1
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Wang J, Sun M, Huang W. Unsupervised EEG-Based Seizure Anomaly Detection with Denoising Diffusion Probabilistic Models. Int J Neural Syst 2024; 34:2450047. [PMID: 38864575 DOI: 10.1142/s0129065724500473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
While many seizure detection methods have demonstrated great accuracy, their training necessitates a substantial volume of labeled data. To address this issue, we propose a novel method for unsupervised seizure anomaly detection called SAnoDDPM, which uses denoising diffusion probabilistic models (DDPM). We designed a novel pipeline that uses a variable lower bound on Markov chains to identify potential values that are unlikely to occur in anomalous data. The model is first trained on normal data, then anomalous data is input to the trained model. The model resamples the anomalous data and converts it to normal data. Finally, the presence of seizures can be determined by comparing the before and after data. Moreover, the input 2D spectrograms are encoded into vector-quantized representations, which enables powerful and efficient DDPM while maintaining its quality. Experimental comparisons on the publicly available datasets, CHB-MIT and TUH, show that our method delivers better results, significantly reduces inference time, and is suitable for deployment in a clinical environments. As far as we are aware, this is the first DDPM-based method for seizure anomaly detection. This novel approach significantly contributes to the progression of seizure detection algorithms, thereby augmenting their practicality in clinical settings.
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Affiliation(s)
- Jiale Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, P. R. China
| | - Mengxue Sun
- School of Information Science and Engineering, Shandong Normal University, Jinan, P. R. China
| | - Wenhui Huang
- School of Information Science and Engineering, Shandong Normal University, Jinan, P. R. China
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2
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Al Fahoum A, Zyout A. Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG-Based Schizophrenia Detection. Int J Neural Syst 2024; 34:2450046. [PMID: 39010724 DOI: 10.1142/s0129065724500461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizophrenia in its early stages. For diagnosing psychiatric/neurological disorders, electroencephalogram (EEG) testing is considered a financially viable, safe, and reliable alternative. Using the reconstructed phase space (RPS) and the continuous wavelet transform, the researchers maximized the differences between the EEG nonstationary signals of normal and schizophrenia individuals, which cannot be observed in the time, frequency, or time-frequency domains. This reveals significant information, highlighting more distinguishable features. Then, a deep learning network was trained to enhance the accuracy of the resulting image classification. The algorithm's efficacy was confirmed through three distinct methods: employing 70% of the dataset for training, 15% for validation, and the remaining 15% for testing. This was followed by a 5-fold cross-validation technique and a leave-one-out classification approach. Each method was iterated 100 times to ascertain the algorithm's robustness. The performance metrics derived from these tests - accuracy, precision, sensitivity, F1 score, Matthews correlation coefficient, and Kappa - indicated remarkable outcomes. The algorithm demonstrated steady performance across all evaluation strategies, underscoring its relevance and reliability. The outcomes validate the system's accuracy, precision, sensitivity, and robustness by showcasing its capability to autonomously differentiate individuals diagnosed with schizophrenia from those in a state of normal health.
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Affiliation(s)
- Amjed Al Fahoum
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan
| | - Ala'a Zyout
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan
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3
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Wang Y, Yuan S, Liu JX, Hu W, Jia Q, Xu F. Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection. Int J Neural Syst 2024; 34:2450040. [PMID: 38753012 DOI: 10.1142/s0129065724500400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers a semi-supervised hybrid architecture for detecting seizures, which combines the extracted electroencephalogram (EEG) feature dataset and convolutional autoencoder, called Fd-CAE. First, various features in the time domain and entropy domain are extracted to characterize the EEG signal, which helps distinguish epileptic seizures subsequently. Then, the unlabeled EEG features are fed into the convolutional autoencoder (CAE) for training, which effectively represents EEG features by optimizing the loss between the input and output features. This unsupervised feature learning process can better combine and optimize EEG features from unlabeled data. After that, the pre-trained encoder part of the model is used for further feature learning of labeled data to obtain its low-dimensional feature representation and achieve classification. This model is performed on the neonatal EEG dataset collected at the University of Helsinki Hospital, which has a high discriminative ability to detect seizures, with an accuracy of 92.34%, precision of 93.61%, recall rate of 98.74%, and F1-score of 95.77%, respectively. The results show that unsupervised learning by CAE is beneficial to the characterization of EEG signals, and the proposed Fd-CAE method significantly improves classification performance.
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Affiliation(s)
- Yuxia Wang
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Shasha Yuan
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Wenrong Hu
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Qingwei Jia
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Fangzhou Xu
- School of Electronic and Information Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, P. R. China
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4
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Lv H, Zhang Y, Xiao T, Wang Z, Wang S, Feng H, Zhao X, Zhao Y. Seizure Detection Based on Lightweight Inverted Residual Attention Network. Int J Neural Syst 2024; 34:2450042. [PMID: 38818805 DOI: 10.1142/s0129065724500424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detection. Additionally, existing methods often neglect the key characteristic channels and spatial regions of electroencephalography (EEG) signals. To solve these issues, we propose a lightweight EEG-based seizure detection model named lightweight inverted residual attention network (LRAN). Specifically, we employ a four-stage inverted residual mobile block (iRMB) to effectively extract the hierarchical features from EEG. The convolutional block attention module (CBAM) is introduced to make the model focus on important feature channels and spatial information, thereby enhancing the discrimination of the learned features. Finally, convolution operations are used to capture local information and spatial relationships between features. We conduct intra-subject and inter-subject experiments on a publicly available dataset. Intra-subject experiments obtain 99.25% accuracy in segment-based detection and 0.36/h false detection rate (FDR) in event-based detection, respectively. Inter-subject experiments obtain 84.32% accuracy. Both sets of experiments maintain high classification accuracy with a low number of parameters, where the multiply accumulate operations (MACs) are 25.86[Formula: see text]M and the number of parameters is 0.57[Formula: see text]M.
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Affiliation(s)
- Hongbin Lv
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Yongfeng Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Tiantian Xiao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Ziwei Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Shuai Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Hailing Feng
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Xianxun Zhao
- Department of Automotive Engineering, Heze Engineering Technician College, Heze 274000, P. R. China
| | - Yanna Zhao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
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5
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Wu M, Peng H, Liu Z, Wang J. Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model. Int J Neural Syst 2024:2450051. [PMID: 39004932 DOI: 10.1142/s0129065724500515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person's life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to improve the efficiency of early detection and diagnosis of seizure, this paper proposes a new seizure detection method, which is based on discrete wavelet transform (DWT) and multi-channel long- and short-term memory-like spiking neural P (LSTM-SNP) model. First, the signal is decomposed into 5 levels by using DWT transform to obtain the features of the components at different frequencies, and a series of time-frequency features in wavelet coefficients are extracted. Then, these different features are used to train a multi-channel LSTM-SNP model and perform seizure detection. The proposed method achieves a high seizure detection accuracy on the CHB-MIT dataset: 98.25% accuracy, 98.22% specificity and 97.59% sensitivity. This indicates that the proposed epilepsy detection method can show competitive detection performance.
