Ramy Hussein1, Hamid Palangi2, Rabab K Ward3, Z Jane Wang4. 1. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Digital Aviation Lab, Boeing, Vancouver, BC V6B 2X6, Canada. Electronic address: ramy@ece.ubc.ca. 2. Microsoft Research AI, Redmond, WA 98052, United States. Electronic address: hpalangi@microsoft.com. 3. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada. Electronic address: rababw@ece.ubc.ca. 4. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada. Electronic address: zjanew@ece.ubc.ca.
Abstract
OBJECTIVE: Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep neural networks in seizure detection has not been fully exploited in terms of the optimal design of the model architecture and the detection power of the time-series brain data. In this work, a deep neural network architecture is introduced to learn the temporal dependencies in Electroencephalogram (EEG) data for robust detection of epileptic seizures. METHODS: A deep Long Short-Term Memory (LSTM) network is first used to learn the high-level representations of different EEG patterns. Then, a Fully Connected (FC) layer is adopted to extract the most robust EEG features relevant to epileptic seizures. Finally, these features are supplied to a softmax layer to output predicted labels. RESULTS: The results on a benchmark clinical dataset reveal the prevalence of the proposed approach over the baseline techniques; achieving 100% classification accuracy, 100% sensitivity, and 100% specificity. Our approach is additionally shown to be robust in noisy and real-life conditions. It maintains high detection performance in the existence of common EEG artifacts (muscle activities and eye movement) as well as background noise. CONCLUSIONS: We demonstrate the clinical feasibility of our seizure detection approach achieving superior performance over the cutting-edge techniques in terms of seizure detection performance and robustness. SIGNIFICANCE: Our seizure detection approach can contribute to accurate and robust detection of epileptic seizures in ideal and real-life situations.
OBJECTIVE: Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep neural networks in seizure detection has not been fully exploited in terms of the optimal design of the model architecture and the detection power of the time-series brain data. In this work, a deep neural network architecture is introduced to learn the temporal dependencies in Electroencephalogram (EEG) data for robust detection of epileptic seizures. METHODS: A deep Long Short-Term Memory (LSTM) network is first used to learn the high-level representations of different EEG patterns. Then, a Fully Connected (FC) layer is adopted to extract the most robust EEG features relevant to epileptic seizures. Finally, these features are supplied to a softmax layer to output predicted labels. RESULTS: The results on a benchmark clinical dataset reveal the prevalence of the proposed approach over the baseline techniques; achieving 100% classification accuracy, 100% sensitivity, and 100% specificity. Our approach is additionally shown to be robust in noisy and real-life conditions. It maintains high detection performance in the existence of common EEG artifacts (muscle activities and eye movement) as well as background noise. CONCLUSIONS: We demonstrate the clinical feasibility of our seizure detection approach achieving superior performance over the cutting-edge techniques in terms of seizure detection performance and robustness. SIGNIFICANCE: Our seizure detection approach can contribute to accurate and robust detection of epileptic seizures in ideal and real-life situations.
Authors: Razieh Faghihpirayesh; Sebastian Ruf; Marianna La Rocca; Rachael Garner; Paul Vespa; Deniz Erdogmus; Dominique Duncan Journal: Annu Int Conf IEEE Eng Med Biol Soc Date: 2021-11
Authors: Md Rashed-Al-Mahfuz; Mohammad Ali Moni; Shahadat Uddin; Salem A Alyami; Matthew A Summers; Valsamma Eapen Journal: IEEE J Transl Eng Health Med Date: 2021-01-11 Impact factor: 3.316
Authors: Sani Saminu; Guizhi Xu; Zhang Shuai; Isselmou Abd El Kader; Adamu Halilu Jabire; Yusuf Kola Ahmed; Ibrahim Abdullahi Karaye; Isah Salim Ahmad Journal: Brain Sci Date: 2021-05-20