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Ming Z, Chen D, Gao T, Tang Y, Tu W, Chen J. V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges. Neural Netw 2024; 172:106136. [PMID: 38266472 DOI: 10.1016/j.neunet.2024.106136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 11/20/2023] [Accepted: 01/16/2024] [Indexed: 01/26/2024]
Abstract
Interictal epileptiform discharges (IED) as large intermittent electrophysiological events are associated with various severe brain disorders. Automated IED detection has long been a challenging task, and mainstream methods largely focus on singling out IEDs from backgrounds from the perspective of waveform, leaving normal sharp transients/artifacts with similar waveforms almost unattended. An open issue still remains to accurately detect IED events that directly reflect the abnormalities in brain electrophysiological activities, minimizing the interference from irrelevant sharp transients with similar waveforms only. This study then proposes a dual-view learning framework (namely V2IED) to detect IED events from multi-channel EEG via aggregating features from the two phases: (1) Morphological Feature Learning: directly treating the EEG as a sequence with multiple channels, a 1D-CNN (Convolutional Neural Network) is applied to explicitly learning the deep morphological features; and (2) Spatial Feature Learning: viewing the EEG as a 3D tensor embedding channel topology, a CNN captures the spatial features at each sampling point followed by an LSTM (Long Short-Term Memories) to learn the evolution of these features. Experimental results from a public EEG dataset against the state-of-the-art counterparts indicate that: (1) compared with the existing optimal models, V2IED achieves a larger area under the receiver operating characteristic (ROC) curve in detecting IEDs from normal sharp transients with a 5.25% improvement in accuracy; (2) the introduction of spatial features improves performance by 2.4% in accuracy; and (3) V2IED also performs excellently in distinguishing IEDs from background signals especially benign variants.
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Affiliation(s)
- Zhekai Ming
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Dan Chen
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China.
| | - Tengfei Gao
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Yunbo Tang
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
| | - Weiping Tu
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Jingying Chen
- National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China
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Liao J, Wang J, Zhan CA, Yang F. Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection. Phys Eng Sci Med 2024; 47:31-47. [PMID: 37747646 DOI: 10.1007/s13246-023-01340-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 09/14/2023] [Indexed: 09/26/2023]
Abstract
Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms. We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score. Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity. EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.
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Affiliation(s)
- Jiahui Liao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Chang'an A Zhan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
| | - Feng Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
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Eguchi K, Yaguchi H, Kudo I, Kimura I, Nabekura T, Kumagai R, Fujita K, Nakashiro Y, Iida Y, Hamada S, Honma S, Takei A, Moriwaka F, Yabe I. Differentiation of speech in Parkinson's disease and spinocerebellar degeneration using deep neural networks. J Neurol 2024; 271:1004-1012. [PMID: 37989963 DOI: 10.1007/s00415-023-12091-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 10/29/2023] [Accepted: 10/30/2023] [Indexed: 11/23/2023]
Abstract
INTRODUCTION Assessing dysarthria features in patients with neurodegenerative diseases helps diagnose underlying pathologies. Although deep neural network (DNN) techniques have been widely adopted in various audio processing tasks, few studies have tested whether DNNs can help differentiate neurodegenerative diseases using patients' speech data. This study evaluated whether a DNN model using a transformer architecture could differentiate patients with Parkinson's disease (PD) from patients with spinocerebellar degeneration (SCD) using speech data. METHODS Speech data were obtained from 251 and 101 patients with PD and SCD, respectively, while they read a passage. We fine-tuned a pre-trained DNN model using log-mel spectrograms generated from speech data. The DNN model was trained to predict whether the input spectrogram was generated from patients with PD or SCD. We used fivefold cross-validation to evaluate the predictive performance using the area under the receiver operating characteristic curve (AUC) and accuracy, sensitivity, and specificity. RESULTS Average ± standard deviation of the AUC, accuracy, sensitivity, and specificity of the trained model for the fivefold cross-validation were 0.93 ± 0.04, 0.87 ± 0.03, 0.83 ± 0.05, and 0.89 ± 0.05, respectively. CONCLUSION The DNN model can differentiate speech data of patients with PD from that of patients with SCD with relatively high accuracy and AUC. The proposed method can be used as a non-invasive, easy-to-perform screening method to differentiate PD from SCD using patient speech and is expected to be applied to telemedicine.
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Affiliation(s)
- Katsuki Eguchi
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan.
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan.