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Affiliation(s)
- Min Wu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Zhicai Liu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P. R. China
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6
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Swami P, Maheshwari J, Kumar M, Bhatia M. Evolutionary transfer optimization-based approach for automated ictal pattern recognition using brain signals. Front Hum Neurosci 2024; 18:1386168. [PMID: 39055535 PMCID: PMC11269234 DOI: 10.3389/fnhum.2024.1386168] [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: 02/14/2024] [Accepted: 05/01/2024] [Indexed: 07/27/2024] Open
Abstract
The visual scrutinization process for detecting epileptic seizures (ictal patterns) is time-consuming and prone to manual errors, which can have serious consequences, including drug abuse and life-threatening situations. To address these challenges, expert systems for automated detection of ictal patterns have been developed, yet feature engineering remains problematic due to variability within and between subjects. Single-objective optimization approaches yield less reliable results. This study proposes a novel expert system using the non-dominated sorting genetic algorithm (NSGA)-II to detect ictal patterns in brain signals. Employing an evolutionary multi-objective optimization (EMO) approach, the classifier minimizes both the number of features and the error rate simultaneously. Input features include statistical features derived from phase space transformations, singular values, and energy values of time-frequency domain wavelet packet transform coefficients. Through evolutionary transfer optimization (ETO), the optimal feature set is determined from training datasets and passed through a generalized regression neural network (GRNN) model for pattern detection of testing datasets. The results demonstrate high accuracy with minimal computation time (<0.5 s), and EMO reduces the feature set matrix by more than half, suggesting reliability for clinical applications. In conclusion, the proposed model offers promising advancements in automating ictal pattern recognition in EEG data, with potential implications for improving epilepsy diagnosis and treatment. Further research is warranted to validate its performance across diverse datasets and investigate potential limitations.
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Affiliation(s)
- Piyush Swami
- Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital – Amager and Hvidovre, Copenhagen, Denmark
- Biomedical Engineering Techies, Broendby, Denmark
| | - Jyoti Maheshwari
- School of Behavioural Forensics, National Forensic Sciences University, Gandhinagar, Gujarat, India
| | - Mohit Kumar
- School of Electronics Engineering, VIT-AP University, Amaravati, India
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7
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Gill TS, Zaidi SSH, Shirazi MA. Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures. Epilepsy Behav 2024; 155:109732. [PMID: 38636140 DOI: 10.1016/j.yebeh.2024.109732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 02/03/2024] [Accepted: 02/27/2024] [Indexed: 04/20/2024]
Abstract
Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy diagnosis, but manual seizure classification is time-consuming and requires extensive expertise. This paper presents an automated multi-class seizure classification model using EEG signals from the Temple University Hospital Seizure Corpus ver. 1.5.2. 11 features including time-based correlation, time-based eigenvalues, power spectral density, frequency-based correlation, frequency-based eigenvalues, sample entropy, spectral entropy, logarithmic sum, standard deviation, absolute mean, and ratio of Daubechies D4 wavelet transformed coefficients were extracted from 10-second sliding windows across channels. The model combines multi-head self-attention mechanism with a deep convolutional neural network (CNN) to classify seven subtypes of generalized and focal epileptic seizures. The model achieved 0.921 weighted accuracy and 0.902 weighted F1 score in classifying focal onset non-motor, generalized onset non-motor, simple partial, complex partial, absence, tonic, and tonic-clonic seizures. In comparison, a CNN model without multi-head attention achieved 0.767 weighted accuracy. Ablation studies were conducted to validate the importance of transformer encoders and attention. The promising classification results demonstrate the potential of deep learning for handling EEG complexity and improving epilepsy diagnosis. This seizure classification model could enable timely interventions when translated into clinical practice.
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Affiliation(s)
- Taimur Shahzad Gill
- Department of Electronics and Power Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.
| | - Syed Sajjad Haider Zaidi
- Department of Electronics and Power Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.
| | - Muhammad Ayaz Shirazi
- Department of Electronics and Power Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.
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8
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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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9
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Ma H, Wu Y, Tang Y, Chen R, Xu T, Zhang W. Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction. Comput Biol Med 2024; 176:108565. [PMID: 38744007 DOI: 10.1016/j.compbiomed.2024.108565] [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: 12/22/2023] [Revised: 04/09/2024] [Accepted: 05/05/2024] [Indexed: 05/16/2024]
Abstract
Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal.
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Affiliation(s)
- Hongcheng Ma
- School of Information and Communication Engineering, Hainan University, Haikou, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Yajing Wu
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Yongqiang Tang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Rui Chen
- School of Information and Communication Engineering, Hainan University, Haikou, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Tao Xu
- Shanxi Key Laboratory of Big Data Analysis and Parallel Computing, Taiyuan University of Science and Technology, Taiyuan, China.
| | - Wensheng Zhang
- School of Information and Communication Engineering, Hainan University, Haikou, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China.
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10
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Yedurkar DP, Metkar SP, Stephan T. Multiresolution directed transfer function approach for segment-wise seizure classification of epileptic EEG signal. Cogn Neurodyn 2024; 18:301-315. [PMID: 38699601 PMCID: PMC11061070 DOI: 10.1007/s11571-021-09773-z] [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: 08/12/2021] [Revised: 11/10/2021] [Accepted: 12/13/2021] [Indexed: 11/03/2022] Open
Abstract
Currently, with the bloom in artificial intelligence (AI) algorithms, various human-centered smart systems can be utilized, especially in cognitive computing, for the detection of various chronic brain diseases such as epileptic seizure. The primary goal of this research article is to propose a novel human-centered cognitive computing (HCCC) method for segment-wise seizure classification by employing multiresolution extracted data with directed transfer function (DTF) features, termed as the multiresolution directed transfer function (MDTF) approach. Initially, the multiresolution information of the epileptic seizure signal is extracted using a multiresolution adaptive filtering (MRAF) method. These seizure details are passed to the DTF where the information flow of high frequency bands is computed. Thereafter, different measures of complexity such as approximate entropy (AEN) and sample entropy (SAEN) are computed from the extracted high frequency bands. Lastly, a k-nearest neighbor (k-NN) and support vector machine (SVM) are used for classifying the EEG signal into non-seizure and seizure data depending on the multiresolution based information flow characteristics. The MDTF approach is tested on a standard dataset and validated using a dataset from a local hospital. The proposed technique has obtained an average sensitivity of 98.31%, specificity of 96.13% and accuracy of 98.89% using SVM classifier. The average detection rate of the MDTF approach is 97.72% which is greater than the existing approaches. The proposed MDTF method will help neuro-specialists to locate seizure information drift which occurs within the consecutive segments and between two channels. The main advantage of the MDTF approach is its capability to locate the seizure activity contained by the EEG signal with accuracy. This will assist the neurologists with the precise localization of the epileptic seizure automatically and hence will reduce the burden of time-consuming epileptic seizure analysis.
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Affiliation(s)
- Dhanalekshmi P. Yedurkar
- Department of Electronics and Telecommunication Engineering, College of Engineering Pune, Pune, 411005 India
| | - Shilpa P. Metkar
- Department of Electronics and Telecommunication Engineering, College of Engineering Pune, Pune, 411005 India
| | - Thompson Stephan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka 560054 India
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11
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Dong X, Wen Y, Ji D, Yuan S, Liu Z, Shang W, Zhou W. Epileptic Seizure Detection with an End-to-End Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model. Int J Neural Syst 2024; 34:2450012. [PMID: 38230571 DOI: 10.1142/s0129065724500126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and the capability of bidirectional long short-term memory (BiLSTM) in mining the long-range dependency of multi-channel time-series, we propose an automatic seizure detection method with a novel end-to-end TCN-BiLSTM model in this work. First, raw EEG is filtered with a 0.5-45 Hz band-pass filter, and the filtered data are input into the proposed TCN-BiLSTM network for feature extraction and classification. Post-processing process including moving average filtering, thresholding and collar technique is then employed to further improve the detection performance. The method was evaluated on two EEG database. On the CHB-MIT scalp EEG database, our method achieved a segment-based sensitivity of 94.31%, specificity of 97.13%, and accuracy of 97.09%. Meanwhile, an event-based sensitivity of 96.48% and an average false detection rate (FDR) of 0.38/h were obtained. On the SH-SDU database we collected, the segment-based sensitivity of 94.99%, specificity of 93.25%, and accuracy of 93.27% were achieved. In addition, an event-based sensitivity of 99.35% and a false detection rate of 0.54/h were yielded. The total detection time consumed for 1[Formula: see text]h EEG data was 5.65[Formula: see text]s. These results demonstrate the superiority and promising potential of the proposed method in real-time monitoring of epileptic seizures.
