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Nie J, Shu H, Wu F. An epilepsy classification based on FFT and fully convolutional neural network nested LSTM. Front Neurosci 2024; 18:1436619. [PMID: 39139499 PMCID: PMC11319253 DOI: 10.3389/fnins.2024.1436619] [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: 05/22/2024] [Accepted: 07/16/2024] [Indexed: 08/15/2024] Open
Abstract
Background and objective Epilepsy, which is associated with neuronal damage and functional decline, typically presents patients with numerous challenges in their daily lives. An early diagnosis plays a crucial role in managing the condition and alleviating the patients' suffering. Electroencephalogram (EEG)-based approaches are commonly employed for diagnosing epilepsy due to their effectiveness and non-invasiveness. In this study, a classification method is proposed that use fast Fourier Transform (FFT) extraction in conjunction with convolutional neural networks (CNN) and long short-term memory (LSTM) models. Methods Most methods use traditional frameworks to classify epilepsy, we propose a new approach to this problem by extracting features from the source data and then feeding them into a network for training and recognition. It preprocesses the source data into training and validation data and then uses CNN and LSTM to classify the style of the data. Results Upon analyzing a public test dataset, the top-performing features in the fully CNN nested LSTM model for epilepsy classification are FFT features among three types of features. Notably, all conducted experiments yielded high accuracy rates, with values exceeding 96% for accuracy, 93% for sensitivity, and 96% for specificity. These results are further benchmarked against current methodologies, showcasing consistent and robust performance across all trials. Our approach consistently achieves an accuracy rate surpassing 97.00%, with values ranging from 97.95 to 99.83% in individual experiments. Particularly noteworthy is the superior accuracy of our method in the AB versus (vs.) CDE comparison, registering at 99.06%. Conclusion Our method exhibits precise classification abilities distinguishing between epileptic and non-epileptic individuals, irrespective of whether the participant's eyes are closed or open. Furthermore, our technique shows remarkable performance in effectively categorizing epilepsy type, distinguishing between epileptic ictal and interictal states versus non-epileptic conditions. An inherent advantage of our automated classification approach is its capability to disregard EEG data acquired during states of eye closure or eye-opening. Such innovation holds promise for real-world applications, potentially aiding medical professionals in diagnosing epilepsy more efficiently.
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Affiliation(s)
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, China
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Dong C, Sun D. Spatial-Temporal Dynamic Hypergraph Information Bottleneck for Brain Network Classification. Int J Neural Syst 2024:2450053. [PMID: 39017038 DOI: 10.1142/s0129065724500539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
Recently, Graph Neural Networks (GNNs) have gained widespread application in automatic brain network classification tasks, owing to their ability to directly capture crucial information in non-Euclidean structures. However, two primary challenges persist in this domain. First, within the realm of clinical neuro-medicine, signals from cerebral regions are inevitably contaminated with noise stemming from physiological or external factors. The construction of brain networks heavily relies on set thresholds and feature information within brain regions, making it susceptible to the incorporation of such noises into the brain topology. Additionally, the static nature of the artificially constructed brain network's adjacent structure restricts real-time changes in brain topology. Second, mainstream GNN-based approaches tend to focus solely on capturing information interactions of nearest neighbor nodes, overlooking high-order topology features. In response to these challenges, we propose an adaptive unsupervised Spatial-Temporal Dynamic Hypergraph Information Bottleneck (ST-DHIB) framework for dynamically optimizing brain networks. Specifically, adopting an information theory perspective, Graph Information Bottleneck (GIB) is employed for purifying graph structure, and dynamically updating the processed input brain signals. From a graph theory standpoint, we utilize the designed Hypergraph Neural Network (HGNN) and Bi-LSTM to capture higher-order spatial-temporal context associations among brain channels. Comprehensive patient-specific and cross-patient experiments have been conducted on two available datasets. The results demonstrate the advancement and generalization of the proposed framework.
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Affiliation(s)
- Changxu Dong
- School of Artificial Intelligence, Anhui University, Hefei 230601, P. R. China
| | - Dengdi Sun
- School of Artificial Intelligence, Anhui University, Hefei 230601, P. R. China
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Huang L, Zhou K, Chen S, Chen Y, Zhang J. Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer. Biomed Eng Online 2024; 23:50. [PMID: 38824547 PMCID: PMC11143608 DOI: 10.1186/s12938-024-01244-w] [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: 01/12/2024] [Accepted: 05/08/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios. METHOD To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection. RESULTS The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy. CONCLUSION The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.
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Affiliation(s)
- Leen Huang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Keying Zhou
- Department of Pediatrics, Shenzhen People's Hospital, Shenzhen, 518020, Guangdong, China
- Department of Pediatrics, Second Clinical Medical College of Jinan University, Shenzhen, 518020, Guangdong, China
- Department of Pediatrics, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
| | - Siyang Chen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Yanzhao Chen
- Department of Pediatrics, Shenzhen People's Hospital, Shenzhen, 518020, Guangdong, China
- Department of Pediatrics, Second Clinical Medical College of Jinan University, Shenzhen, 518020, Guangdong, China
- Department of Pediatrics, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
| | - Jinxin Zhang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
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Liu G, Tian L, Wen Y, Yu W, Zhou W. Cosine convolutional neural network and its application for seizure detection. Neural Netw 2024; 174:106267. [PMID: 38555723 DOI: 10.1016/j.neunet.2024.106267] [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: 09/19/2023] [Revised: 02/23/2024] [Accepted: 03/22/2024] [Indexed: 04/02/2024]
Abstract
Traditional convolutional neural networks (CNNs) often suffer from high memory consumption and redundancy in their kernel representations, leading to overfitting problems and limiting their application in real-time, low-power scenarios such as seizure detection systems. In this work, a novel cosine convolutional neural network (CosCNN), which replaces traditional kernels with the robust cosine kernel modulated by only two learnable factors, is presented, and its effectiveness is validated on the tasks of seizure detection. Meanwhile, based on the cosine lookup table and KL-divergence, an effective post-training quantization algorithm is proposed for CosCNN hardware implementation. With quantization, CosCNN can achieve a nearly 75% reduction in the memory cost with almost no accuracy loss. Moreover, we design a configurable cosine convolution accelerator on Field Programmable Gate Array (FPGA) and deploy the quantized CosCNN on Zedboard, proving the proposed seizure detection system can operate in real-time and low-power scenarios. Extensive experiments and comparisons were conducted using two publicly available epileptic EEG databases, the Bonn database and the CHB-MIT database. The results highlight the performance superiority of the CosCNN over traditional CNNs as well as other seizure detection methods.
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Affiliation(s)
- Guoyang Liu
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Lan Tian
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Yiming Wen
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Weize Yu
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Weidong Zhou
- School of Integrated Circuits, Shandong University, Jinan 250100, China.
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Chung YG, Cho A, Kim H, Kim KJ. Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset. Front Neurol 2024; 15:1389731. [PMID: 38836000 PMCID: PMC11148866 DOI: 10.3389/fneur.2024.1389731] [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: 02/22/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024] Open
Abstract
Introduction Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial seizure characteristics of individual patients. Methods We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient's distinctive seizure locations with seizure re-annotation. Results Our multi- and single-channel detectors achieved an average sensitivity of 97.05-100%, false alarm rate of 0.22-0.40/h, and latency of 2.1-3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones. Discussion We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.
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Affiliation(s)
- Yoon Gi Chung
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Anna Cho
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Hunmin Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, 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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Rafiei MH, Gauthier LV, Adeli H, Takabi D. Self-Supervised Learning for Electroencephalography. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:1457-1471. [PMID: 35867362 DOI: 10.1109/tnnls.2022.3190448] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning techniques require relatively large, labeled EEG repositories. EEG data collection and labeling are costly. Moreover, combining available datasets to achieve a large data volume is usually infeasible due to inconsistent experimental paradigms across trials. Self-supervised learning (SSL) solves these challenges because it enables learning from EEG records across trials with variable experimental paradigms, even when the trials explore different phenomena. It aggregates multiple EEG repositories to increase accuracy, reduce bias, and mitigate overfitting in machine learning training. In addition, SSL could be employed in situations where there is limited labeled training data, and manual labeling is costly. This article: 1) provides a brief introduction to SSL; 2) describes some SSL techniques employed in recent studies, including EEG; 3) proposes current and potential SSL techniques for future investigations in EEG studies; 4) discusses the cons and pros of different SSL techniques; and 5) proposes holistic implementation tips and potential future directions for EEG SSL practices.