| | - Hiroaki Yaguchi
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan
| | - Ikue Kudo
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan
| | - Ibuki Kimura
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan
| | - Tomoko Nabekura
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan
| | - Ryuto Kumagai
- Sapporo Parkinson MS Neurological Clinic, Sapporo Kita Sky Building F12, 7-6, Kita 7-Nishi 5, Kita-ku, Sapporo, Hokkaido, 060-0807, Japan
| | - Kenichi Fujita
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan
| | - Yuichi Nakashiro
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan
| | - Yuki Iida
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan
| | - Shinsuke Hamada
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan
| | - Sanae Honma
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan
| | - Asako Takei
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan
| | - Fumio Moriwaka
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo, 063-0802, Japan
| | - Ichiro Yabe
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan
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Abdi-Sargezeh B, Shirani S, Sanei S, Took CC, Geman O, Alarcon G, Valentin A. A review of signal processing and machine learning techniques for interictal epileptiform discharge detection. Comput Biol Med 2024; 168:107782. [PMID: 38070202 DOI: 10.1016/j.compbiomed.2023.107782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 11/15/2023] [Accepted: 11/28/2023] [Indexed: 01/10/2024]
Abstract
Brain interictal epileptiform discharges (IEDs), as one of the hallmarks of epileptic brain, are transient events captured by electroencephalogram (EEG). IEDs are generated by seizure networks, and they occur between seizures (interictal periods). The development of a robust method for IED detection could be highly informative for clinical treatment procedures and epileptic patient management. Since 1972, different machine learning techniques, from template matching to deep learning, have been developed to automatically detect IEDs from scalp EEG (scEEG) and intracranial EEG (iEEG). While the scEEG signals suffer from low information details and high attenuation of IEDs due to the high skull electrical impedance, the iEEG signals recorded using implanted electrodes enjoy higher details and are more suitable for identifying the IEDs. In this review paper, we group IED detection techniques into six categories: (1) template matching, (2) feature representation (mimetic, time-frequency, and nonlinear features), (3) matrix decomposition, (4) tensor factorization, (5) neural networks, and (6) estimation of the iEEG from the concurrent scEEG followed by detection and classification. The methods are compared quantitatively (e.g., in terms of accuracy, sensitivity, and specificity), and their general advantages and limitations are described. Finally, current limitations and possible future research paths related to this field are mentioned.
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Affiliation(s)
- Bahman Abdi-Sargezeh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; School of Science and Technology, Nottingham Trent University, Nottingham, UK.
| | - Sepehr Shirani
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Clive Cheong Took
- Department of Electronic Engineering, Royal Holloway, University of London, London, UK
| | - Oana Geman
- Computer, Electronics and Automation Department, University Stefan cel Mare, Suceava, Romania
| | - Gonzalo Alarcon
- Department of Clinical Neurophysiology, Royal Manchester Children's Hospital, Manchester, UK
| | - Antonio Valentin
- Department of Clinical Neuroscience, King's College London, London, UK
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Bender AC, Jaleel A, Pellerin KR, Moguilner S, Sarkis RA, Cash SS, Lam AD. Altered Sleep Microarchitecture and Cognitive Impairment in Patients With Temporal Lobe Epilepsy. Neurology 2023; 101:e2376-e2387. [PMID: 37848332 PMCID: PMC10752648 DOI: 10.1212/wnl.0000000000207942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/28/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVES To investigate the spatiotemporal characteristics of sleep waveforms in temporal lobe epilepsy (TLE) and examine their association with cognition. METHODS In this retrospective, cross-sectional study, we examined overnight EEG data from adult patients with TLE and nonepilepsy comparisons (NECs) admitted to the epilepsy monitoring unit at Mass General Brigham hospitals. Automated algorithms were used to characterize sleep macroarchitecture (sleep stages) and microarchitecture (spindles, slow oscillations [SOs]) on scalp EEG and to detect hippocampal interictal epileptiform discharges (hIEDs) from foramen ovale electrodes simultaneously recorded in a subset of patients with TLE. We examined the association of sleep features and hIEDs with memory and executive function from clinical neuropsychological evaluations. RESULTS A total of 81 adult patients with TLE and 28 NEC adult patients were included with similar mean ages. There were no significant differences in sleep macroarchitecture between groups, including relative time spent in each sleep stage, sleep efficiency, and sleep fragmentation. By contrast, the spatiotemporal characteristics of sleep microarchitecture were altered in TLE compared with NEC and were associated with cognitive impairments. Specifically, we observed a ∼30% reduction in spindle density in patients with TLE compared with NEC, which was significantly associated with worse memory performance. Spindle-SO coupling strength was also reduced in TLE and, in contrast to spindles, was associated with diminished executive function. We found no significant association between sleep macroarchitectural and microarchitectural parameters and hIEDs. DISCUSSION There is a fundamental alteration of sleep microarchitecture in TLE, characterized by a reduction in spindle density and spindle-SO coupling, and these changes may contribute to neurocognitive comorbidity in this disorder.
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Affiliation(s)
- Alex C Bender
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA.
| | - Afareen Jaleel
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
| | - Kyle R Pellerin
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
| | - Sebastian Moguilner
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
| | - Rani A Sarkis
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
| | - Sydney S Cash
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
| | - Alice D Lam
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
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Abdallah T, Jrad N, Abdallah F, Humeau-Heurtier A, Van Bogaert P. A self-attention model for cross-subject seizure detection. Comput Biol Med 2023; 165:107427. [PMID: 37683531 DOI: 10.1016/j.compbiomed.2023.107427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/03/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023]
Abstract
Epilepsy is a neurological disorder characterized by recurring seizures, detected by electroencephalography (EEG). EEG signals can be detected by manual time-consuming analysis and recently by automatic detection. The latter poses a significant challenge due to the high dimensional and non-stationary nature of EEG signals. Recently, deep learning (DL) techniques have emerged as valuable tools for seizure detection. In this study, a novel data-driven model based on DL, incorporating a self-attention mechanism (SAT), is proposed. One notable advantage of the proposed method is its simplicity in application, as the raw signal data is directly fed into the suggested network without requiring expertise in signal processing. The model leverages a one-dimensional convolutional neural network (CNN) to extract relevant features from EEG signals. These features are then passed through a long short-term memory (LSTM) module to benefit from its memory capabilities, along with a SAT mechanism. The key contribution of this paper lies in the addition of the SAT layer to the LSTM encoder, enabling enhanced exploration of the latent mapping during the encoding step. Cross-subject experiments revealed good performance of this approach with F1-score of 97.8% and 92.7% for binary and five-class epileptic seizure recognition tasks, respectively, on the public UCI dataset, and 97.9% on the CHB-MIT database, surpassing state-of-the-art DL performance. Besides, the proposed method exhibits robustness to inter-subject variability.