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Affiliation(s)
- Xingchen Dong
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Yiming Wen
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Dezan Ji
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Shasha Yuan
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Zhen Liu
- Second Hospital of Shandong University, Jinan 250100, P. R. China
| | - Wei Shang
- Second Hospital of Shandong University, Jinan 250100, P. R. China
| | - Weidong Zhou
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
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12
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Brogin JAF, Faber J, Reyes-Garcia SZ, Cavalheiro EA, Bueno DD. Epileptic seizure suppression: A computational approach for identification and control using real data. PLoS One 2024; 19:e0298762. [PMID: 38416729 PMCID: PMC10901337 DOI: 10.1371/journal.pone.0298762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 01/31/2024] [Indexed: 03/01/2024] Open
Abstract
Epilepsy affects millions of people worldwide every year and remains an open subject for research. Current development on this field has focused on obtaining computational models to better understand its triggering mechanisms, attain realistic descriptions and study seizure suppression. Controllers have been successfully applied to mitigate epileptiform activity in dynamic models written in state-space notation, whose applicability is, however, restricted to signatures that are accurately described by them. Alternatively, autoregressive modeling (AR), a typical data-driven tool related to system identification (SI), can be directly applied to signals to generate more realistic models, and since it is inherently convertible into state-space representation, it can thus be used for the artificial reconstruction and attenuation of seizures as well. Considering this, the first objective of this work is to propose an SI approach using AR models to describe real epileptiform activity. The second objective is to provide a strategy for reconstructing and mitigating such activity artificially, considering non-hybrid and hybrid controllers - designed from ictal and interictal events, respectively. The results show that AR models of relatively low order represent epileptiform activities fairly well and both controllers are effective in attenuating the undesired activity while simultaneously driving the signal to an interictal condition. These findings may lead to customized models based on each signal, brain region or patient, from which it is possible to better define shape, frequency and duration of external stimuli that are necessary to attenuate seizures.
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Affiliation(s)
- João A. F. Brogin
- Department of Mechanical Engineering, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
| | - Jean Faber
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Selvin Z. Reyes-Garcia
- Departamento de Ciencias Morfológicas, Facultad de Ciencias Médicas, Universidad Nacional Autónoma de Honduras, Tegucigalpa, Honduras
| | - Esper A. Cavalheiro
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Douglas D. Bueno
- Department of Mathematics, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
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13
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Zhang W, Cheng Y, Zhang L, Wei Y, Xie H, Huang J. Association between emergence delirium and brain status parameters in children undergoing general anesthesia: A prospective observational study. Paediatr Anaesth 2024; 34:130-137. [PMID: 37788105 PMCID: PMC10824597 DOI: 10.1111/pan.14779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 08/07/2023] [Accepted: 09/15/2023] [Indexed: 10/05/2023]
Abstract
INTRODUCTION Emergence delirium is a common postoperative neurological complication in children after general anesthesia. There is no valid tool to predict emergence delirium. Wavelet index, pain threshold index, anxiety index, and comfort index are real-time brain status parameters extracted from the electroencephalogram, which have recently been developed. The aim is to evaluate the association between real-time brain status parameters during emergence and emergence delirium in children undergoing general anesthesia. METHODS One hundred and thirty patients between 3 and 6 years of age undergoing dental surgery under general anesthesia were enrolled in the study. Real-time electroencephalogram data were recorded at four different time points from end of anesthetics administration (T1), end of surgery (T2), extubation (T3), and response (eye opening, movement) to verbal stimulation (T4). Each patient was assessed for emergence delirium using the Pediatric Anesthesia Emergence Delirium scale. Receiver operating characteristics curves and the associated areas under the curves were computed to analyze the ability of wavelet index, pain threshold index, anxiety index, and comfort index to predict emergence delirium. RESULTS One hundred and sixteen patients were included for final analysis. During recovery from general anesthesia, brain status parameters increased significantly from T1 (wavelet index, 59.5 ± 6.2; pain threshold index, 61.7 ± 5.3; anxiety index, 9.2 ± 2.5; comfort index, 21.6 ± 8.7) to T4 (wavelet index, 67.4 ± 9.4; pain threshold index, 73.2 ± 9.1; anxiety index, 38.6 ± 11.2; comfort index, 66.1 ± 16.5; p < .001). To predict emergence delirium, areas under the curves [95% CI] for anxiety index were 0.84 [0.75-0.93] (p < .001), and comfort index was 0.89 [0.81-0.96] (p < .001). The Pediatric Anesthesia Emergence Delirium scale scores of 37 patients were higher than 10 indicating emergence delirium, and the incidence of emergence delirium was 31.90%. The sensitivity and specificity of anxiety index with corresponding cutoff values in predicting emergence delirium were 73.0% and 89.9%, and the sensitivity and specificity of comfort index in predicting emergence delirium were 91.9% and 83.5%. The best cutoff values for anxiety index and comfort index to predict emergence delirium were 46.5 and 68.5, respectively. The areas under the curves [95% CI] of wavelet index to predict emergence delirium were 0.43 [0.31-0.35] (p = .27), while the areas under the curves [95% CI] of pain threshold index to predict emergence delirium were 0.49 [0.37-0.62] (p = .73). DISCUSSION Both anxiety index and comfort index derived from electroencephalogram wavelet analysis were associated with emergence delirium in pediatric patients undergoing general anesthesia for dental surgery. Wavelet index and pain threshold index were not associated with emergence delirium during general anesthesia for dental surgery in children. CONCLUSIONS AnXi and CFi might be used to guide anesthesiologists to identify and intervene ED in children.
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Affiliation(s)
- Weizhi Zhang
- Department of Anesthesiology, Shanxi Provincial Children’s Hospital, Taiyuan, China
| | - Yansheng Cheng
- Department of Anesthesiology, Shanxi Provincial Children’s Hospital, Taiyuan, China
| | - Li Zhang
- Department of Anesthesiology, Shanxi Provincial Children’s Hospital, Taiyuan, China
| | - Yunwei Wei
- Department of Anesthesiology, Shanxi Provincial Children’s Hospital, Taiyuan, China
| | - Haiqing Xie
- Department of Anesthesiology, Shanxi Provincial Children’s Hospital, Taiyuan, China
| | - Jiapeng Huang
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA
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Abedinzadeh Torghabeh F, Modaresnia Y, Moattar MH. Hybrid deep transfer learning-based early diagnosis of autism spectrum disorder using scalogram representation of electroencephalography signals. Med Biol Eng Comput 2024; 62:495-503. [PMID: 37938451 DOI: 10.1007/s11517-023-02959-z] [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: 04/25/2023] [Accepted: 10/28/2023] [Indexed: 11/09/2023]
Abstract
Early diagnosis of autism spectrum disorder (ASD) plays an important role in the rehabilitation of the patient. This goal necessitates higher-level pattern representation and a strong modeling approach. The proposed approach applies scalogram images of electroencephalography signals for the first purpose and a two-level deep learning architecture for better classification. Scalogram images embed both the temporal and spectral information of the signal. On the other hand, the hybrid deep learning hierarchy of convolutional neural network followed by long short-term memory models both spatial and temporal information of the scalogram image. The approach is evaluated on a dataset of 34 ASD samples and 11 normal cases in without-voice and with-voice conditions. To validate the early diagnosis hypothesis, signals from children older than 5 years are used as the training set, and signals from younger subjects are used as the validation set. The proposed method achieves excellent performance of 99.50% and 98.43% for automatically detecting ASD with and without voice, respectively. This classification performance is higher than most recent reported approaches, and the results show the effectiveness of the approach in early diagnosis of ASD and demonstrate the auditory impact on the diagnosis of autism.