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Yu Z, Kachenoura A, Jeannès RLB, Shu H, Berraute P, Nica A, Merlet I, Albera L, Karfoul A. Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy. Neuroimage 2024; 285:120490. [PMID: 38103624 DOI: 10.1016/j.neuroimage.2023.120490] [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: 08/07/2023] [Revised: 11/30/2023] [Accepted: 12/06/2023] [Indexed: 12/19/2023] Open
Abstract
Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the underlying Electrophysiological Source Imaging (ESI) problem. To guarantee a unique solution, most existing ESI methods pay more attention to solve this inverse problem by imposing physiological constraints. This paper proposes an efficient ESI approach based on simulation-driven deep learning. Epileptic High-resolution 256-channels scalp EEG (Hr-EEG) signals are simulated in a realistic manner to train the proposed patient-specific model. More particularly, a computational neural mass model developed in our team is used to generate the temporal dynamics of the activity of each dipole while the forward problem is solved using a patient-specific three-shell realistic head model and the boundary element method. A Temporal Convolutional Network (TCN) is considered in the proposed model to capture local spatial patterns. To enable the model to observe the EEG signals from different scale levels, the multi-scale strategy is leveraged to capture the overall features and fine-grain features by adjusting the convolutional kernel size. Then, the Long Short-Term Memory (LSTM) is used to extract temporal dependencies among the computed spatial features. The performance of the proposed method is evaluated through three different scenarios of realistic synthetic interictal Hr-EEG data as well as on real interictal Hr-EEG data acquired in three patients with drug-resistant partial epilepsy, during their presurgical evaluation. A performance comparison study is also conducted with two other deep learning-based methods and four classical ESI techniques. The proposed model achieved a Dipole Localization Error (DLE) of 1.39 and Normalized Hamming Distance (NHD) of 0.28 in the case of one patch with SNR of 10 dB. In the case of two uncorrelated patches with an SNR of 10 dB, obtained DLE and NHD were respectively 1.50 and 0.28. Even in the more challenging scenario of two correlated patches with an SNR of 10 dB, the proposed approach still achieved a DLE of 3.74 and an NHD of 0.43. The results obtained on simulated data demonstrate that the proposed method outperforms the existing methods for different signal-to-noise and source configurations. The good behavior of the proposed method is also confirmed on real interictal EEG data. The robustness with respect to noise makes it a promising and alternative tool to localize epileptic brain areas and to reconstruct their electrical activities from EEG signals.
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Affiliation(s)
- Zuyi Yu
- Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, PR China; Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Nanjing 210096, PR China; University Rennes, INSERM, LTSI-UMR 1099, Rennes F-35042, France; Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35042, France
| | - Amar Kachenoura
- University Rennes, INSERM, LTSI-UMR 1099, Rennes F-35042, France; Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35042, France
| | - Régine Le Bouquin Jeannès
- University Rennes, INSERM, LTSI-UMR 1099, Rennes F-35042, France; Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35042, France
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, PR China; Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Nanjing 210096, PR China.
| | | | - Anca Nica
- University Rennes, INSERM, LTSI-UMR 1099, Rennes F-35042, France; Centre Hospitalier Universitaire (CHU) de Rennes, service de neurologie, pôle des neurosciences de Rennes, Rennes F-35042, France
| | - Isabelle Merlet
- University Rennes, INSERM, LTSI-UMR 1099, Rennes F-35042, France; Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35042, France
| | - Laurent Albera
- University Rennes, INSERM, LTSI-UMR 1099, Rennes F-35042, France; Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35042, France.
| | - Ahmad Karfoul
- University Rennes, INSERM, LTSI-UMR 1099, Rennes F-35042, France; Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35042, France
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Sopic D, Teijeiro T, Atienza D, Aminifar A, Ryvlin P. Personalized seizure signature: An interpretable approach to false alarm reduction for long-term epileptic seizure detection. Epilepsia 2023; 64 Suppl 4:S23-S33. [PMID: 35113451 DOI: 10.1111/epi.17176] [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: 10/15/2021] [Revised: 01/13/2022] [Accepted: 01/14/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Long-term automatic detection of focal seizures remains one of the major challenges in epilepsy due to the unacceptably high number of false alarms from state-of-the-art methods. Our aim was to investigate to what extent a new patient-specific approach based on similarly occurring morphological electroencephalographic (EEG) signal patterns could be used to distinguish seizures from nonseizure events, as well as to estimate its maximum performance. METHODS We evaluated our approach on >5500 h of long-term EEG recordings using two public datasets: the PhysioNet.org Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) Scalp EEG database and the EPILEPSIAE European epilepsy database. We visually identified a set of similarly occurring morphological patterns (seizure signature) seen simultaneously over two different EEG channels, and within two randomly selected seizures from each individual. The same seizure signature was then searched for in the entire recording from the same patient using dynamic time warping (DTW) as a similarity metric, with a threshold set to reflect the maximum sensitivity our algorithm could achieve without false alarm. RESULTS At a DTW threshold providing no false alarm during the entire recordings, the mean seizure detection sensitivity across patients was 84%, including 96% for the CHB-MIT database and 74% for the European epilepsy database. A 100% sensitivity was reached in 50% of patients, including 79% from the CHB-MIT database and 27% from the European epilepsy database. The median latency from seizure onset to its detection was 17 ± 10 s, with 84% of seizures being detected within 40 s. SIGNIFICANCE Personalized EEG signature combined with DTW appears to be a promising method to detect ictal events from a limited number of EEG channels with high sensitivity despite low rate of false alarms, high degree of interpretability, and low computational complexity, compatible with its future use in wearable devices.
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Affiliation(s)
- Dionisije Sopic
- Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Tomas Teijeiro
- Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - David Atienza
- Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Amir Aminifar
- Department of Electrical and Information Technology, Lund University, Lund, Sweden
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Neurology Service, Lausanne University Hospital (Vaud University Hospital Center), University of Lausanne, Lausanne, Switzerland
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Wang Z, Hou S, Xiao T, Zhang Y, Lv H, Li J, Zhao S, Zhao Y. Lightweight Seizure Detection Based on Multi-Scale Channel Attention. Int J Neural Syst 2023; 33:2350061. [PMID: 37845193 DOI: 10.1142/s0129065723500612] [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] [Indexed: 10/18/2023]
Abstract
Epilepsy is one kind of neurological disease characterized by recurring seizures. Recurrent seizures can cause ongoing negative mental and cognitive damage to the patient. Therefore, timely diagnosis and treatment of epilepsy are crucial for patients. Manual electroencephalography (EEG) signals analysis is time and energy consuming, making automatic detection using EEG signals particularly important. Many deep learning algorithms have thus been proposed to detect seizures. These methods rely on expensive and bulky hardware, which makes them unsuitable for deployment on devices with limited resources due to their high demands on computer resources. In this paper, we propose a novel lightweight neural network for seizure detection using pure convolutions, which is composed of inverted residual structure and multi-scale channel attention mechanism. Compared with other methods, our approach significantly reduces the computational complexity, making it possible to deploy on low-cost portable devices for seizures detection. We conduct experiments on the CHB-MIT dataset and achieves 98.7% accuracy, 98.3% sensitivity and 99.1% specificity with 2.68[Formula: see text]M multiply-accumulate operations (MACs) and only 88[Formula: see text]K parameters.
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Affiliation(s)
- Ziwei Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Sujuan Hou
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Tiantian Xiao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Yongfeng Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Hongbin Lv
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Jiacheng Li
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Shanshan Zhao
- Department of Hematology, Heze Hospital of Traditional Chinese Medicine, Heze 274000, P. R. China
| | - Yanna Zhao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
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Zhang S, Wang H, Zheng Z, Liu T, Li W, Zhang Z, Sun Y. Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals. Int J Neural Syst 2023; 33:2350055. [PMID: 37899654 DOI: 10.1142/s0129065723500557] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
Automated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels remaining. (2) Most works focus on the functional connection of EEG signals, ignoring their spatial proximity. The spatial topological structure of EEG signals has not been fully utilized to capture more fine-grained features. (3) Prior depression detection models fail to provide interpretability. To address these challenges, this paper proposes a new model, Multi-view Graph Contrastive Learning via Adaptive Channel Optimization (MGCL-ACO) for depression detection in EEG signals. Specifically, the proposed model first selects the critical channels by maximizing the mutual information between tracks and labels of EEG signals to eliminate data redundancy. Then, the MGCL-ACO model builds two similarity metric views based on functional connectivity and spatial proximity. MGCL-ACO constructs the feature extraction module by graph convolutions and contrastive learning to capture more fine-grained features of different perspectives. Finally, our model provides interpretability by visualizing a brain map related to the significance scores of the selected channels. Extensive experiments have been performed on public datasets, and the results show that our proposed model outperforms the most advanced baselines. Our proposed model not only provides a promising approach for automated depression detection using optimal EEG signals but also has the potential to improve the accuracy and interpretability of depression diagnosis in clinical practice.
