دانلود مقاله تشخیص تشنج با EEG
خلاصه
1. معرفی
2. مواد و روش ها
3. روش طبقه بندی صرع پیشنهادی
4. نتایج و بحث
5. نتیجه گیری ها
در دسترس بودن داده ها
تضاد علاقه
منابع
Abstract
1. Introduction
2. Materials and Methods
3. Proposed Epilepsy Classification Method
4. Results and Discussion
5. Conclusions
Data Availability
Conflicts of Interest
References
چکیده
تشخیص دقیق صرع به جلوگیری از عواقب جدی تشنج کمک می کند. از آنجایی که الکتروانسفالوگرام (EEG) فعالیت مغزی بیماران را به طور موثر منعکس می کند، در دهه های گذشته به طور گسترده در تشخیص صرع استفاده شده است. اخیراً روش‌های تشخیص مبتنی بر یادگیری عمیق که به طور خودکار ویژگی‌های سیگنال‌های EEG را یاد می‌گیرند توجه زیادی را به خود جلب کرده‌اند. با این حال، با روش‌های تشخیص مبتنی بر یادگیری عمیق، فرمت‌های ورودی متفاوت سیگنال‌های EEG منجر به عملکردهای تشخیص متفاوت می‌شود. در این مقاله، ما یک روش تشخیص تشنج صرعی مبتنی بر یادگیری عمیق با فرمت‌های ورودی ترکیبی سیگنال‌های EEG، یعنی EEG اصلی، تبدیل فوریه EEG، تبدیل فوریه کوتاه‌مدت EEG، و تبدیل موجک EEG پیشنهاد می‌کنیم. شبکه‌های عصبی کانولوشنال (CNN) برای استخراج ویژگی‌های پنهان از این ورودی‌ها طراحی شده‌اند. یک مکانیسم ترکیب ویژگی برای ادغام ویژگی های آموخته شده برای ایجاد یک ویژگی تلفیق کننده پایدار تر برای تشخیص تشنج اعمال می شود. نتایج تجربی نشان می دهد که روش ترکیبی پیشنهادی ما برای بهبود عملکرد تشخیص تشنج در سناریو های چند شاتی موثر است.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
The precise detection of epileptic seizure helps to prevent the serious consequences of seizures. As the electroencephalogram (EEG) reflects the brain activity of patients effectively, it has been widely used in epileptic seizure detection in the past decades. Recently, deep learning-based detection methods which automatically learn features from the EEG signals have attracted much attention. However, with deep learning-based detection methods, different input formats of EEG signals will lead to different detection performances. In this paper, we propose a deep learning-based epileptic seizure detection method with hybrid input formats of EEG signals, i.e., original EEG, Fourier transform of EEG, short-time Fourier transform of EEG, and wavelet transform of EEG. Convolutional neural networks (CNNs) are designed for extracting latent features from these inputs. A feature fusion mechanism is applied to integrate the learned features to generate a more stable syncretic feature for seizure detection. The experimental results show that our proposed hybrid method is effective to improve the seizure detection performance in few-shot scenarios.
Introduction
Approximately one percent of the world’s population, 65 million people, suffer from epilepsy, more than Parkinson’s disease, Alzheimer’s disease, and multiple sclerosis combined [1]. About two-thirds of people with epilepsy can be treated with medication, and the rest may require surgical intervention. Epilepsy has the characteristics of sudden and recurrent seizures, which may lead to falls, asphyxia, and even death. Therefore, seizure detection is very important for early warning and treatment of epilepsy.
Epileptic seizure detection is mainly based on electroencephalogram (EEG) [2–4]. Single-channel EEG acquisition equipment improves the practicability of EEG in epileptic detection due to its simplicity in implementation. However, the provided information by signal-channel EEG signal is limited because of the small number of channels. Therefore, it is worth studying to establish a model with high accuracy and high robustness for single-channel EEG epileptic detection.
The traditional methods are mainly based on feature engineering techniques which extract the corresponding features from EEG signals and then complete the detection based on the extracted features [5–9]. These features include time-domain features [10–12], frequency-domain features [8, 9], and time-frequency-domain features [13–15]. Once the features are extracted, EEG signals can be classified using a variety of classifiers. No matter what classifier is used, the quality of designed features will greatly affect the performance of epilepsy detection. In recent years, with the development of deep learning technology, many works have applied deep learning to perform epilepsy detection [16, 17]. Different from traditional feature engineering, deep learning methods automatically learn features from EEG signals and further complete detection tasks with an end-to-end manner without complicated manual feature design process and can achieve better performance than traditional methods in many scenarios.
Conclusions
In this paper, we focus on epileptic classification in few-shot scenarios. In order to make the classification accuracy higher and more stable, we propose a deep learning method with hybrid input, i.e., original EEG signal, FFT, STFT, and DWT of EEG signal. In order to alleviate the tendency of overfitting, two means are applied. The first is that we replace the traditional convolution by depthwise separable convolution for reducing the parameters in network and then 2 regularization is applied, whose function is decreasing the complexity of the model. We conduct several experiments to distinguish normal and epileptic EEG, and the results show the proposed method with hybrid input has strong advantages in epileptic classification. It benefits from the complementarity of time-domain properties, frequency-domain properties, and time-frequency-domain properties. Our proposed method provides a new perspective to enrich the input information to make improvements for deep learning-based epileptic diagnosis.