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Affiliation(s)
| | - Yeganeh Modaresnia
- Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
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15
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Wolfson SS, Kirk I, Waldie K, King C. EEG Complexity Analysis of Brain States, Tasks and ASD Risk. ADVANCES IN NEUROBIOLOGY 2024; 36:733-759. [PMID: 38468061 DOI: 10.1007/978-3-031-47606-8_37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Autism spectrum disorder is an increasingly prevalent and debilitating neurodevelopmental condition and an electroencephalogram (EEG) diagnostic challenge. Despite large amounts of electrophysiological research over many decades, an EEG biomarker for autism spectrum disorder (ASD) has not been found. We hypothesized that reductions in complex dynamical system behaviour in the human central nervous system as part of the macroscale neuronal function during cognitive processes might be detectable in whole EEG for higher-risk ASD adults. In three studies, we compared the medians of correlation dimension, largest Lyapunov exponent, Higuchi's fractal dimension, multiscale entropy, multifractal detrended fluctuation analysis and Kolmogorov complexity during resting, cognitive and social skill tasks in 20 EEG channels of 39 adults over a range of ASD risk. We found heterogeneous complexity distribution with clusters of hierarchical sequences pointing to potential cognitive processing differences, but no clear distinction based on ASD risk. We suggest that there is indication of statistically significant differences between complexity measures of brain states and tasks. Though replication of our studies is needed with a larger sample, we believe that our electrophysiological and analytic approach has potential as a biomarker for earlier ASD diagnosis.
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Affiliation(s)
- Stephen S Wolfson
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand.
| | - Ian Kirk
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
| | - Karen Waldie
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
| | - Chris King
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
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16
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Zhang H, Zhou QQ, Chen H, Hu XQ, Li WG, Bai Y, Han JX, Wang Y, Liang ZH, Chen D, Cong FY, Yan JQ, Li XL. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil Med Res 2023; 10:67. [PMID: 38115158 PMCID: PMC10729551 DOI: 10.1186/s40779-023-00502-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/23/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.
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Affiliation(s)
- Hao Zhang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Qing-Qi Zhou
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China
| | - He Chen
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Xiao-Qing Hu
- Department of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, 999077, China
- HKU-Shenzhen Institute of Research and Innovation, Shenzhen, 518057, Guangdong, China
| | - Wei-Guang Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Yang Bai
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang, 330006, China
| | - Jun-Xia Han
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yao Wang
- School of Communication Science, Beijing Language and Culture University, Beijing, 100083, China
| | - Zhen-Hu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.
| | - Dan Chen
- School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Feng-Yu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, Liaoning, China.
| | - Jia-Qing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China.
| | - Xiao-Li Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China.
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Yousif MAA, Ozturk M. Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach. Int J Neural Syst 2023; 33:2350064. [PMID: 37830300 DOI: 10.1142/s0129065723500648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately perfect time and frequency resolutions. In this paper, it is aimed to show the TF representation performance and robustness of ConceFT by using it for the classification of the epileptic electroencephalography (EEG) signals. Therefore, a signal classification algorithm which uses TF images obtained with ConceFT to feed the transfer learning structure has been presented. Epilepsy is a common neurological disorder that millions of people suffer worldwide. Daily lives of the patients are quite difficult because of the unpredictable time of seizures. EEG signals monitoring the electrical activity of the brain can be used to detect approaching seizures and make possible to warn the patient before the attack. GoogLeNet which is a well-known deep learning model has been preferred to classify TF images. Classification performance is directly related to the TF representation accuracy of the ConceFT. The proposed method has been tested for various classification scenarios and obtained accuracies between 95.83% and 99.58% for two and three-class classification scenarios. High results show that ConceFT is a successful and promising TF analysis method for non-stationary biomedical signals.
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Affiliation(s)
- Mosab A A Yousif
- Department of Biomedical Engineering, Institute of Graduate Studies, Istanbul University-Cerrahpasa, Istanbul, Turkey
- Department of Electronics Engineering, University of Gezira, Wad-Madani, Sudan
| | - Mahmut Ozturk
- Department of Electrical and Electronics Engineering, Engineering Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
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18
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Liu D, Dong X, Bian D, Zhou W. Epileptic Seizure Prediction Using Attention Augmented Convolutional Network. Int J Neural Syst 2023; 33:2350054. [PMID: 37675593 DOI: 10.1142/s0129065723500545] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly challenging due to their nonictal nature and remarkable similarities. In this study, a novel epileptic seizure prediction method is proposed based on multi-head attention (MHA) augmented convolutional neural network (CNN) to address the issue of CNN's limit of capturing global information of input signals. First, data enhancement is performed on original EEG recordings to balance the pre-ictal and inter-ictal EEG data, and the EEG recordings are sliced into 6-second-long EEG segments. Subsequently, EEG time-frequency distribution is obtained using Stockwell transform (ST), and the attention augmented convolutional network is employed for feature extraction and classification. Finally, post-processing is utilized to reduce the false prediction rate (FPR). The CHB-MIT EEG database was used to evaluate the system. The validation results showed a segment-based sensitivity of 98.24% and an event-based sensitivity of 94.78% with a FPR of 0.05/h were yielded, respectively. The satisfying results of the proposed method demonstrate its possible potential for clinical applications.
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Affiliation(s)
- Dongsheng Liu
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
| | - Xingchen Dong
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
| | - Dong Bian
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
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19
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Yogarajan G, Alsubaie N, Rajasekaran G, Revathi T, Alqahtani MS, Abbas M, Alshahrani MM, Soufiene BO. EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network. Sci Rep 2023; 13:17710. [PMID: 37853025 PMCID: PMC10584945 DOI: 10.1038/s41598-023-44318-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 10/06/2023] [Indexed: 10/20/2023] Open
Abstract
Electroencephalogram (EEG) is one of the most common methods used for seizure detection as it records the electrical activity of the brain. Symmetry and asymmetry of EEG signals can be used as indicators of epileptic seizures. Normally, EEG signals are symmetrical in nature, with similar patterns on both sides of the brain. However, during a seizure, there may be a sudden increase in the electrical activity in one hemisphere of the brain, causing asymmetry in the EEG signal. In patients with epilepsy, interictal EEG may show asymmetric spikes or sharp waves, indicating the presence of epileptic activity. Therefore, the detection of symmetry/asymmetry in EEG signals can be used as a useful tool in the diagnosis and management of epilepsy. However, it should be noted that EEG findings should always be interpreted in conjunction with the patient's clinical history and other diagnostic tests. In this paper, we propose an EEG-based improved automatic seizure detection system using a Deep neural network (DNN) and Binary dragonfly algorithm (BDFA). The DNN model learns the characteristics of the EEG signals through nine different statistical and Hjorth parameters extracted from various levels of decomposed signals obtained by using the Stationary Wavelet Transform. Next, the extracted features were reduced using the BDFA which helps to train DNN faster and improve its performance. The results show that the extracted features help to differentiate the normal, interictal, and ictal signals effectively with 100% accuracy, sensitivity, specificity, and F1 score with a 13% selected feature subset when compared to the existing approaches.
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Affiliation(s)
- G Yogarajan
- Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, 626005, India
| | - Najah Alsubaie
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - G Rajasekaran
- Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, 626005, India
| | - T Revathi
- Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, 626005, India
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, 61421, Abha, Saudi Arabia
- BioImaging Unit, Space Research Centre, University of Leicester, Michael Atiyah Building, Leicester, LE1 7RH, UK
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia
| | | | - Ben Othman Soufiene
- PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia.