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Affiliation(s)
- Shuangyong Zhang
- School of Information Science and Engineering Shandong Normal University, Jinan 250014, P. R. China
| | - Hong Wang
- School of Information Science and Engineering Shandong Normal University, Jinan 250014, P. R. China
| | - Zixi Zheng
- School of Information Science and Engineering Shandong Normal University, Jinan 250014, P. R. China
| | - Tianyu Liu
- School of Information Science and Engineering Shandong Normal University, Jinan 250014, P. R. China
| | - Weixin Li
- School of Information Science and Engineering Shandong Normal University, Jinan 250014, P. R. China
| | - Zishan Zhang
- School of Information Science and Engineering Shandong Normal University, Jinan 250014, P. R. China
| | - Yanshen Sun
- Department of Computer Science, Virginia Tech, Blacksburg 24061, USA
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Si X, He H, Yu J, Ming D. Cross-Subject Emotion Recognition Brain-Computer Interface Based on fNIRS and DBJNet. CYBORG AND BIONIC SYSTEMS 2023; 4:0045. [PMID: 37519929 PMCID: PMC10374245 DOI: 10.34133/cbsystems.0045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/05/2023] [Indexed: 08/01/2023] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that has gradually been applied in emotion recognition research due to its advantages of high spatial resolution, real time, and convenience. However, the current research on emotion recognition based on fNIRS is mainly limited to within-subject, and there is a lack of related work on emotion recognition across subjects. Therefore, in this paper, we designed an emotion evoking experiment with videos as stimuli and constructed the fNIRS emotion recognition database. On this basis, deep learning technology was introduced for the first time, and a dual-branch joint network (DBJNet) was constructed, creating the ability to generalize the model to new participants. The decoding performance obtained by the proposed model shows that fNIRS can effectively distinguish positive versus neutral versus negative emotions (accuracy is 74.8%, F1 score is 72.9%), and the decoding performance on the 2-category emotion recognition task of distinguishing positive versus neutral (accuracy is 89.5%, F1 score is 88.3%), negative versus neutral (accuracy is 91.7%, F1 score is 91.1%) proved fNIRS has a powerful ability to decode emotions. Furthermore, the results of the ablation study of the model structure demonstrate that the joint convolutional neural network branch and the statistical branch achieve the highest decoding performance. The work in this paper is expected to facilitate the development of fNIRS affective brain-computer interface.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine,
Tianjin University, Tianjin 300072, People’s Republic of China
- Tianjin Key Laboratory of Brain Science and Neural Engineering,
Tianjin University, Tianjin 300072, People’s Republic of China
| | - Huang He
- Academy of Medical Engineering and Translational Medicine,
Tianjin University, Tianjin 300072, People’s Republic of China
- Tianjin Key Laboratory of Brain Science and Neural Engineering,
Tianjin University, Tianjin 300072, People’s Republic of China
| | - Jiayue Yu
- Tianjin Key Laboratory of Brain Science and Neural Engineering,
Tianjin University, Tianjin 300072, People’s Republic of China
- Tianjin International Engineering Institute,
Tianjin University, Tianjin 300072, People’s Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine,
Tianjin University, Tianjin 300072, People’s Republic of China
- Tianjin Key Laboratory of Brain Science and Neural Engineering,
Tianjin University, Tianjin 300072, People’s Republic of China
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12
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Yu HS, Meng XF. Characteristic analysis of epileptic brain network based on attention mechanism. Sci Rep 2023; 13:10742. [PMID: 37400535 PMCID: PMC10317957 DOI: 10.1038/s41598-023-38012-0] [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: 10/05/2022] [Accepted: 06/30/2023] [Indexed: 07/05/2023] Open
Abstract
Constructing an efficient and accurate epilepsy detection system is an urgent research task. In this paper, we developed an EEG-based multi-frequency multilayer brain network (MMBN) and an attentional mechanism based convolutional neural network (AM-CNN) model to study epilepsy detection. Specifically, based on the multi-frequency characteristics of the brain, we first use wavelet packet decomposition and reconstruction methods to divide the original EEG signals into eight frequency bands, and then construct MMBN through correlation analysis between brain regions, where each layer corresponds to a specific frequency band. The time, frequency and channel related information of EEG signals are mapped into the multilayer network topology. On this basis, a multi-branch AM-CNN model is designed, which completely matches the multilayer structure of the proposed brain network. The experimental results on public CHB-MIT datasets show that eight frequency bands divided in this work are all helpful for epilepsy detection, and the fusion of multi-frequency information can effectively decode the epileptic brain state, achieving accurate detection of epilepsy with an average accuracy of 99.75%, sensitivity of 99.43%, and specificity of 99.83%. All of these provide reliable technical solutions for EEG-based neurological disease detection, especially for epilepsy detection.
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Affiliation(s)
- Hong-Shi Yu
- School of Electronics and Information Engineering, Liaoning Technical University, Huludao, 125105, China.
- Liaoning Key Laboratory of Radio Frequency Big Data Intelligent Application, Huludao, 125105, China.
| | - Xiang-Fu Meng
- School of Electronics and Information Engineering, Liaoning Technical University, Huludao, 125105, China
- Liaoning Key Laboratory of Radio Frequency Big Data Intelligent Application, Huludao, 125105, China
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13
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Tian Z, Hu B, Si Y, Wang Q. Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifier. Brain Sci 2023; 13:brainsci13050820. [PMID: 37239292 DOI: 10.3390/brainsci13050820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 04/28/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
(1) Background: Epilepsy is a neurological disorder that causes repeated seizures. Since electroencephalogram (EEG) patterns differ in different states (inter-ictal, pre-ictal, and ictal), a seizure can be detected and predicted by extracting various features. However, the brain connectivity network, a two-dimensional feature, is rarely studied. We aim to investigate its effectiveness for seizure detection and prediction. (2) Methods: Two time-window lengths, five frequency bands, and five connectivity measures were used to extract image-like features, which were fed into a support vector machine for the subject-specific model (SSM) and a convolutional neural networks meet transformers (CMT) classifier for the subject-independent model (SIM) and cross-subject model (CSM). Finally, feature selection and efficiency analyses were conducted. (3) Results: The classification results on the CHB-MIT dataset showed that a long window indicated better performance. The best detection accuracies of SSM, SIM, and CSM were 100.00, 99.98, and 99.27%, respectively. The highest prediction accuracies were 99.72, 99.38, and 86.17%, respectively. In addition, Pearson Correlation Coefficient and Phase Lock Value connectivity in the β and γ bands showed good performance and high efficiency. (4) Conclusions: The proposed brain connectivity features showed good reliability and practical value for automatic seizure detection and prediction, which expects to develop portable real-time monitoring equipment.
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Affiliation(s)
- Ziwei Tian
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 101408, China
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
| | - Bingliang Hu
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
| | - Yang Si
- Department of Neurology, Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, Chengdu 610072, China
- School of Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
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14
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Si X, Yang Z, Zhang X, Sun Y, Jin W, Wang L, Yin S, Ming D. Patient-independent seizure detection based on long-term iEEG and a novel lightweight CNN. J Neural Eng 2023; 20. [PMID: 36626831 DOI: 10.1088/1741-2552/acb1d9] [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: 05/04/2022] [Accepted: 01/10/2023] [Indexed: 01/11/2023]
Abstract
Objective.Patient-dependent seizure detection based on intracranial electroencephalography (iEEG) has made significant progress. However, due to the difference in the locations and number of iEEG electrodes used for each patient, patient-independent seizure detection based on iEEG has not been carried out. Additionally, current seizure detection algorithms based on deep learning have outperformed traditional machine learning algorithms in many performance metrics. However, they still have shortcomings of large memory footprints and slow inference speed.Approach.To solve the above problems of the current study, we propose a novel lightweight convolutional neural network model combining the Convolutional Block Attention Module (CBAM). Its performance for patient-independent seizure detection is evaluated on two long-term continuous iEEG datasets: SWEC-ETHZ and TJU-HH. Finally, we reproduce four other patient-independent methods to compare with our method and calculate the memory footprints and inference speed for all methods.Main results.Our method achieves 83.81% sensitivity (SEN) and 85.4% specificity (SPE) on the SWEC-ETHZ dataset and 86.63% SEN and 92.21% SPE on the TJU-HH dataset. In particular, it takes only 11 ms to infer 10 min iEEG (128 channels), and its memory footprint is only 22 kB. Compared to baseline methods, our method not only achieves better patient-independent seizure detection performance but also has a smaller memory footprint and faster inference speed.Significance.To our knowledge, this is the first iEEG-based patient-independent seizure detection study. This facilitates the application of seizure detection algorithms to the future clinic.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Zhuobin Yang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Yulin Sun
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Weipeng Jin
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Le Wang
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Shaoya Yin
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
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15
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GNMF-based quadratic feature extraction in SSTFT domain for epileptic EEG detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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16
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Chou CH, Shen TW, Tung H, Hsieh PF, Kuo CE, Chen TM, Yang CW. Convolutional neural network-based fast seizure detection from video electroencephalograms. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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17
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Gu B, Adeli H. Toward automated prediction of sudden unexpected death in epilepsy. Rev Neurosci 2022; 33:877-887. [PMID: 35619127 DOI: 10.1515/revneuro-2022-0024] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/19/2022] [Indexed: 12/14/2022]
Abstract
Sudden unexpected death in epilepsy (SUDEP) is a devastating yet overlooked complication of epilepsy. The rare and complex nature of SUDEP makes it challenging to study. No prediction or prevention of SUDEP is currently available in a clinical setting. In the past decade, significant advances have been made in our knowledge of the pathophysiologic cascades that lead to SUDEP. In particular, studies of brain, heart, and respiratory functions in both human patients at the epilepsy monitoring unit and animal models during fatal seizures provide critical information to integrate computational tools for SUDEP prediction. The rapid advances in automated seizure detection and prediction algorithms provide a fundamental framework for their adaption in predicting SUDEP. If a SUDEP can be predicted, then there will be a potential for medical intervention to be administered, either by their caregivers or via an implanted device automatically delivering electrical stimulation or medication, and finally save lives from fatal seizures. This article presents recent developments of SUDEP studies focusing on the pathophysiologic basis of SUDEP and computational implications of machine learning techniques that can be adapted and extended for SUDEP prediction. This article also discusses some novel ideas for SUDEP prediction and rescue including principal component analysis and closed-loop intervention.
