0% Complete
صفحه اصلی
/
سی و دومین کنفرانس بین المللی مهندسی برق
HFO detection from iEEG signals in epilepsy using time-trained graphs and Deep Graph Convolutional Neural Network
نویسندگان :
Fatemeh Gharebaghi asl
1
Sepideh Hajipour Sardouie
2
1- دانشگاه صنعتی شریف
2- دانشگاه صنعتی شریف
کلمات کلیدی :
intracranial Electroencephalography (iEEG)،Epilepsy،(High Frequency Oscillations (HFOs،Deep Graph Convolutional Neural Network (DGCNN)
چکیده :
Intracranial electroencephalography (iEEG) is a type of brain signal widely used to study neurological diseases. Usually, the iEEG signal has frequency components of up to 80 Hz. However, recent studies have shown that, in some conditions, such as epilepsy, the brain signal contains frequency components higher than 80 Hz. These are called high-frequency oscillations (HFOs), and are considered biomarkers for epilepsy. This paper proposes a new methodology for the automated detection of HFOs based on time-domain features of signals and a deep graph convolutional neural network (DGCNN) algorithm. The proposed method was evaluated using the iEEG data of the Fedele’s group from 20 patients with medically intractable epilepsy. The method assumes that the temporal data structure is a graph structure that differs between HFO and non-HFO intervals. By treating the sequence of time samples as the nodes of a graph and training the adjacency matrix of the resulting graph using time data, different graphs are obtained for HFO and non-HFO intervals. Moreover, other features such as RMS, STE, LL, and Teager energy distinguish the intervals. Therefore, these features are considered as node features that help to increase classification accuracy. The DGCNN network is used to classify the time-trained graphs with extracted node features. The proposed methodology has the following significant advantages: 1) it achieves a higher sensitivity than the recently reported HFO detectors using the DGCNN classifier, and 2) it can automatically extract the common features of HFO events from different patients and is more robust, unlike other automated methods in the literature where the features of HFOs were manually extracted based on researchers' knowledge, which may be subject to observer bias. The proposed method achieved 90.7% sensitivity and 93.3% specificity so it has a higher sensitivity than the recently reported HFO detectors.
لیست مقالات
لیست مقالات بایگانی شده
Gray Box High-Frequency Modeling of Transformer using Particle Swarm Optimization
Mehdi Shamsodini Lori - Mohammad Hamed Samimi - Jawad Faiz
رمز نگاری داده های EEGبا کلید ترکیبی RSA-AESبرای بالا بردن امنیت و بهینه سازی مدت زمان رمزگذاری و رمز گشایی
حجت قیمت گر - پریسا قربانی
Peer-to-peer Energy Sharing Considering Prosumers' Preferences and Load Uncertainties
Mohammad Bagher Moradi - Mohammad Hasan Nazari - Seyed Hossein Hosseinian - Hamed Nafisi
An Iterative Approach to Enhance the Accuracy of TDOA-Based Localization by Averaging and Reducing Noise
Reza Bahrampour - Mohammad Hossein Madani - Hossein Bahramgiri
High-Performance Biosensor Based on SRR for Early Breast Cancer Detection
Hasti Enayattarighehkari - Sina Aramtan - Gholamreza Moradi - Farhad Azadi Namin
Multi-Octave Continuous Mode Power Amplifier with More Than 46 dBm Peak Output Power
Marzieh Chegini - Mahmoud Kamarei
Kalman Filter Fusion Based on Interactive Multiple Model for Target Tracking in Wireless Sensor Networks
Zahra Zamani - Behrouz Safarinejadian
Modeling and Analysis of Segmental Translator Permanent Magnet Linear Switched Reluctance Motor
Milad Golzarzadeh - Hashem Oraee - Babak Ganji
A Modified Suspended Carrier Transmitter for Medical Implants
Khashayar Dehghan - Omid Shoaei - Shahin Jafarabadi Ashtiani
Compare Efficiency of Modeled and Designed Tilt Bi-rotor, Quad-rotor, and Hexa-rotor in Agriculture Application
Arshia Rezaei - Amin Talaeizadeh - Aria Alasti
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 40.4.2