1
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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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2
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Paliwal V, Das K, Doesburg SM, Medvedev G, Xi P, Ribary U, Pachori RB, Vakorin VA. Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2038-2048. [PMID: 38768007 DOI: 10.1109/tnsre.2024.3403198] [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: 05/22/2024]
Abstract
Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifying long multivariate time series, optimal prediction models and feature extraction methods for EEG classification remain elusive. Our study addressed the problem of EEG classification under the framework of brain age prediction, applying a deep learning model on EEG time series. We hypothesized that decomposing EEG signals into oscillatory modes would yield more accurate age predictions than using raw or canonically frequency-filtered EEG. Specifically, we employed multivariate intrinsic mode functions (MIMFs), an empirical mode decomposition (EMD) variant based on multivariate iterative filtering (MIF), with a convolutional neural network (CNN) model. Testing a large dataset of routine clinical EEG scans (n = 6540) from patients aged 1 to 103 years, we found that an ad-hoc CNN model without fine-tuning could reasonably predict brain age from EEGs. Crucially, MIMF decomposition significantly improved performance compared to canonical brain rhythms (from delta to lower gamma oscillations). Our approach achieved a mean absolute error (MAE) of 13.76 ± 0.33 and a correlation coefficient of 0.64 ± 0.01 in brain age prediction over the entire lifespan. Our findings indicate that CNN models applied to EEGs, preserving their original temporal structure, remains a promising framework for EEG classification, wherein the adaptive signal decompositions such as the MIF can enhance CNN models' performance in this task.
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3
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Jamil M, Aziz MZ, Yu X. Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study. Biomed Phys Eng Express 2024; 10:045023. [PMID: 38599183 DOI: 10.1088/2057-1976/ad3cde] [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: 11/29/2023] [Accepted: 04/10/2024] [Indexed: 04/12/2024]
Abstract
Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms. Continuous wavelet transform (CWT), empirical Fourier decomposition (EFD), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) were exploited for the acquisition of 2D scalograms from 1D data. The research evaluates the effect of multiple factors, including noisy versus denoised scalograms, different optimizers, learning rates, single versus dual channels, model size, and computational time consumption. The benchmark Bern-Barcelona EEG dataset is used for testing purpose. Results obtained show that the combination of MobileNetV2, Continuous Wavelet Transform (CWT) and Adam optimizer at a learning rate of 10-4, coupled with dual-data channels, provides the best performance metrics. Specifically, these parameters result in optimal sensitivity, specificity, f1-score, and classification accuracy, with respective values of 96.06%, 96.15%, 96.08%, and 96.10%. To further corroborate the efficacy of opted pretrained models on exploited Signal Decomposition (SD) algorithms, the classifiers are also being simulated on Temple University database at pinnacle modeling composition. A similar pattern in the outcome readily validate the findings of our study and robustness of deep learning models on epilepsy EEG scalograms.The conclusions drawn emphasize the potential of pretrained CNN-based models to create a robust, automated system for diagnosing epileptiform. Furthermore, the study offers insights into the effectiveness of varying time-frequency techniques and classifier parameters for classifying epileptic EEG data.
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Affiliation(s)
- Mudasir Jamil
- School of Automation, Northwestern Polytechnical University, Xi'an, 710000, People's Republic of China
| | - Muhammad Zulkifal Aziz
- School of Automation, Northwestern Polytechnical University, Xi'an, 710000, People's Republic of China
| | - Xiaojun Yu
- School of Automation, Northwestern Polytechnical University, Xi'an, 710000, People's Republic of China
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4
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Ali E, Angelova M, Karmakar C. Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives. ROYAL SOCIETY OPEN SCIENCE 2024; 11:230601. [PMID: 39076791 PMCID: PMC11286169 DOI: 10.1098/rsos.230601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 11/23/2023] [Accepted: 03/28/2024] [Indexed: 07/31/2024]
Abstract
Epilepsy is a life-threatening neurological condition. Manual detection of epileptic seizures (ES) is laborious and burdensome. Machine learning techniques applied to electroencephalography (EEG) signals are widely used for automatic seizure detection. Some key factors are worth considering for the real-world applicability of such systems: (i) continuous EEG data typically has a higher class imbalance; (ii) higher variability across subjects is present in physiological signals such as EEG; and (iii) seizure event detection is more practical than random segment detection. Most prior studies failed to address these crucial factors altogether for seizure detection. In this study, we intend to investigate a generalized cross-subject seizure event detection system using the continuous EEG signals from the CHB-MIT dataset that considers all these overlooked aspects. A 5-second non-overlapping window is used to extract 92 features from 22 EEG channels; however, the most significant 32 features from each channel are used in experimentation. Seizure classification is done using a Random Forest (RF) classifier for segment detection, followed by a post-processing method used for event detection. Adopting all the above-mentioned essential aspects, the proposed event detection system achieved 72.63% and 75.34% sensitivity for subject-wise 5-fold and leave-one-out analyses, respectively. This study presents the real-world scenario for ES event detectors and furthers the understanding of such detection systems.