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Affiliation(s)
- Bin Gu
- Department of Neuroscience, Ohio State University, Columbus, OH 43210, USA
| | - Hojjat Adeli
- Department of Neuroscience, Ohio State University, Columbus, OH 43210, USA.,Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, USA
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18
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XAI4EEG: spectral and spatio-temporal explanation of deep learning-based seizure detection in EEG time series. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07809-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
AbstractIn clinical practice, algorithmic predictions may seriously jeopardise patients’ health and thus are required to be validated by medical experts before a final clinical decision is met. Towards that aim, there is need to incorporate explainable artificial intelligence techniques into medical research. In the specific field of epileptic seizure detection there are several machine learning algorithms but less methods on explaining them in an interpretable way. Therefore, we introduce XAI4EEG: an application-aware approach for an explainable and hybrid deep learning-based detection of seizures in multivariate EEG time series. In XAI4EEG, we combine deep learning models and domain knowledge on seizure detection, namely (a) frequency bands, (b) location of EEG leads and (c) temporal characteristics. XAI4EEG encompasses EEG data preparation, two deep learning models and our proposed explanation module visualizing feature contributions that are obtained by two SHAP explainers, each explaining the predictions of one of the two models. The resulting visual explanations provide an intuitive identification of decision-relevant regions in the spectral, spatial and temporal EEG dimensions. To evaluate XAI4EEG, we conducted a user study, where users were asked to assess the outputs of XAI4EEG, while working under time constraints, in order to emulate the fact that clinical diagnosis is done - more often than not - under time pressure. We found that the visualizations of our explanation module (1) lead to a substantially lower time for validating the predictions and (2) leverage an increase in interpretability, trust and confidence compared to selected SHAP feature contribution plots.
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19
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Yuan S, Mu J, Zhou W, Dai LY, Liu JX, Wang J, Liu X. Automatic Epileptic Seizure Detection Using Graph-Regularized Non-Negative Matrix Factorization and Kernel-Based Robust Probabilistic Collaborative Representation. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2641-2650. [PMID: 36063515 DOI: 10.1109/tnsre.2022.3204533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Automatic seizure detection system can serve as a meaningful clinical tool for the treatment and analysis of epilepsy using electroencephalogram (EEG) and has obtained rapid development. An automatic detection of epileptic seizure method based on kernel-based robust probabilistic collaborative representation (ProCRC) combined with graph-regularized non-negative matrix factorization (GNMF) is proposed in this work. The raw EEG signals are pre-processed through the wavelet transform to obtain time-frequency distribution of EEG signals as preliminary feature information and GNMF is further employed for dimension reduction, retaining and enhancing the productive feature information of EEG signals. Then, the test sample is represented using robust ProCRC that can decide whether the testing sample belongs to each class (seizure or non-seizure) by jointly maximizing the likelihood. In addition, the kernel trick is applied to improve the separability of non-linear high dimensional EEG signals in robust ProCRC. Finally, post-processing techniques are introduced to generate more accurate and reliable results. The average epoch-based sensitivity of 96.48%, event-based sensitivity of 93.65% and specificity of 98.55% are acquired in this method, which is evaluated on the public Freiburg EEG database.
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20
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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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21
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Song K, Fang J, Zhang L, Chen F, Wan J, Xiong N. An Intelligent Epileptic Prediction System Based on Synchrosqueezed Wavelet Transform and Multi-Level Feature CNN for Smart Healthcare IoT. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176458. [PMID: 36080916 PMCID: PMC9460721 DOI: 10.3390/s22176458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 05/03/2023]
Abstract
Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients' quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively.
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Affiliation(s)
- Kunpeng Song
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
| | - Jiajia Fang
- Department of Neurology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu 322000, China
| | - Lei Zhang
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
| | - Fangni Chen
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
- Correspondence:
| | - Jian Wan
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
| | - Neal Xiong
- Department of Computer Science and Mathematics, Sul Ross State University, Alpine, TX 79830, USA
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22
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Dash DP, Kolekar MH, Chakraborty C, Khosravi MR. Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3552512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Epilepsy is one of the significant neurological disorders affecting nearly 65 million people worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were proposed for efficient seizure detection using intracranial and surface EEG signals. In the last decade, various machine learning techniques based on seizure detection approaches were proposed. This paper discusses different machine learning and deep learning techniques for seizure detection using intracranial and surface EEG signals. A wide range of machine learning techniques such as support vector machine (SVM) classifiers, artificial neural network (ANN) classifier, and deep learning techniques such as a convolutional neural network (CNN) classifier, long-short term memory (LSTM) network for seizure detection are compared in this paper. The effectiveness of time-domain features, frequency domain features, and time-frequency domain features are discussed along with different machine learning techniques. Along with EEG, other physiological signals such as electrocardiogram are used to enhance seizure detection accuracy which are discussed in this paper. In recent years deep learning techniques based on seizure detection have found good classification accuracy. In this paper, an LSTM deep learning-network-based approach is implemented for seizure detection and compared with state-of-the-art methods. The LSTM based approach achieved 96.5% accuracy in seizure-nonseizure EEG signal classification. Apart from analyzing the physiological signals, sentiment analysis also has potential to detect seizure.
Impact Statement-
This review paper gives a summary of different research work related to epileptic seizure detection using machine learning and deep learning techniques. Manual seizure detetion is time consuming and requires expertise. So the artificial intelligence techniques such as machine learning and deep learning techniques are used for automatic seizure detection. Different physiological signals are used for seizure detection. Different researchers are working on developing automatic seizure detection using EEG, ECG, accelerometer, sentiment analysis. There is a need for a review paper that can discuss previous techniques and give further research direction. We have discussed different techniques for seizure detection with an accuracy comparison table. It can help the researcher to get an overview of both surface and intracranial EEG-based seizure detection approaches. The new researcher can easily compare different models and decide the model they want to start working on. A deep learning model is discussed to give a practical application of seizure detection. Sentiment analysis is another dimension of seizure detection and summerizing it will give a new prospective to the reader.