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Affiliation(s)
- Tala Abdallah
- Univ Angers, LARIS, SFR MATHSTIC, F-49000 Angers, 62 avenue Notre-Dame du Lac, France.
| | - Nisrine Jrad
- Univ Angers, LARIS, SFR MATHSTIC, F-49000 Angers, 62 avenue Notre-Dame du Lac, France; University of Catholique de l'Ouest, Angers-Nantes, 49000, France
| | | | - Anne Humeau-Heurtier
- Univ Angers, LARIS, SFR MATHSTIC, F-49000 Angers, 62 avenue Notre-Dame du Lac, France
| | - Patrick Van Bogaert
- Univ Angers, LARIS, SFR MATHSTIC, F-49000 Angers, 62 avenue Notre-Dame du Lac, France; The Department of Pediatric Neurology, CHU, Angers, 49000, France
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Lih OS, Jahmunah V, Palmer EE, Barua PD, Dogan S, Tuncer T, García S, Molinari F, Acharya UR. EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population. Comput Biol Med 2023; 164:107312. [PMID: 37597408 DOI: 10.1016/j.compbiomed.2023.107312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database. METHOD The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient's embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model. RESULTS Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively. CONCLUSION The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient's embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide.
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Affiliation(s)
- Oh Shu Lih
- Cogninet Australia, Sydney, NSW, 2010, Australia
| | - V Jahmunah
- School of Engineering, Nanyang Polytechnic, Singapore
| | - Elizabeth Emma Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, 2031, Australia; School of Women's and Children's Health, University of New South Wales, Randwick, 2031, Australia
| | - Prabal D Barua
- School of Business (Information System), University of Southern Queensland, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Salvador García
- Andalusian Institute of Data Science and Computational Intelligence, Department of Computer Science and Artificial Intelligence, University of Granada, Spain
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia.
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Chan HL, Ouyang Y, Huang PJ, Li HT, Chang CW, Chang BL, Hsu WY, Wu T. Deep neural networks for the detection of temporal-lobe epileptiform discharges from scalp electroencephalograms. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Chung YG, Lee WJ, Na SM, Kim H, Hwang H, Yun CH, Kim KJ. Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms. Sci Rep 2023; 13:6755. [PMID: 37185941 PMCID: PMC10130023 DOI: 10.1038/s41598-023-33906-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 04/20/2023] [Indexed: 05/17/2023] Open
Abstract
Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalography (EEG) recordings of the frontal, temporal, and occipital scalp regions. This study included 38 patients with frontal (n = 15), temporal (n = 13), and occipital (n = 10) IEDs and 232 controls without IEDs from a single tertiary center. All the EEG recordings were segmented into 1.5-s epochs and fed into 1- or 2-dimensional convolutional neural networks to construct binary classification models to detect IEDs in each focal region and multiclass classification models to categorize IEDs into frontal, temporal, and occipital regions. The binary classification models exhibited accuracies of 79.3-86.4%, 93.3-94.2%, and 95.5-97.2% for frontal, temporal, and occipital IEDs, respectively. The three- and four-class models exhibited accuracies of 87.0-88.7% and 74.6-74.9%, respectively, with temporal, occipital, and non-IEDs F1-scores of 89.9-92.3%, 84.9-90.6%, and 84.3-86.0%; and 86.6-86.7%, 86.8-87.2%, and 67.8-69.2% for the three- and four-class (frontal, 50.3-58.2%) models, respectively. The deep learning-based models could help enhance EEG interpretation. Although they performed well, the resolution of region-specific focal IED misinterpretations and further model improvement are needed.
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Affiliation(s)
- Yoon Gi Chung
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
| | - Woo-Jin Lee
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
| | - Sung Min Na
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
| | - Hunmin Kim
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea.
| | - Hee Hwang
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
- Kakao Healthcare, Seongnam-si, Republic of Korea
| | - Chang-Ho Yun
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
| | - Ki Joong Kim
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
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Jiang L, Fan Q, Ren J, Dong F, Jiang T, Liu J. An improved BECT spike detection method with functional brain network features based on PLV. Front Neurosci 2023; 17:1150668. [PMID: 37008227 PMCID: PMC10060895 DOI: 10.3389/fnins.2023.1150668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
BackgroundChildren with benign childhood epilepsy with centro-temporal spikes (BECT) have spikes, sharps, and composite waves on their electroencephalogram (EEG). It is necessary to detect spikes to diagnose BECT clinically. The template matching method can identify spikes effectively. However, due to the individual specificity, finding representative templates to detect spikes in actual applications is often challenging.PurposeThis paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning.MethodsTo obtain high detection effect, this method uses a specific template matching method and the ‘peak-to-peak' phenomenon of montages to obtain a set of candidate spikes. With the set of candidate spikes, functional brain networks (FBN) are constructed based on phase locking value (PLV) to extract the features of the network structure during spike discharge with phase synchronization. Finally, the time domain features of the candidate spikes and the structural features of the FBN-PLV are input into the artificial neural network (ANN) to identify the spikes.ResultsBased on FBN-PLV and ANN, the EEG data sets of four BECT cases from the Children's Hospital, Zhejiang University School of Medicine are tested with the AC of 97.6%, SE of 98.3%, and SP 96.8%.