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Affiliation(s)
- Emran Ali
- School of Information Technology, Deakin University, Melbourne Burwood Campus, Melbourne, Victoria3125, Australia
| | - Maia Angelova
- School of Information Technology, Deakin University, Melbourne Burwood Campus, Melbourne, Victoria3125, Australia
- Aston Digital Futures Institute, EPS, Aston University, Birmingham, UK
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Chandan Karmakar
- School of Information Technology, Deakin University, Melbourne Burwood Campus, Melbourne, Victoria3125, Australia
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5
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Ju X, Li M, Tian W, Hu D. EEG-based emotion recognition using a temporal-difference minimizing neural network. Cogn Neurodyn 2024; 18:405-416. [PMID: 38699602 PMCID: PMC11061074 DOI: 10.1007/s11571-023-10004-w] [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: 02/10/2023] [Revised: 07/25/2023] [Accepted: 08/21/2023] [Indexed: 05/05/2024] Open
Abstract
Electroencephalogram (EEG) emotion recognition plays an important role in human-computer interaction. An increasing number of algorithms for emotion recognition have been proposed recently. However, it is still challenging to make efficient use of emotional activity knowledge. In this paper, based on prior knowledge that emotion varies slowly across time, we propose a temporal-difference minimizing neural network (TDMNN) for EEG emotion recognition. We use maximum mean discrepancy (MMD) technology to evaluate the difference in EEG features across time and minimize the difference by a multibranch convolutional recurrent network. State-of-the-art performances are achieved using the proposed method on the SEED, SEED-IV, DEAP and DREAMER datasets, demonstrating the effectiveness of including prior knowledge in EEG emotion recognition.
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Affiliation(s)
- Xiangyu Ju
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Ming Li
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Wenli Tian
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
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6
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Qi N, Piao Y, Zhang H, Wang Q, Wang Y. Seizure prediction based on improved vision transformer model for EEG channel optimization. Comput Methods Biomech Biomed Engin 2024:1-12. [PMID: 38449110 DOI: 10.1080/10255842.2024.2326097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 02/24/2024] [Indexed: 03/08/2024]
Abstract
Epileptic seizures are unpredictable events caused by abnormal discharges of a patient's brain cells. Extensive research has been conducted to develop seizure prediction algorithms based on long-term continuous electroencephalogram (EEG) signals. This paper describes a patient-specific seizure prediction method that can serve as a basis for the design of lightweight, wearable and effective seizure-prediction devices. We aim to achieve two objectives using this method. The first aim is to extract robust feature representations from multichannel EEG signals, and the second aim is to reduce the number of channels used for prediction by selecting an optimal set of channels from multichannel EEG signals while ensuring good prediction performance. We design a seizure-prediction algorithm based on a vision transformer (ViT) model. The algorithm selects channels that play a key role in seizure prediction from 22 channels of EEG signals. First, we perform a time-frequency analysis of processed time-series signals to obtain EEG spectrograms. We then segment the spectrograms of multiple channels into many non-overlapping patches of the same size, which are input into the channel selection layer of the proposed model, named Sel-JPM-ViT, enabling it to select channels. Application of the Sel-JPM-ViT model to the Boston Children's Hospital-Massachusetts Institute of Technology scalp EEG dataset yields results using only three to six channels of EEG signals that are slightly better that the results obtained using 22 channels of EEG signals. Overall, the Sel-JPM-ViT model exhibits an average classification accuracy of 93.65%, an average sensitivity of 94.70% and an average specificity of 92.78%.
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Affiliation(s)
- Nan Qi
- Department of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China
| | - Yan Piao
- Department of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China
| | - Hao Zhang
- Department of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China
| | - Qi Wang
- Department of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China
| | - Yue Wang
- Department of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China
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7
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Liao J, Wang J, Zhan CA, Yang F. Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection. Phys Eng Sci Med 2024; 47:31-47. [PMID: 37747646 DOI: 10.1007/s13246-023-01340-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 09/14/2023] [Indexed: 09/26/2023]
Abstract
Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms. We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score. Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity. EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.
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Affiliation(s)
- Jiahui Liao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Chang'an A Zhan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
| | - Feng Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
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8
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Wang Z, Liu F, Shi S, Xia S, Peng F, Wang L, Ai S, Xu Z. Automatic epileptic seizure detection based on persistent homology. Front Physiol 2023; 14:1227952. [PMID: 38192741 PMCID: PMC10773586 DOI: 10.3389/fphys.2023.1227952] [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: 05/24/2023] [Accepted: 11/28/2023] [Indexed: 01/10/2024] Open
Abstract
Epilepsy is a prevalent brain disease, which is quite difficult-to-treat or cure. This study developed a novel automatic seizure detection method based on the persistent homology method. In this study, a Vietoris-Rips (VR) complex filtration model was constructed based on the EEG data. And the persistent homology method was applied to calculate the VR complex filtration barcodes to describe the topological changes of EEG recordings. Afterward, the barcodes as the topological characteristics of EEG signals were fed into the GoogLeNet for classification. The persistent homology is applicable for multi-channel EEG data analysis, where the global topological information is calculated and the features are extracted by considering the multi-channel EEG data as a whole, without the multiple calculations or the post-stitching. Three databases were used to evaluate the proposed approach and the results showed that the approach had high performances in the epilepsy detection. The results obtained from the CHB-MIT Database recordings revealed that the proposed approach can achieve a segment-based averaged accuracy, sensitivity and specificity values of 97.05%, 96.71% and 97.38%, and achieve an event-based averaged sensitivity value of 100% with 1.22 s average detection latency. In addition, on the Siena Scalp Database, the proposed method yields averaged accuracy, sensitivity and specificity values of 96.42%, 95.23% and 97.6%. Multiple tasks of the Bonn Database also showed achieved accuracy of 99.55%, 98.63%, 98.28% and 97.68%, respectively. The experimental results on these three EEG databases illustrate the efficiency and robustness of our approach for automatic detection of epileptic seizure.