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23
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Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning. Comput Biol Med 2022; 146:105511. [DOI: 10.1016/j.compbiomed.2022.105511] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 12/11/2022]
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24
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Ibrahim FE, Emara HM, El-Shafai W, Elwekeil M, Rihan M, Eldokany IM, Taha TE, El-Fishawy AS, El-Rabaie ESM, Abdellatef E, Abd El-Samie FE. Deep-learning-based seizure detection and prediction from electroencephalography signals. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3573. [PMID: 35077027 DOI: 10.1002/cnm.3573] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 01/19/2022] [Accepted: 01/19/2022] [Indexed: 06/14/2023]
Abstract
Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively low inter-rater agreement. Furthermore, the new data interpretation consumes an excessive amount of time and resources. Hence, an automatic seizure detection and prediction system can improve the quality of patient care in terms of shortening the diagnosis period, reducing manual errors, and automatically detecting debilitating events. Moreover, for patient treatment, it is important to alert the patients of epilepsy seizures prior to seizure occurrence. Various distinguished studies presented good solutions for two-class seizure detection problems with binary classification scenarios. To deal with these challenges, this paper puts forward effective approaches for EEG signal classification for normal, pre-ictal, and ictal activities. Three models are presented for the classification task. Two of them are patient-specific, while the third one is patient non-specific, which makes it better for the general classification tasks. The two-class classification is implemented between normal and pre-ictal activities for seizure prediction and between normal and ictal activities for seizure detection. A more generalized three-class classification framework is considered to identify all EEG signal activities. The first model depends on a Convolutional Neural Network (CNN) with residual blocks. It contains thirteen layers with four residual learning blocks. It works on spectrograms of EEG signal segments. The second model depends on a CNN with three layers. It also works on spectrograms. On the other hand, the third model depends on Phase Space Reconstruction (PSR) to eliminate the limitations of the spectrograms used in the first models. A five-layer CNN is used with this strategy. The advantage of the PSR is the direct projection from the time domain, which keeps the main trend of different signal activities. The third model deals with all signal activities, and it was tested for all patients of the CHB-MIT dataset. It has a superior performance compared to the first models and the state-of-the-art models.
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Affiliation(s)
- Fatma E Ibrahim
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Heba M Emara
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Walid El-Shafai
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
- Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, Saudi Arabia
| | - Mohamed Elwekeil
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
- Department of Electrical and Information Engineering (DIEI), University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Mohamed Rihan
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
- Department of Electrical and Information Engineering (DIEI), University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Ibrahim M Eldokany
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Taha E Taha
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Adel S El-Fishawy
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - El-Sayed M El-Rabaie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Essam Abdellatef
- Delta Higher Institute for Engineering and Technology (DHIET), Mansoura, Egypt
| | - Fathi E Abd El-Samie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
- Department of Information Technology, College of Computer and Information sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
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25
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Luo Z, Jin S, Li Z, Huang H, Xiao L, Chen H, Heidari AA, Hu J, Chen C, Chen P, Hu Z. Hierarchical Harris hawks optimization for epileptic seizure classification. Comput Biol Med 2022; 145:105397. [DOI: 10.1016/j.compbiomed.2022.105397] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 03/08/2022] [Accepted: 03/09/2022] [Indexed: 01/15/2023]
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26
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Yu Z, Albera L, Jeannes RLB, Kachenoura A, Karfoul A, Yang C, Shu H. Epileptic Seizure Prediction Using Deep Neural Networks via Transfer Learning and Multi-Feature Fusion. Int J Neural Syst 2022; 32:2250032. [DOI: 10.1142/s0129065722500320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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27
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Guo Y, Jiang X, Tao L, Meng L, Dai C, Long X, Wan F, Zhang Y, van Dijk J, Aarts RM, Chen W, Chen C. Epileptic Seizure Detection by Cascading Isolation Forest-based Anomaly Screening and EasyEnsemble. IEEE Trans Neural Syst Rehabil Eng 2022; 30:915-924. [PMID: 35353703 DOI: 10.1109/tnsre.2022.3163503] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming. In this study, we propose a hybrid system integrating an unsupervised learning (UL) module and a supervised learning (SL) module, where the UL module can significantly reduce the workload of data labelling. For preliminary seizure screening, UL synthesizes amplitude-integrated EEG (aEEG) extraction, isolation forest-based anomaly detection, adaptive segmentation, and silhouette coefficient-based anomaly detection evaluation. The UL module serves to quickly locate the determinate subjects (seizure segments and seizure-free segments) and the indeterminate subjects (potential seizure candidates). Afterwards, more robust seizure detection for the indeterminate subjects is performed by the SL using an EasyEnsemble algorithm. EasyEnsemble, as a class-imbalance learning method, can potentially decrease the generalization error of the seizure-free segments. The proposed method can significantly reduce the workload of data labelling while guaranteeing satisfactory performance. The proposed seizure detection system is evaluated using the Children's Hospital Boston - Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, and it achieves a mean accuracy of 92.62%, a mean sensitivity of 95.55%, and a mean specificity of 92.57%. To the best of our knowledge, this is the first epilepsy seizure detection study employing the integration of both the UL and the SL modules, achieving a competitive performance superior or similar to that of the state-of-the-art methods.
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28
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Zhou J, Liu L, Leng Y, Yang Y, Gao B, Jiang Z, Nie W, Yuan Q. Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic. Int J Neural Syst 2022; 32:2250017. [DOI: 10.1142/s0129065722500174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own previous EEG data and therefore cannot be initially diagnosed. If the EEG data of other patients can be used to achieve cross-patient detection, and cross-patient and patient-specific experiments can be combined at the same time, this method will be more widely used. In this work, an EEG classification model based on a self-organizing fuzzy logic (SOF) classifier is proposed for both cross-patient and patient-specific seizure detection. After preprocessing, the features of the original EEG signal are extracted and sent to the SOF classifier. This classification model is free from predefined parameters or a prior assumption regarding the EEG data generation model and only stores the key meta-parameters in memory. Therefore, it is very suitable for large-scale EEG signals in cross-patient detection. Selecting different granularity and classification distance in two different experiments after post-processing will achieve the best results. Experiments were conducted using a long-term continuous scalp EEG database and the [Formula: see text]-mean of cross-patient and patient-specific detection reached 83.35% and 92.04%, respectively. A comparison with other methods shows that there is greater performance and generalizability with this method.
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Affiliation(s)
- Jiazheng Zhou
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Li Liu
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Yan Leng
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Yuying Yang
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Bin Gao
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Zonghong Jiang
- College of Resources and Environment Engineering, Guizhou University, Guiyang 550025, P. R. China
| | - Weiwei Nie
- The First Affiliated Hospital of Shandong, First Medical University, Jinan 250014, P. R. China
| | - Qi Yuan
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
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29
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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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30
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Liu X, Wang M, Aftab R. Study on the Prediction Method of Long-term Benign and Malignant Pulmonary Lesions Based on LSTM. Front Bioeng Biotechnol 2022; 10:791424. [PMID: 35309999 PMCID: PMC8924408 DOI: 10.3389/fbioe.2022.791424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/06/2022] [Indexed: 11/20/2022] Open
Abstract
In order to more accurately and comprehensively characterize the changes and development rules of lesion characteristics in pulmonary medical images in different periods, the study was conducted to predict the evolution of pulmonary nodules in the longitudinal dimension of time, and a benign and malignant prediction model of pulmonary lesions in different periods was constructed under multiscale three-dimensional (3D) feature fusion. According to the sequence of computed tomography (CT) images of patients at different stages, 3D interpolation was conducted to generate 3D lung CT images. The 3D features of different size lesions in the lungs were extracted using 3D convolutional neural networks for fusion features. A time-modulated long short-term memory was constructed to predict the benign and malignant lesions by using the improved time-length memory method to learn the feature vectors of lung lesions with temporal and spatial characteristics in different periods. The experiment shows that the area under the curve of the proposed method is 92.71%, which is higher than that of the traditional method.
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Affiliation(s)
- Xindong Liu
- Faculty of Science, Hong Kong Baptist University, Hong Kong, China
| | - Mengnan Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Rukhma Aftab
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
- *Correspondence: Rukhma Aftab,
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31
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Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Alizadehsani R, Zare A, Khosravi A, Subasi A, Rajendra Acharya U, Gorriz JM. Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103417] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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32
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Peng P, Song Y, Yang L, Wei H. Seizure Prediction in EEG Signals Using STFT and Domain Adaptation. Front Neurosci 2022; 15:825434. [PMID: 35115906 PMCID: PMC8805457 DOI: 10.3389/fnins.2021.825434] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 12/22/2021] [Indexed: 12/04/2022] Open
Abstract
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional approaches commonly collect training and testing samples from the same patient due to inter-individual variability. However, the challenging problem of domain shift between various subjects remains unsolved, resulting in a low conversion rate to the clinic. In this work, a domain adaptation (DA)-based model is proposed to circumvent this issue. The short-time Fourier transform (STFT) is employed to extract the time-frequency features from raw EEG data, and an autoencoder is developed to map these features into high-dimensional space. By minimizing the inter-domain distance in the embedding space, this model learns the domain-invariant information, such that the generalization ability is improved by distribution alignment. Besides, to increase the feasibility of its application, this work mimics the data distribution under the clinical sampling situation and tests the model under this condition, which is the first study that adopts the assessment strategy. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can minimize the domain gap effectively compared with previous approaches.
