پیش بینی حملات صرعی در بیماران با صرع لوب تمپورال (tle) بر اساس آنالیز کپستروم و مدل ar تعمیم یافته سیگنال eeg

با توجه به اینکه تشنج‏ها موجب اختلال در هوشیاری بدون پیش آگاهی می‏شود، پیش بینی آن‏ها می‏تواند باعث کاهش فشار روانی و بهبود کیفیت زندگی شود. در این مقاله، امکان پیش بینی کوتاه مدت حمله صرع بدون حذف مصنوعات ، با زمان و دقت مناسب با استفاده از مدل ar و کپستروم بهبود یافته بررسی شده است. ابتدا سیگنال eeg با تبدیل موجک، به دلیل تفاوت فرکانس حمله ‏ها و مصنوعات هر بیمار با بیمار دیگر تفکیک می‏شود. سپس تشخیص تغییرات دوره حمله با استفاده از مدل‏سازی ar و روش کپستروم به دلیل متناوب بودن دامنه و فرکانس این دوره، انجام می‏پذیرد. در مرحله بعد با مقایسه دوره جاری با دوره پس‏زمینه و اعمال فیلتر میانه، خطای ناشی از مصنوعات (artifact) و تخلیه‏های میان حمله‏ای کاهش داده می‏شود. در نهایت سیگنال با روش پنجره پیشرو متوسط‏گیری شده و منحنی پوش بالای نمودار محاسبه می‏شود. روش پیشنهادی روی مدل پیشنهادی صرعی بزرگسال و همچنین 10 بیمار با داده‏های eeg طولانی مدت بدون حذف مصنوعات بررسی شده است. دقت و مقدار متوسط زمان پیش بینی، به ترتیب 92% ، 5/18 ثانیه بدست آمده است.

prediction of epileptic seizures in patients with temporal lobe epilepsy (tle) based on cepstrum analysis and ar model of eeg signal

epilepsy is a chronic disorder of brain function caused by abnormal and excessive electrical neurons discharge in the brain. seizures cause disturbances in consciousness that occur without prior notice, so their prediction ability, based on eeg data, can reduce stress and improve quality of life. an epileptic patient eeg data consists of five parts: ictal, inter-ictal, pre-ictal, post-ictal, and it (seconds before ictal onset). the purpose of predicting an attack is to detect the period of pre-ictal or it to create warnings for medical procedures that are actually determined hours or minutes before ictal and do not necessarily mean the exact time of ictal [4]. the aim of many studies has been to identify the pre-ictal period based on eeg data. however, the problem of reliable prediction of epileptic seizures remains largely unsolved [5]. eeg and ieeg data types are used in detection and predicting methods. due to the fact that artifacts and noises have a greater effect on eeg than ieeg, if there is ieeg, it has been tried to use it [6, 7]. seizure warning methods that have a clinical application are generally based on the use on eeg [8]. numerous studies have been performed to detect and predict seizures. the methods of signal processing and feature extraction are same in detection and prediction, but the difference is that, in detection, ictal and inter-ictal periods are compared, while in prediction, pre-ictal or it and inter-ictal periods are being compared. some algorithms use data modeling to extract features. references [13, 14], the coefficients ar model for the eeg data is obtained with least squares estimator, then the model coefficients are classified by svm binary classification. in the article [15] the non-gaussian eeg is considered using the arima model (autoregressive integrated moving average). in references [16, 17], predictions are performed based on the dynamic model with hidden variable and the sparse lvar model, respectively. also other features such as mean phase coherency [18-20], lag synchronization index to compare phase synchronization between irregular oscillations [8,21], eigenspectra of space-delay correlation and covariance matrices [22], largest lyapunov exponent [23, 25], decorrelation time, hjorth parameters such as mobility and complexity, power spectrum in frequency bands, spectral edge frequency, the four statistical moments: mean, variance, kurtosis, skewness and there are features based on entropy and probability [6, 26-29]. empirical mode decomposition (emd) and wavelet transform methods have also been used to extract the feature [2, 30, 31, 37]. in articles [32, 33], the cepstrum method has been used on short time multi channels eeg and ieeg in different patient states. cepstrum is used to extract slow and periodic changes in speech that can be used to detect the ictal period from the inter-ictal, and has also been used to linearize the eeg [34]. in the paper [33], cepstrum coefficients of multi-channel eeg are calculated and the 9 first coefficients are considered, then calculates the velocity and acceleration of the desired coefficients and uses a neural network to detect an epileptic seizure. the method of this paper was improved in 2014. in this way, first the signal energy and coefficients of cepstrum are calculated and then the same process is followed. the accuracy values ​​of velocity and acceleration coefficients in this study were 89.7% - 98.7% and 98.9% - 99.9%, respectively [32].