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Affiliation(s)
- Ziyu Wang
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Feifei Liu
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Shuhua Shi
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Shengxiang Xia
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Fulai Peng
- Medical Rehabilitation Research Center, Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China
| | - Lin Wang
- The Fifth People’s Hospital of Jinan, Jinan, China
| | - Sen Ai
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Zheng Xu
- School of Science, Shandong Jianzhu University, Jinan, China
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9
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Wong S, Simmons A, Rivera-Villicana J, Barnett S, Sivathamboo S, Perucca P, Kwan P, Kuhlmann L, Vasa R, O'Brien TJ. EEG based automated seizure detection - A survey of medical professionals. Epilepsy Behav 2023; 149:109518. [PMID: 37952416 DOI: 10.1016/j.yebeh.2023.109518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/29/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023]
Abstract
Diagnosing and managing seizures presents substantial challenges for clinicians caring for patients with epilepsy. Although machine learning (ML) has been proposed for automated seizure detection using EEG data, there is little evidence of these technologies being broadly adopted in clinical practice. Moreover, there is a noticeable lack of surveys investigating this topic from the perspective of medical practitioners, which limits the understanding of the obstacles for the development of effective automated seizure detection. Besides the issue of generalisability and replicability seen in a small amount of studies, obstacles to the adoption of automated seizure detection remain largely unknown. To understand the obstacles preventing the application of seizure detection tools in clinical practice, we conducted a survey targeting medical professionals involved in the management of epilepsy. Our study aimed to gather insights on various factors such as the clinical utility, professional sentiment, benchmark requirements, and perceived barriers associated with the use of automated seizure detection tools. Our key findings are: I) The minimum acceptable sensitivity reported by most of our respondents (80%) seems achievable based on studies reported from most currently available ML-based EEG seizure detection algorithms, but replication studies often fail to meet this minimum. II) Respondents are receptive to the adoption of ML seizure detection tools and willing to spend time in training. III) The top three barriers for usage of such tools in clinical practice are related to availability, lack of training, and the blackbox nature of ML algorithms. Based on our findings, we developed a guide that can serve as a basis for developing ML-based seizure detection tools that meet the requirements of medical professionals, and foster the integration of these tools into clinical practice.
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Affiliation(s)
- Sheng Wong
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia.
| | - Anj Simmons
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | | | - Scott Barnett
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Shobi Sivathamboo
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Piero Perucca
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia; Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, Victoria, Australia; Bladin-Berkovic Comprehensive Epilepsy Program, Austin Health, Heidelberg, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia; Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Rajesh Vasa
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
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10
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Wang Z, Zhang W, Li S, Chen X, Wu D. Unsupervised domain adaptation for cross-patient seizure classification. J Neural Eng 2023; 20:066002. [PMID: 37906968 DOI: 10.1088/1741-2552/ad0859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/31/2023] [Indexed: 11/02/2023]
Abstract
Objective. Epileptic seizure is a chronic neurological disease affecting millions of patients. Electroencephalogram (EEG) is the gold standard in epileptic seizure classification. However, its low signal-to-noise ratio, strong non-stationarity, and large individual difference nature make it difficult to directly extend the seizure classification model from one patient to another. This paper considers multi-source unsupervised domain adaptation for cross-patient EEG-based seizure classification, i.e. there are multiple source patients with labeled EEG data, which are used to label the EEG trials of a new patient.Approach. We propose an source domain selection (SDS)-global domain adaptation (GDA)-target agent subdomain adaptation (TASA) approach, which includes SDS to filter out dissimilar source domains, GDA to align the overall distributions of the selected source domains and the target domain, and TASA to identify the most similar source domain to the target domain so that its labels can be utilized.Main results. Experiments on two public seizure datasets demonstrated that SDS-GDA-TASA outperformed 13 existing approaches in unsupervised cross-patient seizure classification.Significance. Our approach could save clinicians plenty of time in labeling EEG data for epilepsy patients, greatly increasing the efficiency of seizure diagnostics.
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Affiliation(s)
- Ziwei Wang
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Wen Zhang
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Siyang Li
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Xinru Chen
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Dongrui Wu
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
- Belt and Road Joint Laboratory on Measurement and Control Technology, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
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11
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Djemili R, Djemili I. Nonlinear and chaos features over EMD/VMD decomposition methods for ictal EEG signals detection. Comput Methods Biomech Biomed Engin 2023:1-20. [PMID: 37861376 DOI: 10.1080/10255842.2023.2271603] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/08/2023] [Indexed: 10/21/2023]
Abstract
The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence.
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Affiliation(s)
| | - Ilyes Djemili
- Lab. Electrotech, Université 20 Août, Skikda, Algeria
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12
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Liu S, Wang J, Li S, Cai L. Epileptic Seizure Detection and Prediction in EEGs Using Power Spectra Density Parameterization. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3884-3894. [PMID: 37725738 DOI: 10.1109/tnsre.2023.3317093] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
Power spectrum analysis is one of the effective tools for classifying epileptic signals based on electroencephalography (EEG) recordings. However, the conflation of periodic and aperiodic components within the EEG may presents an obstacle to epilepsy detection or prediction. In this paper, we explored the significance of the periodic and aperiodic components of the EEG power spectrum for the detection and prediction of epilepsy respectively. We use a power spectrum density parameterization method to separate the periodic and aperiodic components of the signals, and validate their roles in epilepsy detection and prediction on two public datasets. The average classification accuracy of the periodic and aperiodic components for 10 clinical tasks on the Bonn EEG database were 73.9% and 96.68%, respectively, and increases to 98.88% when combined. For 22 patients on the CHB-MIT Long-term EEG database, the combined features achieve an average detection accuracy of 99.95% and successfully predict all seizures with low false prediction rates. We conclude that both the periodic and aperiodic components of the EEG power spectrum contributed to discriminating different stages of epilepsy, but the aperiodic neural activity played a decisive role in classification. This discovery has significant implications for diagnosing epileptic seizures and providing personalized brain activity information to improve the accuracy and efficiency of epilepsy detection.