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Affiliation(s)
- Peizhen Peng
- Key Laboratory of Measurement and Control of Control Science and Engineering (CSE), Ministry of Education, School of Automation, Southeast University, Nanjing, China
| | - Yang Song
- State Grid Nanjing Power Supply Company, Nanjing, China
| | - Lu Yang
- Epilepsy Center, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Haikun Wei
- Key Laboratory of Measurement and Control of Control Science and Engineering (CSE), Ministry of Education, School of Automation, Southeast University, Nanjing, China
- *Correspondence: Haikun Wei
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33
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Cherian R, Kanaga EG. Theoretical and Methodological Analysis of EEG based Seizure Detection and Prediction: An Exhaustive Review. J Neurosci Methods 2022; 369:109483. [DOI: 10.1016/j.jneumeth.2022.109483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/13/2022] [Accepted: 01/13/2022] [Indexed: 02/07/2023]
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34
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Hu T, Xie L, Zhang L, Li G, Yi Z. Deep Multimodal Neural Network Based on Data-Feature Fusion for Patient-Specific Quality Assurance. Int J Neural Syst 2021; 32:2150055. [PMID: 34895106 DOI: 10.1142/s0129065721500556] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Patient-specific quality assurance (QA) for Volumetric Modulated Arc Therapy (VMAT) plans is routinely performed in the clinical. However, it is labor-intensive and time-consuming for medical physicists. QA prediction models can address these shortcomings and improve efficiency. Current approaches mainly focus on single cancer and single modality data. They are not applicable to clinical practice. To assess the accuracy of QA results for VMAT plans, this paper presents a new model that learns complementary features from the multi-modal data to predict the gamma passing rate (GPR). According to the characteristics of VMAT plans, a feature-data fusion approach is designed to fuse the features of imaging and non-imaging information in the model. In this study, 690 VMAT plans are collected encompassing more than ten diseases. The model can accurately predict the most VMAT plans at all three gamma criteria: 2%/2 mm, 3%/2 mm and 3%/3 mm. The mean absolute error between the predicted and measured GPR is 2.17%, 1.16% and 0.71%, respectively. The maximum deviation between the predicted and measured GPR is 3.46%, 4.6%, 8.56%, respectively. The proposed model is effective, and the features of the two modalities significantly influence QA results.
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Affiliation(s)
- Ting Hu
- Department of Computer Science and Technology, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan, P. R. China
| | - Lizhang Xie
- Department of Computer Science and Technology, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan, P. R. China
| | - Lei Zhang
- Department of Computer Science and Technology, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan, P. R. China
| | - Guangjun Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China
| | - Zhang Yi
- Department of Computer Science and Technology, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan, P. R. China
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35
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Che X, Zheng Y, Chen X, Song S, Li S. Decoding Color Visual Working Memory from EEG Signals Using Graph Convolutional Neural Networks. Int J Neural Syst 2021; 32:2250003. [PMID: 34895115 DOI: 10.1142/s0129065722500034] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed that color could be decoded from scalp electroencephalogram (EEG) signals during the encoding stage of VWM, which process visible information with strong neural coding. Whether color could be decoded from other VWM processing stages, especially the maintaining stage which processes invisible information, is still unknown. Here, we constructed an EEG color graph convolutional network model (ECo-GCN) to decode colors during different VWM stages. Based on graph convolutional networks, ECo-GCN considers the graph structure of EEG signals and may be more efficient in color decoding. We found that (1) decoding accuracies for colors during the encoding, early, and late maintaining stages were 81.58%, 79.36%, and 77.06%, respectively, exceeding those during the pre-stimuli stage (67.34%), and (2) the decoding accuracy during maintaining stage could predict participants' memory performance. The results suggest that EEG signals during the maintaining stage may be more sensitive than behavioral measurement to predict the VWM performance of human, and ECo-GCN provides an effective approach to explore human cognitive function.
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Affiliation(s)
- Xiaowei Che
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Yuanjie Zheng
- Key Laboratory of Intelligent Computing & Information, Security in Universities of Shandong Shandong Provincial, Key Laboratory for Novel Distributed Computer Software, Technology Shandong Key Laboratory of Medical, Physics and Image Processing School of Information, Science and Engineering Institute of Biomedical Sciences, Shandong Normal University, Jinan 250358, P. R. China
| | - Xin Chen
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Sutao Song
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Shouxin Li
- Department of Psychology, Shandong Normal University, Jinan, P. R. China
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36
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Maimaiti B, Meng H, Lv Y, Qiu J, Zhu Z, Xie Y, Li Y, Yu-Cheng, Zhao W, Liu J, Li M. An Overview of EEG-based Machine Learning Methods in Seizure Prediction and Opportunities for Neurologists in this Field. Neuroscience 2021; 481:197-218. [PMID: 34793938 DOI: 10.1016/j.neuroscience.2021.11.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022]
Abstract
The unpredictability of epileptic seizures is one of the most problematic aspects of the field of epilepsy. Methods or devices capable of detecting seizures minutes before they occur may help prevent injury or even death and significantly improve the quality of life. Machine learning (ML) is an emerging technology that can markedly enhance algorithm performance by interpreting data. ML has gained increasing attention from medical researchers in recent years. Its epilepsy applications range from the localization of the epileptic region, predicting the medical or surgical outcome of epilepsy, and automated electroencephalography (EEG) analysis to seizure prediction. While ML has good prospects with regard to detecting epileptic seizures via EEG signals, many clinicians are still unfamiliar with this field. This work briefly summarizes the history and recent significant progress made in this field and clarifies the essential components of the automatic seizure detection system using ML methodologies for clinicians. This review also proposes how neurologists can actively contribute to ensure improvements in seizure prediction using EEG-based ML.
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Affiliation(s)
- Buajieerguli Maimaiti
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Hongmei Meng
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China.
| | - Yudan Lv
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Jiqing Qiu
- Department of Neurological Surgery, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Zhanpeng Zhu
- Department of Neurological Surgery, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Yinyin Xie
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Yue Li
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Yu-Cheng
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Weixuan Zhao
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Jiayu Liu
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Mingyang Li
- Department of Communication Engineering, Jilin University, Changchun, Jilin, People's Republic of China.
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37
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Cao X, Yao B, Chen B, Sun W, Tan G. Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG. Front Neurosci 2021; 15:760987. [PMID: 34720869 PMCID: PMC8555879 DOI: 10.3389/fnins.2021.760987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 09/13/2021] [Indexed: 11/30/2022] Open
Abstract
Accurate identification of the type of seizure is very important for the treatment plan and drug prescription of epileptic patients. Artificial intelligence has shown considerable potential in the fields of automated EEG analysis and seizure classification. However, the highly personalized representation of epileptic seizures in EEG has led to many research results that are not satisfactory in clinical applications. In order to improve the clinical adaptability of the algorithm, this paper proposes an adversarial learning-driven domain-invariant deep feature representation method, which enables the hybrid deep networks (HDN) to reliably identify seizure types. In the train phase, we first use the labeled multi-lead EEG short samples to train squeeze-and-excitation networks (SENet) to extract short-term features, and then use the compressed samples to train the long short-term memory networks (LSTM) to extract long-time features and construct a classifier. In the inference phase, we first adjust the feature mapping of LSTM through the adversarial learning between LSTM and clustering subnet so that the EEG of the target patient and the EEG in the database obey the same distribution in the deep feature space. Finally, we use the adjusted classifier to identify the type of seizure. Experiments were carried out based on the TUH EEG Seizure Corpus and CHB-MIT seizure database. The experimental results show that the proposed domain adaptive deep feature representation improves the classification accuracy of the hybrid deep model in the target set by 5%. It is of great significance for the clinical application of EEG automatic analysis equipment.
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Affiliation(s)
- Xincheng Cao
- School of Aerospace Engineering, Xiamen University, Xiamen, China.,Shenzhen Research Institute of Xiamen University, Shenzhen, China
| | - Bin Yao
- School of Aerospace Engineering, Xiamen University, Xiamen, China.,Shenzhen Research Institute of Xiamen University, Shenzhen, China
| | - Binqiang Chen
- School of Aerospace Engineering, Xiamen University, Xiamen, China.,Shenzhen Research Institute of Xiamen University, Shenzhen, China
| | - Weifang Sun
- College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China
| | - Guowei Tan
- Xiamen Key Laboratory of Brain Center, Department of Neurosurgery, The First Affiliated Hospital of Xiamen University, Xiamen, China
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38
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Bhattacharya A, Baweja T, Karri SPK. Epileptic Seizure Prediction Using Deep Transformer Model. Int J Neural Syst 2021; 32:2150058. [PMID: 34720065 DOI: 10.1142/s0129065721500581] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The electroencephalogram (EEG) is the most promising and efficient technique to study epilepsy and record all the electrical activity going in our brain. Automated screening of epilepsy through data-driven algorithms reduces the manual workload of doctors to diagnose epilepsy. New algorithms are biased either towards signal processing or deep learning, which holds subjective advantages and disadvantages. The proposed pipeline is an end-to-end automated seizure prediction framework with a Fourier transform feature extraction and deep learning-based transformer model, a blend of signal processing and deep learning - this imbibes the potential features to automatically identify the attentive regions in EEG signals for effective screening. The proposed pipeline has demonstrated superior performance on the benchmark dataset with average sensitivity and false-positive rate per hour (FPR/h) as 98.46%, 94.83% and 0.12439, 0, respectively. The proposed work shows great results on the benchmark datasets and a big potential for clinics as a support system with medical experts monitoring the patients.