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Affiliation(s)
- Lurong Jiang
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China
| | - Qikai Fan
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China
| | - Juntao Ren
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China
| | - Fang Dong
- College of Information and Electric Engineering, Zhejiang University City College, Hangzhou, China
| | - Tiejia Jiang
- Department of Neurology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Junbiao Liu
- Digital Culture Innovation Research Institute, Zhejiang University City College, Hangzhou, China
- *Correspondence: Junbiao Liu
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11
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Gireesh ED, Skinner H, Seo J, Ching P, Hyeong LK, Baumgartner J, Gurupur V. Deep neural networks and gradient-weighted class activation mapping to classify and analyze EEG. INTELLIGENT DECISION TECHNOLOGIES 2023. [DOI: 10.3233/idt-228040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Deep Neural Networks (DNN) have significantly improved the capabilities for analysis and classification of data, including that of biomedical signals (eg. ElectroencephalogramEEG). Optimal classification of EEG signals from seizure onset zones has been challenging especially given the complexity of signals arising from multiple locations. Also, underlying electrophysiological abnormalities which signify epileptogenic zones have not been clearly defined. Previous studies have demonstrated, automatic feature generation based on deep learning as a useful tool for interictal epileptiform discharge (IEDs) detection. Also signals with transformations have been used in convolutional neural network (CNN) based models in the past for classifying EEG data. We explored the use of deep learning for identification of the seizure onset zones using regular dense neural network and CNN based models. After the training the model using sample data the results were validated with a smaller percentage (10%) of the data. The models were noted to be accurate in predicting the seizure onset zones with significant degree of accuracy (87–99%) with a much shorter duration of signal recorded, compared to previous studies. We further investigated model’s decision-making process with heatmapping (gradient-weighted class activation map: Grad-CAM) approach, combining with signal processing using Hilbert transform. To identify the visible features in the signal, which maximally contribute to the seizure onset zone prediction, correlation of heatmap and analytical signal of the EEG, was calculated. A high correlation between heatmap and analytical signal was noted, suggesting that the model may be utilizing the higher power regions of the data in decision making process. This study demonstrates the potential use of DNN based strategies in identifying the epileptogenic zones in the intracranial EEG. It also shows the heatmapping strategies can help in establishing how specific signal patterns may be contributing to the decision making of DNN.
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12
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Saccà V, Novellino F, Salsone M, Abou Jaoude M, Quattrone A, Chiriaco C, Madrigal JLM, Quattrone A. Challenging functional connectivity data: machine learning application on essential tremor recognition. Neurol Sci 2023; 44:199-207. [PMID: 36123559 DOI: 10.1007/s10072-022-06400-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 01/10/2023]
Abstract
BACKGROUND AND AIMS This paper aimed to investigate the usefulness of applying machine learning on resting-state fMRI connectivity data to recognize the pattern of functional changes in essential tremor (ET), a disease characterized by slight brain abnormalities, often difficult to detect using univariate analysis. METHODS We trained a support vector machine with a radial kernel on the mean signals extracted by 14 brain networks obtained from resting-state fMRI scans of 18 ET and 19 healthy control (CTRL) subjects. Classification performance between pathological and control subjects was evaluated using a tenfold cross-validation. Recursive feature elimination was performed to rank the importance of the extracted features. Moreover, univariate analysis using Mann-Whitney U test was also performed. RESULTS The machine learning algorithm achieved an AUC of 0.75, with four networks (language, primary visual, cerebellum, and attention), which have an essential role in ET pathophysiology, being selected as the most important features for classification. By contrast, the univariate analysis was not able to find significant results among these two conditions. CONCLUSION The machine learning approach identifies the changes in functional connectivity of ET patients, representing a promising instrument to discriminate specific pathological conditions and find novel functional biomarkers in resting-state fMRI studies.
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Affiliation(s)
- Valeria Saccà
- Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy.,Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Fabiana Novellino
- Department of Pharmacology and Toxicology, School of Medicine, Universidad Complutense de Madrid (UCM), Av. Complutense s/n, 28040, Madrid, Spain. .,Instituto de Investigación Neuroquímica (IUINQ-UCM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Investigación Sanitaria Hospital, 12 de Octubre (Imas12), Madrid, Spain. .,Institute of Bioimaging and Molecular Physiology (IBFM), National Research Council, Catanzaro, Italy.
| | - Maria Salsone
- Institute of Molecular Bioimaging and Physiology, National Research Council, Milan, Italy.,Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | | | - Andrea Quattrone
- Institute of Neurology, University Magna Graecia, Catanzaro, Italy.,Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - José L M Madrigal
- Department of Pharmacology and Toxicology, School of Medicine, Universidad Complutense de Madrid (UCM), Av. Complutense s/n, 28040, Madrid, Spain.,Instituto de Investigación Neuroquímica (IUINQ-UCM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Investigación Sanitaria Hospital, 12 de Octubre (Imas12), Madrid, Spain
| | - Aldo Quattrone
- Institute of Bioimaging and Molecular Physiology (IBFM), National Research Council, Catanzaro, Italy. .,Neuroscience Research Center, Magna Graecia University, Catanzaro, Italy.