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Tripathi PM, Kumar A, Kumar M, Komaragiri RS. Automatic seizure detection and classification using super-resolution superlet transform and deep neural network -A preprocessing-less method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107680. [PMID: 37459774 DOI: 10.1016/j.cmpb.2023.107680] [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: 02/15/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 08/29/2023]
Abstract
CONTEXT Epilepsy, characterized by recurrent seizures, is a chronic brain disease that affects approximately 50 million. Recurrent seizures characterize it. A seizure, a burst of uncontrolled electrical activity between brain cells, results in temporary changes in behavior, level of consciousness, and involuntary movements. An accurate prediction of seizures can improve the standard of living in epileptic subjects. The increasing capabilities of machine learning and computer-assisted devices can detect seizures accurately with minimal human intervention. PROPOSED APPROACH This paper proposes a method to detect seizure and non-seizure events using superlet transform (SLT) and a deep convolution neural network: VGG-19. The electroencephalogram (EEG) dataset from the University of Bonn is used to validate the efficacy of the proposed method. METHODOLOGY SLT, a high-resolution time-frequency technique, converts EEG records into two-dimensional (2-D) images. SLT provides a high-resolution time-frequency representation reflecting the oscillation bursts in an EEG record. The time-frequency representations as 2-D images are fed to a pre-trained convolutional neural network: VGG-19. The last layers of VGG-19 are replaced with new layers to accommodate the different classification problems. RESULTS The proposed method achieved an accuracy of 100% for all seven seizure and non-seizure detection cases considered in this work. In the case of three and five-class classification problems, the proposed method has better accuracy than other existing methods. The CHB-MIT scalp EEG database is also used to assess the effectiveness of the proposed method, which achieved a classification accuracy of 94.3% in distinguishing between seizure and non-seizure events. CONCLUSION The results obtained using the proposed methodology show the efficacy of the proposed method in accurately detecting seizures and other brain activity with the least pre-processing and human involvement. The proposed method can assist medical practitioners by saving their effort and time.
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Affiliation(s)
- Prashant Mani Tripathi
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Ashish Kumar
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - Manjeet Kumar
- Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India.
| | - Rama S Komaragiri
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
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Statsenko Y, Babushkin V, Talako T, Kurbatova T, Smetanina D, Simiyu GL, Habuza T, Ismail F, Almansoori TM, Gorkom KNV, Szólics M, Hassan A, Ljubisavljevic M. Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach. Biomedicines 2023; 11:2370. [PMID: 37760815 PMCID: PMC10525492 DOI: 10.3390/biomedicines11092370] [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: 06/05/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 09/29/2023] Open
Abstract
Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.
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Affiliation(s)
- Yauhen Statsenko
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Medical Imaging Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain P.O. Box 15551, United Arab Emirates
- Big Data Analytics Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Vladimir Babushkin
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Tatsiana Talako
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Department of Oncohematology, Minsk Scientific and Practical Center for Surgery, Transplantology and Hematology, 220089 Minsk, Belarus
| | - Tetiana Kurbatova
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Darya Smetanina
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Gillian Lylian Simiyu
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Tetiana Habuza
- Big Data Analytics Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Fatima Ismail
- Pediatric Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Taleb M. Almansoori
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Klaus N.-V. Gorkom
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Miklós Szólics
- Neurology Division, Medicine Department, Tawam Hospital, Al Ain P.O. Box 15258, United Arab Emirates
- Internal Medicine Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Ali Hassan
- Neurology Division, Medicine Department, Tawam Hospital, Al Ain P.O. Box 15258, United Arab Emirates
| | - Milos Ljubisavljevic
- Physiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates;
- Neuroscience Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain P.O. Box 15551, United Arab Emirates
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Wang C, Liu L, Zhuo W, Xie Y. An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 12:22-31. [PMID: 38059126 PMCID: PMC10697289 DOI: 10.1109/jtehm.2023.3308196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 07/17/2023] [Accepted: 08/19/2023] [Indexed: 12/08/2023]
Abstract
OBJECTIVE Epilepsy, an enduring neurological disorder, afflicts approximately 65 million individuals globally, significantly impacting their physical and mental wellbeing. Traditional epilepsy detection methods are labor-intensive, leading to inefficiencies. Although deep learning techniques for brain signal detection have gained traction in recent years, their clinical application advancement is hindered by the significant requirement for high-quality data and computational resources during training. METHODS & RESULTS The neural network training initially involved merging two datasets of different data quality, namely Bonn University datasets and CHB-MIT datasets, to bolster its generalization capabilities. To tackle the issues of dataset size and class imbalance, we employed small window segmentation and Synthetic Minority Over-sampling Technique (SMOTE). algorithms to augment and equalize the data. A streamlined neural network architecture was then proposed, drastically reducing the model's training parameters. Notably, a model trained with a mere 9,371 parameters yielded impressive results. The three-classification task on the combined dataset delivered an accuracy of 98.52%, sensitivity of 97.99%, specificity of 99.35%, and precision of 98.44%. CONCLUSION The experimental findings of this study underscore the superiority of the proposed method over existing approaches in both model size reduction and accuracy enhancement. As a result, it is more apt for deployment in low-cost, low computational hardware devices, including wearable technology, and various clinical applications. Clinical and Translational Impact Statement- This study is a Pre-Clinical Research. The lightweight neural network is easily deployed on hardware device for real-time epileptic EEG detection.