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20
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Qiao R, Zhang H, Tian Y. EEG cortical network reveals the temporo-spatial mechanism of visual search. Brain Res Bull 2023; 203:110758. [PMID: 37704055 DOI: 10.1016/j.brainresbull.2023.110758] [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: 06/06/2023] [Revised: 08/06/2023] [Accepted: 09/11/2023] [Indexed: 09/15/2023]
Abstract
This study aims to explore a method based on brain networks for implicit attention by using wavelet coherence as feature to identify individual targets in the visual field, find the optimal classification rhythm and time window, and investigate the relationship between the optimal rhythm and N2pc event-related potential. The study uses a weighted minimum norm estimate to locate the sources of the scalp EEG and reconstructs the source time series. The functional connectivity between brain areas during the visual search process is evaluated using wavelet coherence analysis, and a lateral difference network is constructed based on the difference in coherence values between the left and right visual fields. A support vector machine classifier is trained based on the wavelet coherence network features to identify the target in the left or right visual field. We also extract N2pc from the source activity data of the parieto-occipital brain region and record the time period in which N2pc occurred. The study finds that the best classification performance is achieved in the theta rhythm from 200 to 400 ms and achieved an average classification accuracy of 87% (chance level: 51.07%) in a serial search task. And this time window corresponds to the time period when N2pc appeared. The results show that the use of wavelet coherence analysis to evaluate the functional connectivity between brain areas during the visual search process provides a new approach for analyzing brain activity. The study's findings regarding the relationship between the N2pc and theta rhythm and the effectiveness of using wavelet coherence network features based on the theta rhythm for visual search classification contribute to the understanding of the neural mechanisms underlying visual search.
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Affiliation(s)
- Rui Qiao
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Haiyong Zhang
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Yin Tian
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Institute for Advanced Sciences,Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing 400064, China.
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21
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Miller J, Jones T, Upston J, Deng ZD, McClintock SM, Erhardt E, Farrar D, Abbott CC. Electric Field, Ictal Theta Power, and Clinical Outcomes in Electroconvulsive Therapy. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:760-767. [PMID: 36925066 PMCID: PMC10329999 DOI: 10.1016/j.bpsc.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/22/2023] [Accepted: 03/01/2023] [Indexed: 03/17/2023]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is efficacious for treatment-resistant depression. Treatment-induced cognitive impairment can adversely impact functional outcomes. Our pilot study linked the electric field to ictal theta power from a single suprathreshold treatment and linked ictal theta power to changes in phonemic fluency. In this study, we set out to replicate our findings and expand upon the utility of ictal theta power as a potential cognitive biomarker. METHODS Twenty-seven participants (18 female and 9 male) received right unilateral ECT for treatment-resistant depression. Pre-ECT magnetic resonance imaging and finite element modeling determined the 90th percentile maximum electric field in the brain. Two-lead electroencephalographs were digitally captured across the ECT course, with the earliest suprathreshold treatment used to determine power spectral density. Clinical and cognitive outcomes were assessed pre-, mid-, and post-ECT. We assessed the relationship between the electric field in the brain, ictal theta power, clinical outcome (Inventory of Depressive Symptomatology), and cognitive outcomes (phonemic and semantic fluency) with linear models. RESULTS Ictal theta power in the Fp1 and Fp2 channels was associated with the electric field, antidepressant outcome, and phonemic and semantic fluency. The relationship between ictal theta power and phonemic fluency was strengthened in the longitudinal analysis. The electric field in the brain was directly associated with phonemic and semantic fluency but not with antidepressant outcome. CONCLUSIONS Ictal theta power is a potential cognitive biomarker early on in the ECT course to help guide parameter changes. Larger studies are needed to further assess ictal theta power's role in predicting mood outcome and changes with ECT parameters.
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Affiliation(s)
- Jeremy Miller
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, New Mexico.
| | - Tom Jones
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Joel Upston
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Zhi-De Deng
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland; Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Shawn M McClintock
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, Texas
| | - Erik Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico
| | - Danielle Farrar
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Christopher C Abbott
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, New Mexico.
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Roy B, Malviya L, Kumar R, Mal S, Kumar A, Bhowmik T, Hu JW. Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals. Diagnostics (Basel) 2023; 13:diagnostics13111936. [PMID: 37296788 DOI: 10.3390/diagnostics13111936] [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: 04/16/2023] [Revised: 05/14/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Stress has an impact, not only on a person's physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.
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Affiliation(s)
- Bishwajit Roy
- Department of Computer Science Engineering-AI & ML, Siliguri Institute of Technology, Siliguri 734009, India
| | - Lokesh Malviya
- School of Computing Science and Engineering, Vellore Institute of Technology Bhopal University, Bhopal 466114, India
| | - Radhikesh Kumar
- Department of Computer Science and Engineering, National Institute of Technology, Patna 800001, India
| | - Sandip Mal
- School of Computing Science and Engineering, Vellore Institute of Technology Bhopal University, Bhopal 466114, India
| | - Amrendra Kumar
- Department of Civil Engineering, Roorkee Institute of Technology, Roorkee 247667, India
| | - Tanmay Bhowmik
- Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar 382426, India
| | - Jong Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22022, Republic of Korea
- Incheon Disaster Prevention Research Center, Incheon National University, Incheon 22022, Republic of Korea
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23
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Zhong L, Wan J, Yi F, He S, Wu J, Huang Z, Lu Y, Yang J, Li Z. Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG. Front Neurosci 2023; 17:1174005. [PMID: 37081931 PMCID: PMC10111052 DOI: 10.3389/fnins.2023.1174005] [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: 02/25/2023] [Accepted: 03/21/2023] [Indexed: 04/07/2023] Open
Abstract
Objective Epilepsy is the second most common brain neurological disease after stroke, which has the characteristics of sudden and recurrence. Seizure prediction is seriously important for improving the quality of patients' lives. Methods From the perspective of multiple dimensions including time-frequency, entropy and brain network, this paper proposed a novel approach by constructing the optimal spatiotemporal feature set to predict seizures. Based on strong independence and large information capabilities, the two-dimensional feature screening algorithm is performed to eliminate unnecessary redundant features. In order to verify the effectiveness of the optimal feature set, support vector machine (SVM) was used to classify the preictal and interictal states on both the Kaggle intracranial EEG and CHB-MIT scalp EEG dataset. Results This model achieved an average accuracy of 98.01%, AUC of 0.96, F-Score of 98.3% and FPR of 0.0383/h on the Kaggle dataset; On the CHB-MIT dataset, the average accuracy, AUC, F-score and FPR were 95.93%, 0.92, 94.97% and 0.0473/h, respectively. Further ablation experiments have confirmed that the temporal and spatial features fusion has better performance than the individual temporal or spatial features. Conclusion Compared to the state-of-the-art methods, our approach outperforms most of these existing techniques. The results show that our approach can effectively extract the spatiotemporal information of epileptic EEG signals to predict epileptic seizures with high performance.