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Affiliation(s)
- Abhijeet Bhattacharya
- Electrical and Electronics Engineering, Bharati Vidyapeeth's College of Engineering, A-4 Block, Baba Ramdev Marg, Shiva Enclave, Paschim Vihar, New Delhi, 110063, India
| | - Tanmay Baweja
- Electrical and Electronics Engineering, Bharati Vidyapeeth's College of Engineering, A-4 Block, Baba Ramdev Marg, Shiva Enclave, Paschim Vihar, New Delhi, 110063, India
| | - S P K Karri
- Department of Electrical Engineering, National Institute of Technology, Andhra Pradesh, Tadepalligudem - 534101, India
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39
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Wang X, Zhang G, Wang Y, Yang L, Liang Z, Cong F. One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG. Int J Neural Syst 2021; 32:2150048. [PMID: 34635034 DOI: 10.1142/s0129065721500489] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all channels from seizure onset and free zones), were used as the inputs of 1D-CNN for classification, and the patient-specific model was trained. Finally, the channel form with the best classification was selected for each patient. The proposed method was evaluated on the Freiburg Hospital iEEG dataset. In the situation of seizure occurrence period (SOP) of 30[Formula: see text]min and seizure prediction horizon (SPH) of 5[Formula: see text]min, 98.60[Formula: see text] accuracy, 98.85[Formula: see text] sensitivity and 0.01/h false prediction rate (FPR) were achieved. In the situation of SOP of 60[Formula: see text]min and SPH of 5[Formula: see text]min, 98.32[Formula: see text] accuracy, 98.48[Formula: see text] sensitivity and 0.01/h FPR were attained. Compared with the many existing methods using the same iEEG dataset, our method showed a better performance.
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Affiliation(s)
- Xiaoshuang Wang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, P. R. China.,Faculty of Information Technology, University of Jyväskylä, Jyväskylä 40014, Finland
| | - Guanghui Zhang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, P. R. China.,Faculty of Information Technology, University of Jyväskylä, Jyväskylä 40014, Finland
| | - Ying Wang
- Department of Neurology and Psychiatry, First Affiliated Hospital, DaLian Medical University, Dalian, P. R. China
| | - Lin Yang
- Department of Neurology and Psychiatry, First Affiliated Hospital, DaLian Medical University, Dalian, P. R. China
| | - Zhanhua Liang
- Department of Neurology and Psychiatry, First Affiliated Hospital, DaLian Medical University, Dalian, P. R. China
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, P. R. China.,Faculty of Information Technology, University of Jyväskylä, Jyväskylä 40014, Finland.,School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, P. R. China.,Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province Dalian University of Technology, Dalian, P. R. China
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40
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Olamat A, Ozel P, Akan A. Synchronization Analysis In Epileptic EEG Signals Via State Transfer Networks Based On Visibility Graph Technique. Int J Neural Syst 2021; 32:2150041. [PMID: 34583629 DOI: 10.1142/s0129065721500416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy is a persistent and recurring neurological condition in a community of brain neurons that results from sudden and abnormal electrical discharges. This paper introduces a new form of assessment and interpretation of the changes in electroencephalography (EEG) recordings from different brain regions in epilepsy disorders based on graph analysis and statistical rescale range analysis. In this study, two different states of epilepsy EEG data (preictal and ictal phases), obtained from 17 subjects (18 channels each), were analyzed by a new method called state transfer network (STN). The analysis performed by STN yields a network metric called motifs, which are averaged over all channels and subjects in terms of their persistence level in the network. The results showed an increase of overall motif persistence during the ictal over the preictal phase, reflecting the synchronization increase during the seizure phase (ictal). An evaluation of intermotif cross-correlation indicated a definite manifestation of such synchronization. Moreover, these findings are compared with several other well-known methods such as synchronization likelihood (SL), visibility graph similarity (VGS), and global field synchronization (GFS). It is hinted that the STN method is in good agreement with approaches in the literature and more efficient. The most significant contribution of this research is introducing a novel nonlinear analysis technique of generalized synchronization. The STN method can be used for classifying epileptic seizures based on the synchronization changes between multichannel data.
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Affiliation(s)
- Ali Olamat
- Biomedical Engineering Department, Istanbul University, Istanbul, Turkey
| | - Pinar Ozel
- Biomedical Engineering Department, Nevsehir, Hacı Bektas Veli University, Nevsehir, Turkey
| | - Aydin Akan
- Electrical and Electronics Engineering, Department, Izmir University of Economics, Izmir, Turkey
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Zhao Y, Dong C, Zhang G, Wang Y, Chen X, Jia W, Yuan Q, Xu F, Zheng Y. EEG-Based Seizure detection using linear graph convolution network with focal loss. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106277. [PMID: 34315015 DOI: 10.1016/j.cmpb.2021.106277] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/03/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Epilepsy is a clinical phenomenon caused by sudden abnormal and excessive discharge of brain neurons. It affects around 70 million people all over the world. Seizure detection from Electroencephalography (EEG) has achieved rapid development. However, existing methods often extract features from single channel EEG while ignoring the spatial relationship between different EEG channels. To fill this gap, a novel seizure detection model based on linear graph convolution network (LGCN) was proposed to enhance the feature embedding of raw EEG signals during seizure and non-seizure periods. METHOD Pearson correlation matrix of raw EEG signals was calculated to build the input graph of the graph neural network where the coefficients of the matrix models the spatial relations in EEG signals. The last softmax layer makes the final decision (seizure vs. non-seizure). In addition, focal loss was utilized to redefine the loss function of LGCN to deal with the data imbalance problem during seizure detection. RESULTS Experiments are conducted on the CHB-MIT dataset. The seizure detection accuracy, specificity, sensitivity, F1 and Auc are 99.30%, 98.82%, 99.43%, 98.73% and 98.57% respectively. CONCLUSIONS The proposed approach yields superior performance over the-state-of-the-art in seizure detection tasks on the CHB-MIT dataset. Our method works in an end-to-end manner and it does not need manually designed features. The ability to deal with imbalanced data is also attractive in real seizure detection scenarios where the duration of seizures is much shorter than the lasting time of non-seizure events.
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Affiliation(s)
- Yanna Zhao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China.
| | - Changxu Dong
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
| | - Gaobo Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
| | - Yaru Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
| | - Xin Chen
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
| | - Weikuan Jia
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
| | - Qi Yuan
- School of Physics and Electronics, Shandong Normal University, Jinan 250358, P.R. China
| | - Fangzhou Xu
- School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P.R. China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
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42
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Patient-specific method of sleep electroencephalography using wavelet packet transform and Bi-LSTM for epileptic seizure prediction. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102963] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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43
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Chakrabarti S, Swetapadma A, Pattnaik PK. A channel independent generalized seizure detection method for pediatric epileptic seizures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106335. [PMID: 34390934 DOI: 10.1016/j.cmpb.2021.106335] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 07/27/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy the disorder of the central nervous system has its worldwide presence in roughly 50 million people as estimated by the World Health Organization. Electroencephalogram (EEG) is one of the most common and non-invasive ways of analyzing and studying the subtle changes in neuronal activity of the brain during an epileptic seizure attack. These changes can be analyzed for developing an automated system that would assert the chances of an impending seizure. As changeable nature of seizure affects the patients from having a normal life, hence progress in developing new methods will improve the quality of life and also provide assistance in the medical sector. Objective of the proposed method is to avoid EEG channel selection and use all input EEG channel features to design a generalized epileptic seizure detection framework. METHOD In this work, a long short-term memory network has been proposed that is not complex and has the capability of effectively detecting epileptic seizures from both non-invasive and invasive electroencephalogram recordings. The proposed framework is simple and effective and designed in such capacity that raw electroencephalogram signals can be used to detect seizures. Also, a generalized approach has been followed that is channel independent such that EEG signals belonging to any hemisphere of the brain can be detected effectively by the proposed architecture. RESULTS The automated seizure detection system achieved high seizure detection sensitivity of 99.9%, and a low false-positive rate of 0.003 per hour for the Children's Hospital Boston-Massachusetts Institute of Technology dataset. While for the Sleep-Wake-Epilepsy-Center of the University Department of Neurology at the Inselspital Bern dataset, the sensitivity is 99.4% and false-positive rate of 0.006 per hour. Convergence analysis of the proposed model provides a significant amount of reliability and correctness in the efficient detection of epileptic seizures. CONCLUSION Assessment of the proposed framework on non-invasive as well as invasive EEG signals showed that the framework worked well for different type of EEG recordings as different metrics gave satisfactory results. As the framework is simple and did not require any additional parameter optimization techniques, it reduced the processing overheads without affecting the accuracy. Hence, it can be used as an efficient method for monitoring epileptic seizures.