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Affiliation(s)
- Chenlong Wang
- School of AutomationGuangdong University of TechnologyGuangzhou523083China
| | - Lei Liu
- School of AutomationGuangdong University of TechnologyGuangzhou523083China
| | - Wenhai Zhuo
- School of AutomationGuangdong University of TechnologyGuangzhou523083China
| | - Yun Xie
- School of AutomationGuangdong University of TechnologyGuangzhou523083China
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Zhang X, Dong S, Shen Q, Zhou J, Min J. Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition. Front Neuroinform 2023; 17:1205529. [PMID: 37692360 PMCID: PMC10483404 DOI: 10.3389/fninf.2023.1205529] [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: 04/14/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
Introduction Intelligent recognition of electroencephalogram (EEG) signals can remarkably improve the accuracy of epileptic seizure prediction, which is essential for epileptic diagnosis. Extreme learning machine (ELM) has been applied to EEG signals recognition, however, the artifacts and noises in EEG signals have a serious effect on recognition efficiency. Deep learning is capable of noise resistance, contributing to removing the noise in raw EEG signals. But traditional deep networks suffer from time-consuming training and slow convergence. Methods Therefore, a novel deep learning based ELM (denoted as DELM) motivated by stacking generalization principle is proposed in this paper. Deep extreme learning machine (DELM) is a hierarchical network composed of several independent ELM modules. Augmented EEG knowledge is taken as complementary component, which will then be mapped into next module. This learning process is so simple and fast, meanwhile, it can excavate the implicit knowledge in raw data to a greater extent. Additionally, the proposed method is operated in a single-direction manner, so there is no need to perform parameters fine-tuning, which saves the expense of time. Results Extensive experiments are conducted on the public Bonn EEG dataset. The experimental results demonstrate that compared with the commonly-used seizure prediction methods, the proposed DELM wins the best average accuracies in 13 out of the 22 data and the best average F-measure scores in 10 out of the 22 data. And the running time of DELM is more than two times quickly than deep learning methods. Discussion Therefore, DELM is superior to traditional and some state-of-the-art machine learning methods. The proposed architecture demonstrates its feasibility and superiority in epileptic EEG signal recognition. The proposed less computationally intensive deep classifier enables faster seizure onset detection, which is showing great potential on the application of real-time EEG signal classification.
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Affiliation(s)
- Xiongtao Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Shuai Dong
- School of Information Engineering, Huzhou University, Huzhou, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Qing Shen
- School of Information Engineering, Huzhou University, Huzhou, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Jie Zhou
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, China
| | - Jingjing Min
- Department of Neurology, The First People's Hospital of Huzhou, First Affiliated Hospital of Huzhou University, Huzhou, China
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Geng D, An Q, Fu Z, Wang C, An H. Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening. Comput Biol Med 2023; 162:107060. [PMID: 37290394 PMCID: PMC10229199 DOI: 10.1016/j.compbiomed.2023.107060] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/28/2023] [Accepted: 05/20/2023] [Indexed: 06/10/2023]
Abstract
With the COVID-19 pandemic causing challenges in hospital admissions globally, the role of home health monitoring in aiding the diagnosis of mental health disorders has become increasingly important. This paper proposes an interpretable machine learning solution to optimise initial screening for major depressive disorder (MDD) in both male and female patients. The data is from the Stanford Technical Analysis and Sleep Genome Study (STAGES). We analyzed 5-min short-term electrocardiogram (ECG) signals during nighttime sleep stages of 40 MDD patients and 40 healthy controls, with a 1:1 gender ratio. After preprocessing, we calculated the time-frequency parameters of heart rate variability (HRV) based on the ECG signals and used common machine learning algorithms for classification, along with feature importance analysis for global decision analysis. Ultimately, the Bayesian optimised extremely randomized trees classifier (BO-ERTC) showed the best performance on this dataset (accuracy 86.32%, specificity 86.49%, sensitivity 85.85%, F1-score 0.86). By using feature importance analysis on the cases confirmed by BO-ERTC, we found that gender is one of the most important factors affecting the prediction of the model, which should not be overlooked in our assisted diagnosis. This method can be embedded in portable ECG monitoring systems and is consistent with the literature results.
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Affiliation(s)
- Duyan Geng
- Hebei University of Technology, School of Electrical Engineering, State Key Laboratory of Reliability and Intelligence of Electrical Equipment Co-constructed by Province and Ministry, Tianjin, 300400, China; Hebei Key Laboratory of Electromagnetic Field and Electrical Reliability, School of Electrical Engineering, Hebei University of Technology, Tianjin, 300400, China.
| | - Qiang An
- Hebei University of Technology, School of Life Science and Health Engineering, Tianjin, 300130, China
| | - Zhigang Fu
- Physical Examination Centre, The 983 Hospital of Joint Logistics Support Force of the Chinese People's Liberation Army, Tianjin, China
| | - Chao Wang
- Hebei University of Technology, School of Life Science and Health Engineering, Tianjin, 300130, China
| | - Hongxia An
- Hebei University of Technology, School of Life Science and Health Engineering, Tianjin, 300130, China
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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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19
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Pre-processed and combined EEG data for epileptic seizure classification using deep learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
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20
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EEG channel selection-based binary particle swarm optimization with recurrent convolutional autoencoder for emotion recognition. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
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21
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Quan J, Li Y, Wang L, He R, Yang S, Guo L. EEG-based cross-subject emotion recognition using multi-source domain transfer learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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22
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Rukhsar S, Tiwari AK. Barnes–Hut approximation based accelerating t-SNE for seizure detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
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23
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Pandey A, Singh SK, Udmale SS, Shukla K. An intelligent optimized deep learning model to achieve early prediction of epileptic seizures. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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Effective Epileptic Seizure Detection by Classifying Focal and Non-focal EEG Signals using Human Learning Optimization-based Hidden Markov Model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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Maher C, Yang Y, Truong ND, Wang C, Nikpour A, Kavehei O. Seizure detection with reduced electroencephalogram channels: research trends and outlook. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230022. [PMID: 37153360 PMCID: PMC10154941 DOI: 10.1098/rsos.230022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 04/11/2023] [Indexed: 05/09/2023]
Abstract
Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.