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Affiliation(s)
- Lisha Zhong
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, Sichuan, China
| | - Jiangzhong Wan
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, Sichuan, China
| | - Fangji Yi
- Research Center of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Shuling He
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Jia Wu
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, Sichuan, China
| | - Zhiwei Huang
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, Sichuan, China
| | - Yi Lu
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
| | - Jiazhang Yang
- Yongchuan Women and Children Hospital, Chongqing, China
| | - Zhangyong Li
- Research Center of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
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24
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Peh WY, Thangavel P, Yao Y, Thomas J, Tan YL, Dauwels J. Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG. Int J Neural Syst 2023; 33:2350012. [PMID: 36809996 DOI: 10.1142/s0129065723500120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse characteristics across patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). First, we deploy a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments. Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments. At last, we apply post-processing filters to the segment-level outputs to determine seizures' start and end points in multi-channel EEGs. Finally, we introduce the minimum overlap evaluation scoring as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) dataset and evaluated it on five independent EEG datasets. We evaluate the systems with the following metrics: sensitivity (SEN), precision (PRE), and average and median false positive rate per hour (aFPR/h and mFPR/h). Across four adult scalp EEG and iEEG datasets, we obtained SEN of 0.617-1.00, PRE of 0.534-1.00, aFPR/h of 0.425-2.002, and mFPR/h of 0-1.003. The proposed seizure detector can detect seizures in adult EEGs and takes less than 15[Formula: see text]s for a 30[Formula: see text]min EEG. Hence, this system could aid clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.
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Affiliation(s)
- Wei Yan Peh
- Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore 639798, Singapore
| | - Prasanth Thangavel
- Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore 639798, Singapore
| | - Yuanyuan Yao
- Katholieke Universiteit Leuven, Oude Markt 13, 3000 Leuven, Belgium
| | - John Thomas
- Montreal Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada
| | - Yee-Leng Tan
- National Neuroscience Institute, Singapore 308433, Singapore
| | - Justin Dauwels
- Department of Microelectronics, Delft, University of Technology, 2628 CD Delft, Netherlands
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25
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Qin X, Niu Y, Zhou H, Li X, Jia W, Zheng Y. Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network. Int J Neural Syst 2023; 33:2350009. [PMID: 36655401 DOI: 10.1142/s0129065723500090] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Accurate identification of driver's drowsiness state through Electroencephalogram (EEG) signals can effectively reduce traffic accidents, but EEG signals are usually stored in various clients in the form of small samples. This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver's drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver's privacy and improves communication efficiency.
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Affiliation(s)
- Xue Qin
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China
| | - Yi Niu
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China
| | - Huiyu Zhou
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Xiaojie Li
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China
| | - Weikuan Jia
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China
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26
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Currey D, Craley J, Hsu D, Ahmed R, Venkataraman A. EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG. PLoS One 2023; 18:e0282268. [PMID: 36848345 PMCID: PMC9970073 DOI: 10.1371/journal.pone.0282268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 02/11/2023] [Indexed: 03/01/2023] Open
Abstract
Scalp Electroencephalography (EEG) is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in computational neuroscience towards spatio-temporal predictive analyses. We introduce a novel open-source viewer, the EEG Prediction Visualizer (EPViz), to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to EEG features, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. These results can be saved as high-resolution images for use in manuscripts and presentations. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic data statistics, and annotation editing. Finally, we have included a built-in EDF anonymization module to facilitate sharing of clinical data. Taken together, EPViz fills a much needed gap in EEG visualization. Our user-friendly interface and rich collection of features may also help to promote collaboration between engineers and clinicians.
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Affiliation(s)
- Danielle Currey
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jeff Craley
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - David Hsu
- Department of Neurology, University of Wisconsin Madison, Madison, WI, United States of America
| | - Raheel Ahmed
- Department of Neurosurgery, University of Wisconsin Madison, Madison, WI, United States of America
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States of America
- * E-mail:
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27
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Zhao X, Yoshida N, Ueda T, Sugano H, Tanaka T. Epileptic seizure detection by using interpretable machine learning models. J Neural Eng 2023; 20. [PMID: 36603215 DOI: 10.1088/1741-2552/acb089] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 01/05/2023] [Indexed: 01/06/2023]
Abstract
Objective.Accurate detection of epileptic seizures using electroencephalogram (EEG) data is essential for epilepsy diagnosis, but the visual diagnostic process for clinical experts is a time-consuming task. To improve efficiency, some seizure detection methods have been proposed. Regardless of traditional or machine learning methods, the results identify only seizures and non-seizures. Our goal is not only to detect seizures but also to explain the basis for detection and provide reference information to clinical experts.Approach.In this study, we follow the visual diagnosis mechanism used by clinical experts that directly processes plotted EEG image data and apply some commonly used models of LeNet, VGG, deep residual network (ResNet), and vision transformer (ViT) to the EEG image classification task. Before using these models, we propose a data augmentation method using random channel ordering (RCO), which adjusts the channel order to generate new images. The Gradient-weighted class activation mapping (Grad-CAM) and attention layer methods are used to interpret the models.Main results.The RCO method can balance the dataset in seizure and non-seizure classes. The models achieved good performance in the seizure detection task. Moreover, the Grad-CAM and attention layer methods explained the detection basis of the model very well and calculate a value that measures the seizure degree.Significance.Processing EEG data in the form of images can flexibility to use a variety of machine learning models. The imbalance problem that exists widely in clinical practice is well solved by the RCO method. Since the method follows the visual diagnosis mechanism of clinical experts, the model interpretation results can be presented to clinical experts intuitively, and the quantitative information provided by the model is also a good diagnostic reference.
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Affiliation(s)
- Xuyang Zhao
- Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Tokyo, Japan
| | | | - Tetsuya Ueda
- Faculty of Medicine, Juntendo University, Tokyo, Japan
| | | | - Toshihisa Tanaka
- Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Tokyo, Japan
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28
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Han Y, Bai Y, Liu Q, Zhao Y, Chen T, Wang W, Ni G. Assessing vestibular function using electroencephalogram rhythms evoked during the caloric test. Front Neurol 2023; 14:1126214. [PMID: 36908620 PMCID: PMC9996014 DOI: 10.3389/fneur.2023.1126214] [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: 12/17/2022] [Accepted: 01/31/2023] [Indexed: 02/25/2023] Open
Abstract
Introduction The vestibular system is responsible for motion perception and balance preservation in the body. The vestibular function examination is useful for determining the cause of associated symptoms, diagnosis, and therapy of the patients. The associated cerebral cortex processes and integrates information and is the ultimate perceptual site for vestibular-related symptoms. In recent clinical examinations, less consideration has been given to the cortex associated with the vestibular system. As a result, it is crucial to increase focus on the expression of the cortical level while evaluating vestibular function. From the viewpoint of neuroelectrophysiology, electroencephalograms (EEG) can enhance the assessments of vestibular function at the cortex level. Methods This study recorded nystagmus and EEG data throughout the caloric test. Four phases were considered according to the vestibular activation status: before activation, activation, fixation suppression, and recovery. In different phases, the distribution and changes of the relative power of the EEG rhythms (delta, theta, alpha, and beta) were analyzed, and the correlation between EEG characteristics and nystagmus was also investigated. Results The results showed that, when the vestibule was activated, the alpha power of the occipital region increased, and the beta power of the central and top regions and the occipital region on the left decreased. The changes in the alpha and beta rhythms significantly correlate with nystagmus values in left warm stimulation. Discussion Our findings offer a fresh perspective on cortical electrophysiology for the assessment of vestibular function by demonstrating that the relative power change in EEG rhythms can be used to assess vestibular function.
