیوالکتریک، مغز و اعصاب
کلمات کلیدی فارسی: تشخیص تشنج، الکتروانسفالوگرافی (EEG)، استخراج ویژگی، طبقه بندی
کلمات کلیدی انگلیسی: Seizure detection، EEG، Feature extraction، classification
نوع نگارش مقاله: مقاله مروری (Review Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.bspc.2019.101702
دانشگاه: Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 3.830 در سال 2019
شاخص H_index: 51 در سال 2020
شاخص SJR: 0.711 در سال 2019
شناسه ISSN: 1746-8094
شاخص Quartile (چارک): Q2 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 16
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14140
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


۱٫ Introduction


۲٫ Feature extraction


۳٫ Epileptic seizure detection


۴٫ Methods for feature evaluation


۵٫ Experimental results


۶٫ Conclusions


Acknowledgement


References

نمونه متن انگلیسی مقاله

Abstract


Since the manual detection of electrographic seizures in continuous electroencephalogram (EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop automatic seizure detection are diverse and ongoing. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre-screened results for neurologists. Several features, data transformations, and classifiers have been explored to analyze and classify seizures via EEG signals. In the literature, some jointly-applied features used in the classification may have shared similar contributions, making them redundant in the learning process. Therefore, this paper aims to comprehensively summarize feature descriptions and their interpretations in characterizing epileptic seizures using EEG signals, as well as to review classification performance metrics. To provide meaningful information of feature selection, we conducted an experiment to examine the quality of each feature independently. The Bayesian error and non-parametric probability distribution estimation were employed to determine the significance of the individual features. Moreover, a redundancy analysis using a correlation-based feature selection was applied. The results showed that the following features – variance, energy, nonlinear energy, and Shannon entropy computed on a raw EEG signal, as well as variance, energy, kurtosis, and line length calculated on wavelet coefficients – were able to significantly capture the seizures. When compared with a baseline method of classifying all epochs as normal, an improvement of 4.77–۱۳٫۵۱% in the Bayesian error was obtained.


Introduction


An epileptic seizure, as defined by the International League Against Epilepsy [1], is a temporary event of symptoms due to synchronization of abnormally excessive activities of neurons in the brain. It has been estimated that approximately 65 million people around the world are affected by epilepsy [2]. Nevertheless, it is still a time-consuming process for neurologists to review continuous electroencephalograms (EEGs) to monitor epileptic patients. Therefore, several researchers have developed differenttechniques that help neurologists to identify an epilepsy occurrence [3–۵]. The whole process of automated epileptic seizure analysis primarily consists of data acquisition, signal pre-processing, feature extraction, feature or channel selection, and classification. This paper focuses on a selection of features commonly used in the literature, including statistical parameters (mean, variance, skewness, and kurtosis), amplitude-related parameters (energy, nonlinear energy, line length, maximum and minimum values) and entropyrelated measures. These features can be categorized based on their interpretation or the domain from which the features are calculated. While some studies have considered a particular group of features applicable to their proposed classification method [6–۸], others have applied various groups of features extracted from the time, frequency, and time-frequency domains.

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