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Affiliation(s)
- Christina Maher
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Yikai Yang
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
- Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2050, Australia
- Sydney Neuroimaging Analysis Centre, Camperdown, New South Wales 2050, Australia
| | - Armin Nikpour
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2006, Australia
- Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
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Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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27
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Goshvarpour A, Goshvarpour A. An Innovative Information-Based Strategy for Epileptic EEG Classification. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11253-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2023]
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Patel RK, Kashyap M. Machine learning- based lung disease diagnosis from CT images using Gabor features in Littlewood Paley empirical wavelet transform (LPEWT) and LLE. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2023. [DOI: 10.1080/21681163.2023.2187244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Affiliation(s)
- Rajneesh Kumar Patel
- Department of Electronics & Communication, Maulana Azad National Institute of Technology, Bhopal (M.P.), India
| | - Manish Kashyap
- Department of Electronics & Communication, Maulana Azad National Institute of Technology, Bhopal (M.P.), India
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Wong S, Simmons A, Rivera-Villicana J, Barnett S, Sivathamboo S, Perucca P, Ge Z, Kwan P, Kuhlmann L, Vasa R, Mouzakis K, O'Brien TJ. EEG datasets for seizure detection and prediction- A review. Epilepsia Open 2023. [PMID: 36740244 DOI: 10.1002/epi4.12704] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 01/28/2023] [Indexed: 02/07/2023] Open
Abstract
Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm.
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Affiliation(s)
- Sheng Wong
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Anj Simmons
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | | | - Scott Barnett
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Shobi Sivathamboo
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Piero Perucca
- Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia.,Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia.,Comprehensive Epilepsy Program, Austin Health, Heidelberg, Victoria, Australia
| | - Zongyuan Ge
- Monash eResearch Centre, Monash University, Clayton, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Rajesh Vasa
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Kon Mouzakis
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Terence J O'Brien
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
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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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31
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Shayeste H, Asl BM. Automatic seizure detection based on Gray Level Co-occurrence Matrix of STFT imaged-EEG. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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32
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Amiri M, Aghaeinia H, Amindavar HR. Automatic epileptic seizure detection in EEG signals using sparse common spatial pattern and adaptive short-time Fourier transform-based synchrosqueezing transform. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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He S, Li Y, Le X, Han X, Lin J, Peng X, Li M, Yang R, Yao D, Valdes-Sosa PA, Ren P. Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2023; 31:1764-1775. [PMID: 37030736 DOI: 10.1109/tnsre.2023.3260143] [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: 03/29/2023]
Abstract
OBJECTIVE Multivariate signal (MS) analysis, especially the assessment of its information transmission (for example, from the perspective of network science), is the key to our understanding of various phenomena in biology, physics and economics. Although there is a large amount of literature demonstrating that MS can be decomposed into space-time-frequency domain information, there seems to be no research confirming that multivariate information transmission (MIT) in these three domains can be quantified. Therefore, in this study, we attempted to combine dynamic mode decomposition (DMD) and parallel communication model (PCM) together to realize it. METHODS We first regarded MS as a large-scale system and then used DMD to decompose it into specific subsystems with their own intrinsic oscillatory frequencies. At the same time, the transition probability matrix (TPM) of information transmission within and between MS at two consecutive moments in each subsystem can also be calculated. Then, communication parameters (CPs) derived from each TPM were calculated in order to quantify the MIT in the space-time-frequency domain. In this study, multidimensional electroencephalogram (EEG) signals were used to illustrate our method. RESULTS Compared with traditional EEG brain networks, this method shows greater potential in EEG analysis to distinguish between patients and healthy controls. CONCLUSION This study demonstrates the feasibility of measuring MIT in the space-time-frequency domain simultaneously. SIGNIFICANCE This study shows that MIT analysis in the space-time-frequency domain is not only completely different from the MS decomposition in these three domains, but also can reveal many new phenomena behind MS that have not yet been discovered.
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Combining the neural mass model and Hodgkin–Huxley formalism: Neuronal dynamics modelling. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104026] [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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Lin Y, Wing-Kuen Ling B, Wang W, Hu L, Xu N, Zhou X. Fusion of electroencephalograms at different channels and different activities via multivariate quaternion valued singular spectrum analysis for intellectual and developmental disorder recognition. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Zhang T, Chen W, Chen X. Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D)2PCA and KELM. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals-A Systematic Literature Review. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120781. [PMID: 36550987 PMCID: PMC9774931 DOI: 10.3390/bioengineering9120781] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 12/13/2022]
Abstract
Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.
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Akbari H, Sadiq MT, Siuly S, Li Y, Wen P. Identification of normal and depression EEG signals in variational mode decomposition domain. Health Inf Sci Syst 2022; 10:24. [PMID: 36061530 PMCID: PMC9437202 DOI: 10.1007/s13755-022-00187-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/29/2022] [Indexed: 10/14/2022] Open
Abstract
Early detection of depression is critical in assisting patients in receiving the best therapy possible to avoid negative repercussions. Depression detection using electroencephalogram (EEG) signals is a simple, low-cost, convenient, and accurate approach. This paper proposes a six-stage novel method for detecting depression using EEG signals. First, EEG signals are recorded from 44 subjects, with 22 subjects being normal and 22 subjects being depressed. Second, a simple notch filter with EEG signals differencing approach is employed for effective preprocessing. Third, the variational mode decomposition (VMD) approach is implemented for nonlinear and non-stationary EEG signals analysis, resulting in many modes. Fourth, mutual information-based novel modes selection criterion is proposed to select the most informative modes. In the fifth step, a combination of linear and nonlinear features are extracted from selected modes and at last, classification is performed with neural networks. In this study, a novel single feature is also proposed, which is made using Log energy, norm entropies and fluctuation index, which delivers 100% classification accuracy, sensitivity and specificity. By using these features, a novel depression diagnostic index is also proposed. This integrated index would assist in quicker and more objective identification of normal and depression EEG signals. The proposed computerized framework and the DDI can help health workers, large enterprises, and product developers build a real-time system.