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Affiliation(s)
- Yutong Han
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
| | - Yanru Bai
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
| | - Qiang Liu
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China.,Institute of Otolaryngology of Tianjin, Tianjin, China.,Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
| | - Yuncheng Zhao
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China.,Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Taisheng Chen
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China.,Institute of Otolaryngology of Tianjin, Tianjin, China.,Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
| | - Wei Wang
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China.,Institute of Otolaryngology of Tianjin, Tianjin, China.,Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
| | - Guangjian Ni
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
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29
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Wang J, Ge X, Shi Y, Sun M, Gong Q, Wang H, Huang W. Dual-Modal Information Bottleneck Network for Seizure Detection. Int J Neural Syst 2023; 33:2250061. [PMID: 36599663 DOI: 10.1142/s0129065722500617] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimensional (1D) features of EEG signals into many segments and employ 1D-CNNs. Moreover, these investigations are further constrained by the absence of consideration for temporal links between time series segments or spectrogram images. Therefore, we propose a Dual-Modal Information Bottleneck (Dual-modal IB) network for EEG seizure detection. The network extracts EEG features from both time series and spectrogram dimensions, allowing information from different modalities to pass through the Dual-modal IB, requiring the model to gather and condense the most pertinent information in each modality and only share what is necessary. Specifically, we make full use of the information shared between the two modality representations to obtain key information for seizure detection and to remove irrelevant feature between the two modalities. In addition, to explore the intrinsic temporal dependencies, we further introduce a bidirectional long-short-term memory (BiLSTM) for Dual-modal IB model, which is used to model the temporal relationships between the information after each modality is extracted by convolutional neural network (CNN). For CHB-MIT dataset, the proposed framework can achieve an average segment-based sensitivity of 97.42%, specificity of 99.32%, accuracy of 98.29%, and an average event-based sensitivity of 96.02%, false detection rate (FDR) of 0.70/h. We release our code at https://github.com/LLLL1021/Dual-modal-IB.
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Affiliation(s)
- Jiale Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Xinting Ge
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Yunfeng Shi
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Mengxue Sun
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Qingtao Gong
- Ulsan Ship and Ocean College, Ludong University, Yantai 264025, P. R. China
| | - Haipeng Wang
- Institute of Information Fusion, Naval, Aviation University, Yantai 264001, P. R. China
| | - Wenhui Huang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
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30
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Altun S, Alkan A, Altun H. Automatic Diagnosis of Attention Deficit Hyperactivity Disorder with Continuous Wavelet Transform and Convolutional Neural Network. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE 2022; 20:715-724. [DOI: 10.9758/cpn.2022.20.4.715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/13/2021] [Accepted: 10/30/2021] [Indexed: 11/07/2022]
Affiliation(s)
- Sinan Altun
- Department of Electrical and Electronics Engineering, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey
| | - Ahmet Alkan
- Department of Electrical and Electronics Engineering, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey
| | - Hatice Altun
- Department of Child and Adolescent Psychiatry, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey
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31
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RNN and BiLSTM Fusion for Accurate Automatic Epileptic Seizure Diagnosis Using EEG Signals. LIFE (BASEL, SWITZERLAND) 2022; 12:life12121946. [PMID: 36556313 PMCID: PMC9784456 DOI: 10.3390/life12121946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022]
Abstract
Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to seizures alone. They comprise a wide spectrum of problems that might impair and reduce quality of life. Even with medication, 30% of epilepsy patients still have recurring seizures. An epileptic seizure is caused by significant neuronal electrical activity, which affects brain activity. EEG shows these changes as high-amplitude spiky and sluggish waves. Recognizing seizures on an electroencephalogram (EEG) manually by a professional neurologist is a time-consuming and labor-intensive process, hence an efficient automated approach is necessary for the identification of epileptic seizure. One technique to increase the speed and accuracy with which a diagnosis of epileptic seizures could be made is by utilizing computer-aided diagnosis systems that are built on deep neural networks, or DNN. This study introduces a fusion of recurrent neural networks (RNNs) and bi-directional long short-term memories (BiLSTMs) for automatic epileptic seizure identification via EEG signal processing in order to tackle the aforementioned informational challenges. An electroencephalogram's (EEG) raw data were first normalized after undergoing pre-processing. A RNN model was fed the normalized EEG sequence data and trained to accurately extract features from the data. Afterwards, the features were passed to the BiLSTM layers for processing so that further temporal information could be retrieved. In addition, the proposed RNN-BiLSTM model was tested in an experimental setting using the freely accessible UCI epileptic seizure dataset. Experimental findings of the suggested model have achieved avg values of 98.90%, 98.50%, 98. 20%, and 98.60%, respectively, for accuracy, sensitivity, precision, and specificity. To further verify the new model's efficacy, it is compared to other models, such as the RNN-LSTM and the RNN-GRU learning models, and is shown to have improved the same metrics by 1.8%, 1.69%, 1.95%, and 2.2% on using 5-fold. Additionally, the proposed method was compared to state-of-the-art approaches and proved to be a more accurate categorization of such techniques.
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32
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Yuan S, Mu J, Zhou W, Dai LY, Liu JX, Wang J, Liu X. Automatic Epileptic Seizure Detection Using Graph-Regularized Non-Negative Matrix Factorization and Kernel-Based Robust Probabilistic Collaborative Representation. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2641-2650. [PMID: 36063515 DOI: 10.1109/tnsre.2022.3204533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Automatic seizure detection system can serve as a meaningful clinical tool for the treatment and analysis of epilepsy using electroencephalogram (EEG) and has obtained rapid development. An automatic detection of epileptic seizure method based on kernel-based robust probabilistic collaborative representation (ProCRC) combined with graph-regularized non-negative matrix factorization (GNMF) is proposed in this work. The raw EEG signals are pre-processed through the wavelet transform to obtain time-frequency distribution of EEG signals as preliminary feature information and GNMF is further employed for dimension reduction, retaining and enhancing the productive feature information of EEG signals. Then, the test sample is represented using robust ProCRC that can decide whether the testing sample belongs to each class (seizure or non-seizure) by jointly maximizing the likelihood. In addition, the kernel trick is applied to improve the separability of non-linear high dimensional EEG signals in robust ProCRC. Finally, post-processing techniques are introduced to generate more accurate and reliable results. The average epoch-based sensitivity of 96.48%, event-based sensitivity of 93.65% and specificity of 98.55% are acquired in this method, which is evaluated on the public Freiburg EEG database.
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33
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Hinchliffe C, Yogarajah M, Elkommos S, Tang H, Abasolo D. Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1348. [PMID: 37420367 DOI: 10.3390/e24101348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 09/13/2022] [Accepted: 09/17/2022] [Indexed: 07/09/2023]
Abstract
Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs.