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13
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Huang Z, Ma Y, Wang R, Yuan B, Jiang R, Yang Q, Li W, Sun J. DSCNN-LSTMs: A Lightweight and Efficient Model for Epilepsy Recognition. Brain Sci 2022; 12:brainsci12121672. [PMID: 36552132 PMCID: PMC9775067 DOI: 10.3390/brainsci12121672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Epilepsy is the second most common disease of the nervous system. Because of its high disability rate and the long course of the disease, it is a worldwide medical problem and social public health problem. Therefore, the timely detection and treatment of epilepsy are very important. Currently, medical professionals use their own diagnostic experience to identify seizures by visual inspection of the electroencephalogram (EEG). Not only does it require a lot of time and effort, but the process is also very cumbersome. Machine learning-based methods have recently been proposed for epilepsy detection, which can help clinicians make rapid and correct diagnoses. However, these methods often require extracting the features of EEG signals before using the data. In addition, the selection of features often requires domain knowledge, and feature types also have a significant impact on the performance of the classifier. In this paper, a one-dimensional depthwise separable convolutional neural network and long short-term memory networks (1D DSCNN-LSTMs) model is proposed to identify epileptic seizures by autonomously extracting the features of raw EEG. On the UCI dataset, the performance of the proposed 1D DSCNN-LSTMs model is verified by cross-validation and time complexity comparison. Compared with other previous models, the experimental results show that the highest recognition rates of binary and quintuple classification are 99.57% and 81.30%, respectively. It can be concluded that the 1D DSCNN-LSTMs model proposed in this paper is an effective method to identify seizures based on EEG signals.
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14
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Hirano R, Emura T, Nakata O, Nakashima T, Asai M, Kagitani-Shimono K, Kishima H, Hirata M. Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2879-2890. [PMID: 35536808 DOI: 10.1109/tmi.2022.3173743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysiologists and are time-consuming. Another problem is that spike identification from MEG waveforms largely depends on neurophysiologists' skills and experiences. These problems cause poor cost-effectiveness in clinical MEG examination. To overcome these problems, we fully automated spike identification and ECD estimation using a deep learning approach fully automated AI-based MEG interictal epileptiform discharge identification and ECD estimation (FAMED). We applied a semantic segmentation method, which is an image processing technique, to identify the appropriate times between spike onset and peak and to select appropriate sensors for ECD estimation. FAMED was trained and evaluated using clinical MEG data acquired from 375 patients. FAMED training was performed in two stages: in the first stage, a classification network was learned, and in the second stage, a segmentation network that extended the classification network was learned. The classification network had a mean AUC of 0.9868 (10-fold patient-wise cross-validation); the sensitivity and specificity were 0.7952 and 0.9971, respectively. The median distance between the ECDs estimated by the neurophysiologists and those using FAMED was 0.63 cm. Thus, the performance of FAMED is comparable to that of neurophysiologists, and it can contribute to the efficiency and consistency of MEG ECD analysis.
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15
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Shoeibi A, Moridian P, Khodatars M, Ghassemi N, Jafari M, Alizadehsani R, Kong Y, Gorriz JM, Ramírez J, Khosravi A, Nahavandi S, Acharya UR. An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 149:106053. [DOI: 10.1016/j.compbiomed.2022.106053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 02/01/2023]
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16
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Ray J, Wijesekera L, Cirstea S. Machine learning and clinical neurophysiology. J Neurol 2022; 269:6678-6684. [PMID: 35907045 DOI: 10.1007/s00415-022-11283-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/05/2022] [Accepted: 07/09/2022] [Indexed: 11/29/2022]
Abstract
Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights. In this update article, we will provide a brief overview where such technology is being applied in clinical neurophysiology and possible future directions.
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Affiliation(s)
- Julian Ray
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK.
| | - Lokesh Wijesekera
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
| | - Silvia Cirstea
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
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17
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Mirchi N, Warsi NM, Zhang F, Wong SM, Suresh H, Mithani K, Erdman L, Ibrahim GM. Decoding Intracranial EEG With Machine Learning: A Systematic Review. Front Hum Neurosci 2022; 16:913777. [PMID: 35832872 PMCID: PMC9271576 DOI: 10.3389/fnhum.2022.913777] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving healthy brain function and which fails in disease states. Machine learning (ML), a form of artificial intelligence, is a modern tool that may be able to better decode complex neural signals and enhance interpretation of these data. To date, a number of publications have applied ML to iEEG, but clinician awareness of these techniques and their relevance to neurosurgery, has been limited. The present work presents a review of existing applications of ML techniques in iEEG data, discusses the relative merits and limitations of the various approaches, and examines potential avenues for clinical translation in neurosurgery. One-hundred-seven articles examining artificial intelligence applications to iEEG were identified from 3 databases. Clinical applications of ML from these articles were categorized into 4 domains: i) seizure analysis, ii) motor tasks, iii) cognitive assessment, and iv) sleep staging. The review revealed that supervised algorithms were most commonly used across studies and often leveraged publicly available timeseries datasets. We conclude with recommendations for future work and potential clinical applications.
