تشخیص صرع در سیگنال eeg با استفاده از الگوریتم ابتکاری صفحات شیب دار(ipo)

طبق مطالعات انجام شده، در حدود یک درصد از مردم دنیا از صرع رنج میبرند. اولین مرحله از درمان صرع، تشخیص صحیح آن است. یکی از راه های تشخیص صرع، آنالیز دقیق الکتروانسفالوگرافی(eeg) است. بدین منظور، روش های مختلفی جهت تشخیص خودکار صرع بوسیله تحلیل سیگنال eeg ارائه شده است. در این مقاله با استفاده از یک الگوریتم هوشمند و ابتکاری جدید به نام الگوریتم بهینه سازی صفحات شیبدار(ipo)، به تشخیص و جداسازی سیگنال eeg آغشته به صرع از سیگنال های افراد سالم پرداخته ایم. به دلیل خاصیت غیرخطی و ناایستای سیگنال eeg، از تبدیل ویولت جهت استخراج ویژگی های سیگنال بهره گرفته شده است سپس با استفاده از ویژگی های استخراج شده توسط تبدیل ویولت و اعمال آن به سیستم مبتنی بر الگوریتم ipo به تشخیص صرع پرداخته شده است. با استناد به پژوهش انجام شده، مشخص شد که الگوریتم ابتکاری ipo توانایی بالایی در تشخیص صحیح صرع در سیگنال eeg دارد.

Epileptic seizure detection using Inclined Planes system Optimization algorithm(IPO)

Epilepsy is a neurological disorder after stroke. About 1 percent of people in the world are involved with this second most common neurological disorder. Epilepsy can affect people of different ages with an altered behavior or lack of patient awareness and affect one's social life. In 75% of cases, if epilepsy is diagnosed early and properly, it can be treated.Among all existing methods of analysis for the detection of epileptic brain activity, EEG is more applicable, due to its special features (including its lowcost and innocuous). Despite all the advantages of this method, the visual scoring of the EEG records by a human scorer is clearly a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the amount of data needs to be scored. Thus, a tremendous effort has been devoted by researchers towards automatic epileptic seizures detection in EEG.This paper offers a novel method based on heuristic and intelligent algorithms, inclined planes system optimization (IPO), to detect epileptic samples from healthy subjects. Like other heuristic algorithms, IPO is inspired by nature and its laws. How to move sphere objects on the slope without friction and their desire to reach the lowest point, shapes the main idea of the IPO. In the IPO, small balls like particles in the PSO are placed randomly on the search space. The balls search the search space to find the optimal point which is the lowest point (relative to a reference point) on the surface.In the current work, the data described by Andrzejak et al. was used; which contains 5 sets (Z, O, N, F and S). In this work, three different classification problems are created from the above dataset in order to compare the performance of our method with other approaches:In the first, two sets were examined, normal (set Z) and seizure (set S).In the second, four sets of the dataset were used and they were classified into two different classes: nonseizure (sets Z, N, F) and seizure (set S).In the third, all the EEGs from the dataset were used and they were classified into two different classes: sets Z, O, N and F are included in the nonseizure class and set S in the seizure class.The EEG signal under study is firstly decomposed into five subbands through DWT (D1 ndash;D4 and A4), and each subband represents different frequency bands information. Afterwards, four statistical parameters of maximum, minimum, average and standard deviation were calculated for each subband. And then, using the optimization algorithm IPO, the best weights are calculated to apply to the OVA classifier in order to find the best hyper plane separating the two classes. The fitness function defined in the IPO algorithm, is the number of signals that have been classified incorrectly.To classify EEG signals in three problems, the 10fold CrossValidation method is used. In this method, the data is divided into 10 subsections. And then, one subset is used for test and nine others for training. This procedure is repeated 10 times, until all the data is used for testing. The proposed algorithm have been implemented 10 times for the two wavelet functions Db1 and db2. Using the proposed method, the accuracy obtained for the three problems is 100%, 98/1%, 97/34%, respectively. Also by the proposed method diagnosis of epilepsy can be achieved very quickly. The results show that the algorithm is capable of detecting signals of epileptic and nonepileptic in less than 5 milliseconds. This makes it possible to use this method in realtime systems.