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Affiliation(s)
- Hesam Akbari
- Department of Biomedical Engineering, Islamic Azad University, Tehran, 1584715414 Iran
| | - Muhammad Tariq Sadiq
- School of Architecture, Technology and Engineering, University of Brighton, Brighton, BN2 4AT UK
| | - Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, 3011 Australia
| | - Yan Li
- School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba Campus, 4350 Australia
| | - Paul Wen
- School of Engineering, Victoria University, Melbourne, University of Southern Queensland, Toowoomba Campus, 4350 Australia
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Zanetti R, Pale U, Teijeiro T, Atienza D. Approximate zero-crossing: a new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection. J Neural Eng 2022; 19. [PMID: 36356314 DOI: 10.1088/1741-2552/aca1e4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 11/10/2022] [Indexed: 11/12/2022]
Abstract
Objective. Long-term monitoring of people with epilepsy based on electroencephalography (EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for personalised epilepsy treatment. More specifically, in outpatient settings, the available solutions are not satisfactory either due to poor classification performance or high complexity to be executed in resource-constrained devices (e.g. wearable systems). Therefore, we hypothesize that obtaining high discriminative features is the main avenue to improve low-complexity seizure-detection algorithms.Approach. Inspired by how neurologists recognize ictal EEG data, and to tackle this problem by targeting resource-constrained wearable devices, we introduce a new interpretable and highly discriminative feature for EEG and iEEG, namely approximate zero-crossing (AZC). We obtain AZC by applying a polygonal approximation to mimic how our brain selects prominent patterns among noisy data and then using a zero-crossing count as a measure of the dominating frequency. By employing Kullback-Leiber divergence, leveraging CHB-MIT and SWEC-ETHZ iEEG datasets, we compare the AZC discriminative power against a set of 56 classical literature features (CLF). Moreover, we assess the performances of a low-complexity seizure detection method using only AZC features versus employing the CLF set.Main results. Three AZC features obtained with different approximation thresholds are among the five with the highest median discriminative power. Moreover, seizure classification based on only AZC features outperforms an equivalent CLF-based method. The former detects 102 and 194 seizures, against 99 and 161 for the latter (CHB-MIT and SWEC-ETHZ, respectively). Moreover, the AZC-based method keeps a similar false-alarm rate (i.e. an average of 2.1 and 1.0, against 2.0 and 0.5, per day).Significance. We propose a new feature and demonstrate its capability in seizure classification for both scalp and intracranial EEG. We envision the use of such a feature to improve outpatient monitoring with resource-constrained devices.
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Affiliation(s)
- R Zanetti
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - U Pale
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - T Teijeiro
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.,Department of Mathematics, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - D Atienza
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
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Sreenivasan N, Gargiulo GD, Gunawardana U, Naik G, Nikpour A. Seizure Detection: A Low Computational Effective Approach without Classification Methods. SENSORS (BASEL, SWITZERLAND) 2022; 22:8444. [PMID: 36366141 PMCID: PMC9657642 DOI: 10.3390/s22218444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 10/21/2022] [Accepted: 10/23/2022] [Indexed: 06/16/2023]
Abstract
Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task. To reduce the complexity and cost of the task, researchers have developed several seizure-detection approaches, primarily focusing on classification systems and spectral feature extraction. While these methods can achieve high/optimal performances, the system may require retraining and following up with the feature extraction for each new patient, thus making it impractical for real-world applications. Herein, we present a straightforward manual/automated detection system based on the simple seizure feature amplification analysis to minimize these practical difficulties. Our algorithm (a simplified version is available as additional material), borrowing from the telecommunication discipline, treats the seizure as the carrier of information and tunes filters to this specific bandwidth, yielding a viable, computationally inexpensive solution. Manual tests gave 93% sensitivity and 96% specificity at a false detection rate of 0.04/h. Automated analyses showed 88% and 97% sensitivity and specificity, respectively. Moreover, our proposed method can accurately detect seizure locations within the brain. In summary, the proposed method has excellent potential, does not require training on new patient data, and can aid in the localization of seizure focus/origin.