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Affiliation(s)
- Nykan Mirchi
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nebras M. Warsi
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Frederick Zhang
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Simeon M. Wong
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Hrishikesh Suresh
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Karim Mithani
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Lauren Erdman
- Vector Institute for Artificial Intelligence, MaRS Centre, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Hospital for Sick Children, Toronto, ON, Canada
| | - George M. Ibrahim
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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18
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Abou Jaoude M, Jacobs CS, Sarkis RA, Jing J, Pellerin KR, Cole AJ, Cash SS, Westover MB, Lam AD. Noninvasive Detection of Hippocampal Epileptiform Activity on Scalp Electroencephalogram. JAMA Neurol 2022; 79:614-622. [PMID: 35499837 PMCID: PMC9062772 DOI: 10.1001/jamaneurol.2022.0888] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 03/09/2022] [Indexed: 01/18/2023]
Abstract
Importance The hippocampus is a highly epileptogenic brain region, yet over 90% of hippocampal epileptiform activity (HEA) cannot be identified on scalp electroencephalogram (EEG) by human experts. Currently, detection of HEA requires intracranial electrodes, which limits our understanding of the role of HEA in brain diseases. Objective To develop and validate a machine learning algorithm that accurately detects HEA from a standard scalp EEG, without the need for intracranial electrodes. Design, Setting, and Participants In this diagnostic study, conducted from 2008 to 2021, EEG data were used from patients with temporal lobe epilepsy (TLE) and healthy controls (HCs) to train and validate a deep neural network, HEAnet, to detect HEA on scalp EEG. Participants were evaluated at tertiary-level epilepsy centers at 2 academic hospitals: Massachusetts General Hospital (MGH) or Brigham and Women's Hospital (BWH). Included in the study were patients aged 12 to 78 years with a clinical diagnosis of TLE and HCs without epilepsy. Patients with TLE and HCs with a history of intracranial surgery were excluded from the study. Exposures Simultaneous intracranial EEG and/or scalp EEG. Main Outcomes and Measures Performance was assessed using cross-validated areas under the receiver operating characteristic curve (AUC ROC) and precision-recall curve (AUC PR) and additional clinically relevant metrics. Results HEAnet was trained and validated using data sets that were derived from a convenience sample of 141 eligible participants (97 with TLE and 44 HCs without epilepsy) whose retrospective EEG data were readily available. Data set 1 included the simultaneous scalp EEG and intracranial electrode recordings of 51 patients with TLE (mean [SD] age, 40.7 [15.9] years; 30 men [59%]) at MGH. An automatically generated training data set with 972 095 positive HEA examples was created, in addition to a held-out expert-annotated testing data set with 22 762 positive HEA examples. HEAnet's performance was validated on 2 independent scalp EEG data sets: (1) data set 2 (at MGH; 24 patients with TLE and 20 HCs; mean [SD] age, 42.3 [16.2] years; 17 men [39%]) and (2) data set 3 (at BWH; 22 patients with TLE and 24 HCs; mean [SD] age, 43.0 [14.4] years; 20 men [43%]). For single-event detection of HEA on data set 1, HEAnet achieved a mean (SD) AUC ROC of 0.89 (0.01) and a mean (SD) AUC PR of 0.39 (0.03). On external validation with data sets 2 and 3, HEAnet accurately distinguished TLE from HC (AUC ROC of 0.88 and 0.95, respectively) and predicted epilepsy lateralization with 100% and 92% accuracy, respectively. HEAnet tracked dynamic changes in HEA in response to seizure medication adjustments and performed comparably with human experts in diagnosing TLE from 1-hour scalp EEG recordings, diagnosing TLE in several individuals that experts missed. Without reducing specificity, addition of HEAnet to human expert EEG review increased sensitivity for diagnosing TLE in humans from 50% to 58% to 63% to 67%. Conclusions and Relevance Results of this diagnostic study suggest that HEAnet provides a novel, noninvasive, quantitative, and clinically relevant biomarker of hippocampal hyperexcitability in humans.
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Affiliation(s)
| | - Claire S. Jacobs
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Rani A. Sarkis
- Harvard Medical School, Boston, Massachusetts
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston
| | | | - Andrew J. Cole
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Alice D. Lam
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
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19
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Chung YG, Jeon Y, Yoo S, Kim H, Hwang H. Big data analysis and artificial intelligence in epilepsy - common data model analysis and machine learning-based seizure detection and forecasting. Clin Exp Pediatr 2022; 65:272-282. [PMID: 34844397 PMCID: PMC9171464 DOI: 10.3345/cep.2021.00766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 10/27/2021] [Indexed: 11/27/2022] Open
Abstract
There has been significant interest in big data analysis and artificial intelligence (AI) in medicine. Ever-increasing medical data and advanced computing power have enabled the number of big data analyses and AI studies to increase rapidly. Here we briefly introduce epilepsy, big data, and AI and review big data analysis using a common data model. Studies in which AI has been actively applied, such as those of electroencephalography epileptiform discharge detection, seizure detection, and forecasting, will be reviewed. We will also provide practical suggestions for pediatricians to understand and interpret big data analysis and AI research and work together with technical expertise.
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Affiliation(s)
- Yoon Gi Chung
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea
| | | | - Sooyoung Yoo
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Hunmin Kim
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea.,Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea
| | - Hee Hwang
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea.,Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea
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20
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Engineering nonlinear epileptic biomarkers using deep learning and Benford's law. Sci Rep 2022; 12:5397. [PMID: 35354911 PMCID: PMC8967852 DOI: 10.1038/s41598-022-09429-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 03/22/2022] [Indexed: 12/15/2022] Open
Abstract
In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their frequency responses to 16 widely used engineered metrics to interpret their properties: epileptogenicity index (EI), phase locked high gamma (PLHG), time and frequency domain Cho Gaines distance (TDCG, FDCG), relative band powers, and log absolute band powers (from alpha, beta, theta, delta, low gamma, and high gamma bands). The deep learning models were pretrained for seizure identification on the time and frequency domains of 1 s, single-channel clips of 127 seizures (from 25 different subjects) using "leave-one-out" (LOO) cross validation. Each neural network extracted unique feature spaces that were interpreted using spectral power modulations before being used to train a Random Forest Classifier (RFC) for seizure identification. The Gini Importance of each feature was calculated from the pretrained RFC, enabling the most significant features (MSFs) for each task to be identified. The MSFs were extracted to train another RFC for UPenn and Mayo Clinic's Seizure Detection Kaggle Challenge. They obtained an AUC score of 0.93, demonstrating a transferable method to identify and interpret biomarkers for seizure detection.