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Affiliation(s)
- Satarupa Chakrabarti
- School of Computer Engineering, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Aleena Swetapadma
- School of Computer Engineering, KIIT University, Bhubaneswar, Odisha 751024, India.
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44
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Xue Y, Jiang P, Neri F, Liang J. A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks. Int J Neural Syst 2021; 31:2150035. [PMID: 34304718 DOI: 10.1142/s0129065721500350] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.
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Affiliation(s)
- Yu Xue
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, P. R. China.,Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, P. R. China
| | - Pengcheng Jiang
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, P. R. China
| | - Ferrante Neri
- COL Laboratory, School of Computer Science, University of Nottingham, Nottingham, UK
| | - Jiayu Liang
- Tianjin Key Laboratory of Autonomous Intelligent Technology and System, Tiangong University, Tianjin, P. R. China
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45
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Amodeo M, Arpaia P, Buzio M, Di Capua V, Donnarumma F. Hysteresis Modeling in Iron-Dominated Magnets Based on a Multi-Layered Narx Neural Network Approach. Int J Neural Syst 2021; 31:2150033. [PMID: 34296651 DOI: 10.1142/s0129065721500337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A full-fledged neural network modeling, based on a Multi-layered Nonlinear Autoregressive Exogenous Neural Network (NARX) architecture, is proposed for quasi-static and dynamic hysteresis loops, one of the most challenging topics for computational magnetism. This modeling approach overcomes drawbacks in attaining better than percent-level accuracy of classical and recent approaches for accelerator magnets, that combine hybridization of standard hysteretic models and neural network architectures. By means of an incremental procedure, different Deep Neural Network Architectures are selected, fine-tuned and tested in order to predict magnetic hysteresis in the context of electromagnets. Tests and results show that the proposed NARX architecture best fits the measured magnetic field behavior of a reference quadrupole at CERN. In particular, the proposed modeling framework leads to a percent error below 0.02% for the magnetic field prediction, thus outperforming state of the art approaches and paving a very promising way for future real time applications.
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Affiliation(s)
- Maria Amodeo
- Department of Electronics and Telecommunications (DET), Polytechnic University of Turin, Turin 10129, Italy.,Instrumentation and Measurement Laboratory for Particle Accelerator Laboratory (IMPALab), Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples 80100, Italy.,Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Pasquale Arpaia
- Instrumentation and Measurement Laboratory for Particle Accelerator Laboratory (IMPALab), Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples 80100, Italy.,Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Marco Buzio
- Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Vincenzo Di Capua
- Instrumentation and Measurement Laboratory for Particle Accelerator Laboratory (IMPALab), Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples 80100, Italy.,Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Francesco Donnarumma
- Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Via San Martino della Battaglia, 44, Rome 00185, Italy
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46
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Nodera H, Matsui M. LSTM Neural Network for Inferring Conduction Velocity Distribution in Demyelinating Neuropathies. Front Neurol 2021; 12:699339. [PMID: 34276548 PMCID: PMC8280291 DOI: 10.3389/fneur.2021.699339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/02/2021] [Indexed: 11/13/2022] Open
Abstract
Waveform analysis of compound muscle action potential (CMAP) is important in the detailed analysis of conduction velocities of each axon as seen in temporal dispersion. This understanding is limited because conduction velocity distribution cannot be easily available from a CMAP waveform. Given the recent advent of artificial intelligence, this study aimed to assess whether conduction velocity (CV) distribution can be inferred from CMAP by the use of deep learning algorithms. Simulated CMAP waveforms were constructed from a single motor unit potential and randomly created CV histograms (n = 12,000). After training the data with various recurrent neural networks (RNNs), CV inference was tested by the network. Among simple RNNs, long short-term memory (LSTM) and gated recurrent unit, the best accuracy and loss profiles, were shown by two-layer bidirectional LSTM, with training and validation accuracies of 0.954 and 0.975, respectively. Training with the use of a recurrent neural network can accurately infer conduction velocity distribution in a wide variety of simulated demyelinating neuropathies. Using deep learning techniques, CV distribution can be assessed in a non-invasive manner.
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Affiliation(s)
- Hiroyuki Nodera
- Department of Neurology, Kanazawa Medical University, Uchinada, Japan
| | - Makoto Matsui
- Department of Neurology, Kanazawa Medical University, Uchinada, Japan
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47
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Ozdemir MA, Cura OK, Akan A. Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning. Int J Neural Syst 2021; 31:2150026. [PMID: 34039254 DOI: 10.1142/s012906572150026x] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.
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Affiliation(s)
- Mehmet Akif Ozdemir
- Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli 35620, Izmir, Turkey
| | - Ozlem Karabiber Cura
- Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli 35620, Izmir, Turkey
| | - Aydin Akan
- Department of Electrical and Electronics Eng., Izmir University of Economics, Balcova 35330, Izmir, Turkey
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48
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Zhao Y, Zhang G, Dong C, Yuan Q, Xu F, Zheng Y. Graph Attention Network with Focal Loss for Seizure Detection on Electroencephalography Signals. Int J Neural Syst 2021; 31:2150027. [PMID: 34003084 DOI: 10.1142/s0129065721500271] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automatic seizure detection from electroencephalogram (EEG) plays a vital role in accelerating epilepsy diagnosis. Previous researches on seizure detection mainly focused on extracting time-domain and frequency-domain features from single electrodes, while paying little attention to the positional correlations between different EEG channels of the same subject. Moreover, data imbalance is common in seizure detection scenarios where the duration of nonseizure periods is much longer than the duration of seizures. To cope with the two challenges, a novel seizure detection method based on graph attention network (GAT) is presented. The approach acts on graph-structured data and takes the raw EEG data as input. The positional relationship between different EEG signals is exploited by GAT. The loss function of the GAT model is redefined using the focal loss to tackle data imbalance problem. Experiments are conducted on the CHB-MIT dataset. The accuracy, sensitivity and specificity of the proposed method are 98.89[Formula: see text], 97.10[Formula: see text] and 99.63[Formula: see text], respectively.
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Affiliation(s)
- Yanna Zhao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Gaobo Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Changxu Dong
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Qi Yuan
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China
| | - Fangzhou Xu
- School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Key Lab of Intelligent Computing and Information Security in Universities of Shandong, Shandong Normal University, Jinan 250358, P. R. China
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49
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Peng P, Xie L, Wei H. A Deep Fourier Neural Network for Seizure Prediction Using Convolutional Neural Network and Ratios of Spectral Power. Int J Neural Syst 2021; 31:2150022. [PMID: 33970057 DOI: 10.1142/s0129065721500222] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods usually adopt handcrafted features and manual parameter setting. The over-reliance on the expertise of specialists may lead to weak exploitation of features and low popularization of clinical application. This paper proposes a novel parameterless patient-specific method based on Fourier Neural Network (FNN), where the Fourier transform and backpropagation learning are synthesized to make the predictor more efficient and practical. The employment of FNN is the first attempt in the field of seizure prediction due to its automatic extraction of immanent spectra in epileptic signals. Despite the self-adaptive superiority of FNN, we introduce Convolutional Neural Network (CNN) to further improve its search capability in high-dimensional feature spaces. The study also develops a multi-layer module to estimate spectral power ratios of raw recordings, which optimizes the prediction by enhancing feature diversity. Based on these modules, this paper proposes a two-channel deep neural network: Fourier Ratio Convolutional Neural Network (FRCNN). To demonstrate the reliability of the model, we explain the mathematical meaning of hidden-layer neurons in FRCNN theoretically. This approach is evaluated on both intracranial and scalp EEG datasets. It shows that the predictor achieved a sensitivity of 91.2% and a false prediction rate (FPR) of 0.06[Formula: see text]h[Formula: see text] across intracranial subjects and a sensitivity of 85.4% and an FPR of 0.14[Formula: see text]h[Formula: see text] over scalp subjects. The results indicate that FRCNN enables the convenience of epilepsy treatments while preserving a high degree of precision. In the end, a detailed comparison with the previous methods demonstrates that FRCNN has achieved higher performance and generalization ability.
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Affiliation(s)
- Peizhen Peng
- Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, P. R. China
| | - Liping Xie
- Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, P. R. China
| | - Haikun Wei
- Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, P. R. China
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50
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Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-09986-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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