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Affiliation(s)
- Neethu Sreenivasan
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia
| | - Gaetano D. Gargiulo
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia
- The MARCS Institute, Westmead, NSW 2145, Australia
- Translational Research Health Institute, Westmead, NSW 2145, Australia
- The Ingam Institute for Medical Research, Liverpool, NSW 2170, Australia
| | - Upul Gunawardana
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia
| | - Ganesh Naik
- Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia
| | - Armin Nikpour
- Neurology Department, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
- Central Clinical School, The University of Sydney, Darlington, NSW 2008, Australia
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Nemati N, Meshgini S. A medium-weight deep convolutional neural network-based approach for onset epileptic seizures classification in EEG signals. Brain Behav 2022; 12:e2763. [PMID: 36196623 PMCID: PMC9660412 DOI: 10.1002/brb3.2763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 12/07/2021] [Accepted: 01/11/2022] [Indexed: 11/28/2022] Open
Abstract
INTRODUCTION Epileptic condition can be detected in EEG data seconds before it occurs, according to evidence. To overcome the related long-term mortality and morbidity from epileptic seizures, it is critical to make an initial diagnosis, uncover underlying causes, and avoid applicable risk factors. Progress in diagnosing onset epileptic seizures can ensure that seizures and destroyed damages are detectable at the time of manifestation. Previous seizure detection models had problems with the presence of multiple features, the lack of an appropriate signal descriptor, and the time-consuming analysis, all of which led to uncertainty and different interpretations. Deep learning has recently made tremendous progress in categorizing and detecting epilepsy. METHOD This work proposes an effective classification strategy in response to these issues. The discrete wavelet transform (DWT) is used to breakdown the EEG signal, and a deep convolutional neural network (DCNN) is used to diagnose epileptic seizures in the first phase. Using a medium-weight DCNN (mw-DCNN) architecture, we use a preprocess phase to improve the decision-maker method. The proposed approach was tested on the CHEG-MIT Scalp EEG database's collected EEG signals. RESULT The results of the studies reveal that the mw-DCNN algorithm produces proper classification results under various conditions. To solve the uncertainty challenge, K-fold cross-validation was used to assess the algorithm's repeatability at the test level, and the accuracies were evaluated in the range of 99%-100%. CONCLUSION The suggested structure can assist medical specialistsin analyzing epileptic seizures' EEG signals more precisely.
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Affiliation(s)
- Nazanin Nemati
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Saeed Meshgini
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
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Wang R, Wang H, Shi L, Han C, Che Y. Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1540. [PMID: 36359630 PMCID: PMC9689850 DOI: 10.3390/e24111540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection.
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Affiliation(s)
- Ruofan Wang
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Haodong Wang
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Lianshuan Shi
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Chunxiao Han
- Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Yanqiu Che
- Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
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He J, Cui J, Zhang G, Xue M, Chu D, Zhao Y. Spatial–temporal seizure detection with graph attention network and bi-directional LSTM architecture. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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44
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Zhai Y, Lv Z, Zhao J, Wang W. Knowledge discovery and variable scale evaluation for long series data. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10250-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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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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An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103820] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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47
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Li J, Wu X, Zhang Y, Yang H, Wu X. DRS-Net: A spatial–temporal affective computing model based on multichannel EEG data. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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48
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El-Gindy SAE, Ibrahim FE, Alabasy M, Abdelzaher HM, El-Refy M, Khalaf AAM, El-Dolil SM, El-Fishawy AS, Taha TE, El-Rabaie ESM, Dessouky MI, El-Dokany I, Oraby OA, N. Alotaiby T, Alshebeili SA, Abd El-Samie FE. Detection of Abnormal Activities from Various Signals Based on Statistical Analysis. WIRELESS PERSONAL COMMUNICATIONS 2022; 125:1013-1046. [DOI: 10.1007/s11277-022-09565-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/29/2022] [Indexed: 09/02/2023]
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Zhao Y, Li C, Liu X, Qian R, Song R, Chen X. Patient-Specific Seizure Prediction via Adder Network and Supervised Contrastive Learning. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1536-1547. [PMID: 35657835 DOI: 10.1109/tnsre.2022.3180155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Deep learning (DL) methods have been widely used in the field of seizure prediction from electroencephalogram (EEG) in recent years. However, DL methods usually have numerous multiplication operations resulting in high computational complexity. In addtion, most of the current approaches in this field focus on designing models with special architectures to learn representations, ignoring the use of intrinsic patterns in the data. In this study, we propose a simple and effective end-to-end adder network and supervised contrastive learning (AddNet-SCL). The method uses addition instead of the massive multiplication in the convolution process to reduce the computational cost. Besides, contrastive learning is employed to effectively use label information, points of the same class are clustered together in the projection space, and points of different class are pushed apart at the same time. Moreover, the proposed model is trained by combining the supervised contrastive loss from the projection layer and the cross-entropy loss from the classification layer. Since the adder networks uses the ℓ1-norm distance as the similarity measure between the input feature and the filters, the gradient function of the network changes, an adaptive learning rate strategy is employed to ensure the convergence of AddNet-CL. Experimental results show that the proposed method achieves 94.9% sensitivity, an area under curve (AUC) of 94.2%, and a false positive rate of (FPR) 0.077/h on 19 patients in the CHB-MIT database and 89.1% sensitivity, an AUC of 83.1%, and an FPR of 0.120/h in the Kaggle database. Competitive results show that this method has broad prospects in clinical practice.
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Deng X, Fan X, Lv X, Sun K. SparNet: A Convolutional Neural Network for EEG Space-Frequency Feature Learning and Depression Discrimination. Front Neuroinform 2022; 16:914823. [PMID: 35722169 PMCID: PMC9201718 DOI: 10.3389/fninf.2022.914823] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Depression affects many people around the world today and is considered a global problem. Electroencephalogram (EEG) measurement is an appropriate way to understand the underlying mechanisms of major depressive disorder (MDD) to distinguish depression from normal control. With the development of deep learning methods, many researchers have adopted deep learning models to improve the classification accuracy of depression recognition. However, there are few studies on designing convolution filters for spatial and frequency domain feature learning in different brain regions. In this study, SparNet, a convolutional neural network composed of five parallel convolutional filters and the SENet, is proposed to learn EEG space-frequency domain characteristics and distinguish between depressive and normal control. The model is trained and tested by the cross-validation method of subject division. The results show that SparNet achieves a sensitivity of 95.07%, a specificity of 93.66%, and an accuracy of 94.37% in classification. Therefore, our results can conclude that the proposed SparNet model is effective in detecting depression using EEG signals. It also indicates that the combination of spatial information and frequency domain information is an effective way to identify patients with depression.
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