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21
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Cheng C, Zhou Y, You B, Liu Y, Fei G, Yang L, Dai Y. Multiview Feature Fusion Representation for Interictal Epileptiform Spikes Detection. Int J Neural Syst 2022; 32:2250014. [PMID: 35272587 DOI: 10.1142/s0129065722500149] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Interictal epileptiform spikes (IES) of scalp electroencephalogram (EEG) signals have a strong relation with the epileptogenic region. Since IES are highly unlikely to be detected in scalp EEG signals, the primary diagnosis depends heavily on the visual evaluation of IES. However, visual inspection of EEG signals, the standard IES detection procedure is time-consuming, highly subjective, and error-prone. Furthermore, the highly complex, nonlinear, and nonstationary characteristics of EEG signals lead to the incomplete representation of EEG signals in existing computer-aided methods and consequently unsatisfactory detection performance. Therefore, a novel multiview feature fusion representation (MVFFR) method was developed and combined with a robustness classifier to detect EEG signals with/without IES. MVFFR comprises two steps: First, temporal, frequency, temporal-frequency, spatial, and nonlinear domain features are transformed by the IES to express the latent information effectively. Second, the unsupervised infinite feature-selection method determines the most distinct feature fusion representations. Experimental results using a balanced dataset of six patients showed that MVFFR achieved the optimal detection performance (accuracy: 89.27%, sensitivity: 89.01%, specificity: 89.54%, and precision: 89.82%) compared with other feature ranking methods, and the MVFFR-related method were complementary and indispensable. Additionally, in an independent test, MVFFR maintained excellent generalization capacity with a false detection rate per minute of 0.15 on the unbalanced dataset of one patient.
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Affiliation(s)
- Chenchen Cheng
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, P. R. China.,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.,Heilongjiang Provincial Key Laboratory, of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, P. R. China
| | - Yuanfeng Zhou
- Department of Neurology, Children's Hospital of Fudan University, Shanghai 200000, P. R. China
| | - Bo You
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, P. R. China.,Heilongjiang Provincial Key Laboratory, of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, P. R. China.,School of Automation, Harbin University of Science and Technology, Harbin 150080, P. R. China
| | - Yan Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.,Jinan Guoke Medical Engineering Technology Development Co., Ltd, Jinan 250000, P. R. China
| | - Gao Fei
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University Jinan, P. R. China
| | - Liling Yang
- Department of Neurology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, Jinan 250021, P. R. China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.,Jinan Guoke Medical Engineering Technology Development Co., Ltd, Jinan 250000, P. R. China
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22
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Hoogteijling S, Zijlmans M. Deep learning for epileptogenic zone delineation from the invasive EEG: challenges and lookouts. Brain Commun 2021; 4:fcab307. [PMID: 35169704 PMCID: PMC8833315 DOI: 10.1093/braincomms/fcab307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 11/26/2021] [Accepted: 12/24/2021] [Indexed: 11/24/2022] Open
Affiliation(s)
- Sem Hoogteijling
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
- Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Maeike Zijlmans
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
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Abdi-Sargezeh B, Valentin A, Alarcon G, Martin-Lopez D, Sanei S. Higher-order tensor decomposition based scalp-to-intracranial EEG projection for detection of interictal epileptiform discharges. J Neural Eng 2021; 18. [PMID: 34818640 DOI: 10.1088/1741-2552/ac3cc4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/24/2021] [Indexed: 11/12/2022]
Abstract
Objective.Interictal epileptiform discharges (IEDs) occur between two seizures onsets. IEDs are mainly captured by intracranial recordings and are often invisible over the scalp. This study proposes a model based on tensor factorization to map the time-frequency (TF) features of scalp EEG (sEEG) to the TF features of intracranial EEG (iEEG) in order to detect IEDs from over the scalp with high sensitivity.Approach.Continuous wavelet transform is employed to extract the TF features. Time, frequency, and channel modes of IED segments from iEEG recordings are concatenated into a four-way tensor. Tucker and CANDECOMP/PARAFAC decomposition techniques are employed to decompose the tensor into temporal, spectral, spatial, and segmental factors. Finally, TF features of both IED and non-IED segments from scalp recordings are projected onto the temporal components for classification.Main results.The model performance is obtained in two different approaches: within- and between-subject classification approaches. Our proposed method is compared with four other methods, namely a tensor-based spatial component analysis method, TF-based method, linear regression mapping model, and asymmetric-symmetric autoencoder mapping model followed by convolutional neural networks. Our proposed method outperforms all these methods in both within- and between-subject classification approaches by respectively achieving 84.2% and 72.6% accuracy values.Significance.The findings show that mapping sEEG to iEEG improves the performance of the scalp-based IED detection model. Furthermore, the tensor-based mapping model outperforms the autoencoder- and regression-based mapping models.
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Affiliation(s)
- Bahman Abdi-Sargezeh
- School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
| | - Antonio Valentin
- Department of Clinical Neuroscience, King's College London, London, United Kingdom
| | - Gonzalo Alarcon
- Department of Neurology, Hamad General Hospital, Doha, Qatar
| | | | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
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24
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Shoji T, Yoshida N, Tanaka T. Automated detection of abnormalities from an EEG recording of epilepsy patients with a compact convolutional neural network. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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25
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He H, Liu X, Hao Y. A progressive deep wavelet cascade classification model for epilepsy detection. Artif Intell Med 2021; 118:102117. [PMID: 34412840 DOI: 10.1016/j.artmed.2021.102117] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 04/11/2021] [Accepted: 05/21/2021] [Indexed: 12/27/2022]
Abstract
Automatic epileptic seizure detection according to EEG recordings is helpful for neurologists to identify an epilepsy occurrence in the initial anti-epileptic treatment. To quickly and accurately detect epilepsy, we proposed a progressive deep wavelet cascade classification model (PDWC) based on the discrete wavelet transform (DWT) and Random Forest (RF). Different from current deep networks, the PDWC mimics the progressive object identification process of human beings with recognition cycles. In every cycle, enhanced wavelet energy features at a specific scale were extracted by DWT and input into a set of cascade RF classifiers to realize one recognition. The recognition accuracy of PDWC is gradually improved by the fusion of classification results produced by multiple recognition cycles. Moreover, the cascade structure of PDWC can be automatically determined by the classification accuracy increment between layers. To verify the performance of the PDWC, we respectively applied five traditional schemes and four deep learning schemes to four public datasets. The results show that the PDWC is not only superior than five traditional schemes, including KNN, Bayes, DT, SVM, and RF, but also better than deep learning methods, i.e. convolutional neural network (CNN), Long Short-Term Memory (LSTM), multi-Grained Cascade Forest (gcForest) and wavelet cascade model (WCM). The mean accuracy of PDWC for all subjects of all datasets reaches to 0.9914. With a flexible structure and less parameters, the PDWC is more suitable for the epilepsy detection of diverse EEG signals.
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Affiliation(s)
- Hong He
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Xinyue Liu
- College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China
| | - Yong Hao
- Department of Neurology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
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26
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Fukumori K, Yoshida N, Sugano H, Nakajima M, Tanaka T. Epileptic Spike Detection Using Neural Networks with Linear-Phase Convolutions. IEEE J Biomed Health Inform 2021; 26:1045-1056. [PMID: 34357874 DOI: 10.1109/jbhi.2021.3102247] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
To cope with the lack of highly skilled professionals, machine learning with proper signal processing is key for establishing automated diagnostic-aid technologies with which to conduct epileptic electroencephalogram (EEG) testing. In particular, frequency filtering with the appropriate passbands is essential for enhancing the biomarkerssuch as epileptic spike wavesthat are noted in the EEG. This paper introduces a novel class of neural networks (NNs) that have a bank of linear-phase finite impulse response filters at the first layer as a preprocessor that can behave as bandpass filters that extract biomarkers without destroying waveforms because of a linear-phase condition. Besides, the parameters of the filters are also data-driven. The proposed NNs were trained with a large amount of clinical EEG data, including 15,833 epileptic spike waveforms recorded from 50 patients, and their labels were annotated by specialists. In the experiments, we compared three scenarios for the first layer: no preprocessing, discrete wavelet transform, and the proposed data-driven filters. The experimental results show that the trained data-driven filter bank with supervised learning behaves like multiple bandpass filters. In particular, the trained filter passed a frequency band of approximately 1030 Hz. Moreover, the proposed method detected epileptic spikes, with the area under the receiver operating characteristic curve of 0.967 in the mean of 50 intersubject validations.
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27
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Shoeibi A, Khodatars M, Ghassemi N, Jafari M, Moridian P, Alizadehsani R, Panahiazar M, Khozeimeh F, Zare A, Hosseini-Nejad H, Khosravi A, Atiya AF, Aminshahidi D, Hussain S, Rouhani M, Nahavandi S, Acharya UR. Epileptic Seizures Detection Using Deep Learning Techniques: A Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5780. [PMID: 34072232 PMCID: PMC8199071 DOI: 10.3390/ijerph18115780] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/14/2021] [Accepted: 05/15/2021] [Indexed: 02/06/2023]
Abstract
A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.
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Affiliation(s)
- Afshin Shoeibi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | | | - Navid Ghassemi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan 3513119111, Iran;
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, School of Medicine, University of California, San Francisco, CA 94143, USA;
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran;
| | - Hossein Hosseini-Nejad
- Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran 1631714191, Iran;
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Amir F. Atiya
- Department of Computer Engineering, Faculty of Engineering, Cairo University, Cairo 12613, Egypt;
| | - Diba Aminshahidi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Sadiq Hussain
- System Administrator at Dibrugarh University, Assam 786004, India;
| | - Modjtaba Rouhani
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Udyavara Rajendra Acharya
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Bioinformatics and Medical Engineering, Taichung City 41354, Taiwan
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28
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Lun X, Yu Z, Chen T, Wang F, Hou Y. A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals. Front Hum Neurosci 2020; 14:338. [PMID: 33100985 PMCID: PMC7522466 DOI: 10.3389/fnhum.2020.00338] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 07/31/2020] [Indexed: 11/13/2022] Open
Abstract
A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.
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Affiliation(s)
- Xiangmin Lun
- College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun, China.,School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Zhenglin Yu
- College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun, China
| | - Tao Chen
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Fang Wang
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Yimin Hou
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
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