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Affiliation(s)
- Chloe Hinchliffe
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Mahinda Yogarajah
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, National Hospital for Neurology and Neurosurgery, University College London Hospitals, Epilepsy Society, London WC1E 6BT, UK
- Neurosciences Research Centre, St George's University of London, London SW17 0RE, UK
- Atkinson Morley Regional Neuroscience Centre, St George's Hospital, London SW17 0QT, UK
| | - Samia Elkommos
- Atkinson Morley Regional Neuroscience Centre, St George's Hospital, London SW17 0QT, UK
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London WC2R 2LS, UK
| | - Hongying Tang
- Department of Computer Science, University of Surrey, Guildford GU2 7XH, UK
| | - Daniel Abasolo
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, University of Surrey, Guildford GU2 7XH, UK
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N.J. S, M.S.P. S, S. TG. EEG-based classification of normal and seizure types using relaxed local neighbour difference pattern and artificial neural network. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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35
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A novel technique for stress detection from EEG signal using hybrid deep learning model. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07540-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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36
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Francisco-Vicencio MA, Góngora-Rivera F, Ortiz-Jiménez X, Martinez-Peon D. Sustained attention variation monitoring through EEG effective connectivity. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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37
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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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38
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Xu F, Dong G, Li J, Yang Q, Wang L, Zhao Y, Yan Y, Zhao J, Pang S, Guo D, Zhang Y, Leng J. Deep Convolution Generative Adversarial Network Based Electroencephalogram Data Augmentation For Post-Stroke Rehabilitation With Motor Imagery. Int J Neural Syst 2022; 32:2250039. [DOI: 10.1142/s0129065722500393] [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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39
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Constable PA, Marmolejo-Ramos F, Gauthier M, Lee IO, Skuse DH, Thompson DA. Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder. Front Neurosci 2022; 16:890461. [PMID: 35733935 PMCID: PMC9207322 DOI: 10.3389/fnins.2022.890461] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/09/2022] [Indexed: 12/30/2022] Open
Abstract
Background To evaluate the electroretinogram waveform in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) using a discrete wavelet transform (DWT) approach. Methods A total of 55 ASD, 15 ADHD and 156 control individuals took part in this study. Full field light-adapted electroretinograms (ERGs) were recorded using a Troland protocol, accounting for pupil size, with five flash strengths ranging from –0.12 to 1.20 log photopic cd.s.m–2. A DWT analysis was performed using the Haar wavelet on the waveforms to examine the energy within the time windows of the a- and b-waves and the oscillatory potentials (OPs) which yielded six DWT coefficients related to these parameters. The central frequency bands were from 20–160 Hz relating to the a-wave, b-wave and OPs represented by the coefficients: a20, a40, b20, b40, op80, and op160, respectively. In addition, the b-wave amplitude and percentage energy contribution of the OPs (%OPs) in the total ERG broadband energy was evaluated. Results There were significant group differences (p < 0.001) in the coefficients corresponding to energies in the b-wave (b20, b40) and OPs (op80 and op160) as well as the b-wave amplitude. Notable differences between the ADHD and control groups were found in the b20 and b40 coefficients. In contrast, the greatest differences between the ASD and control group were found in the op80 and op160 coefficients. The b-wave amplitude showed both ASD and ADHD significant group differences from the control participants, for flash strengths greater than 0.4 log photopic cd.s.m–2 (p < 0.001). Conclusion This methodological approach may provide insights about neuronal activity in studies investigating group differences where retinal signaling may be altered through neurodevelopment or neurodegenerative conditions. However, further work will be required to determine if retinal signal analysis can offer a classification model for neurodevelopmental conditions in which there is a co-occurrence such as ASD and ADHD.
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Affiliation(s)
- Paul A. Constable
- College of Nursing and Health Sciences, Caring Futures Institute, Flinders University, Adelaide, SA, Australia
- *Correspondence: Paul A. Constable,
| | - Fernando Marmolejo-Ramos
- Centre for Change and Complexity in Learning, The University of South Australia, Adelaide, SA, Australia
| | - Mercedes Gauthier
- Department of Ophthalmology & Visual Sciences, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC, Canada
| | - Irene O. Lee
- Behavioural and Brain Sciences Unit, Population, Policy and Practice Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - David H. Skuse
- Behavioural and Brain Sciences Unit, Population, Policy and Practice Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Dorothy A. Thompson
- The Tony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Trust, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
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Abstract
Epilepsy is one of the most common brain diseases that affects more than 1% of the world’s population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and require professional skills. This paper presents a novel automatic technique that involves the extraction of specific features from epileptic seizures’ EEG signals using dual-tree complex wavelet transform (DTCWT) and classifying them into one of the seven types of seizures, including absence, complex-partial, focal non-specific, generalized non-specific, simple-partial, tonic-clonic, and tonic seizures. We evaluated the proposed technique on the TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with the existing state-of-the-art techniques using the overall F1-score due to class imbalance of seizure types. Our proposed technique achieved the best results of a weighted F1-score of 99.1% and 74.7% for seizure-wise and patient-wise classification, respectively, thereby setting new benchmark results for this dataset.
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Application of Deep Learning and WT-SST in Localization of Epileptogenic Zone Using Epileptic EEG Signals. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12104879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Focal and non-focal Electroencephalogram (EEG) signals have proved to be effective techniques for identifying areas in the brain that are affected by epileptic seizures, known as the epileptogenic zones. The detection of the location of focal EEG signals and the time of seizure occurrence are vital information that help doctors treat focal epileptic seizures using a surgical method. This paper proposed a computer-aided detection (CAD) system for detecting and classifying focal and non-focal EEG signals as the manual process is time-consuming, prone to error, and tedious. The proposed technique employs time-frequency features, statistical, and nonlinear approaches to form a robust features extraction technique. Four detection and classification techniques for focal and non-focal EEG signals were proposed. (1). Combined hybrid features with Support Vector Machine (Hybrid-SVM) (2). Discrete Wavelet Transform with Deep Learning Network (DWT-DNN) (3). Combined hybrid features with DNN (Hybrid-DNN) as an optimized DNN model. Lastly, (4). A newly proposed technique using Wavelet Synchrosqueezing Transform-Deep Convolutional Neural Network (WTSST-DCNN). Prior to feeding the features to classifiers, statistical analyses, including t-tests, were deployed to obtain relevant and significant features at each approach. The proposed feature extraction technique and classification proved effective and suitable for smart Internet of Medical Things (IoMT) devices as performance parameters of accuracy, sensitivity, and specificity are higher than recently related works with a value of 99.7%, 99.5%, and 99.7% respectively.
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Lian J, Xu F. Spatial Enhanced Pattern Through Graph Convolutional Neural Network for Epileptic EEG Identification. Int J Neural Syst 2022; 32:2250033. [DOI: 10.1142/s0129065722500332] [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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43
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Koch MJ, Duy PQ, Grannan BL, Patel AB, Raymond SB, Agarwalla PK, Kahle KT, Butler WE. Angiographic Pulse Wave Coherence in the Human Brain. Front Bioeng Biotechnol 2022; 10:873530. [PMID: 35592552 PMCID: PMC9110661 DOI: 10.3389/fbioe.2022.873530] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
A stroke volume of arterial blood that arrives to the brain housed in the rigid cranium must be matched over the cardiac cycle by an equivalent volume of ejected venous blood. We hypothesize that the brain maintains this equilibrium by organizing coherent arterial and venous pulse waves. To test this hypothesis, we applied wavelet computational methods to diagnostic cerebral angiograms in four human patients, permitting the capture and analysis of cardiac frequency phenomena from fluoroscopic images acquired at faster than cardiac rate. We found that the cardiac frequency reciprocal phase of a small region of interest (ROI) in a named artery predicts venous anatomy pixel-wise and that the predicted pixels reconstitute venous bolus passage timing. Likewise, a small ROI in a named vein predicts arterial anatomy and arterial bolus passage timing. The predicted arterial and venous pixel groups maintain phase complementarity across the bolus travel. We thus establish a novel computational method to analyze vascular pulse waves from minimally invasive cerebral angiograms and provide the first direct evidence of arteriovenous coupling in the intact human brain. This phenomenon of arteriovenous coupling may be a physiologic mechanism for how the brain precisely maintains mechanical equilibrium against volume displacement and kinetic energy transfer resulting from cyclical deformations with each heartbeat. The study also paves the way to study deranged arteriovenous coupling as an underappreciated pathophysiologic disturbance in a myriad of neurological pathologies linked by mechanical disequilibrium.
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Affiliation(s)
- Matthew J. Koch
- Department of Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Phan Q. Duy
- Medical Scientist Training Program, Yale University School of Medicine, New Haven, CT, United States
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, United States
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, United States
| | - Benjamin L. Grannan
- Department of Neurosurgery, University of Washington Medicine, Seattle, WA, United States
| | - Aman B. Patel
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, United States
| | - Scott B. Raymond
- Department of Radiology, University of Vermont, Burlington, VT, United States
| | - Pankaj K. Agarwalla
- Department of Neurosurgery, Rutgers New Jersey Medical School, Newark, NJ, United States
| | - Kristopher T. Kahle
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, United States
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, United States
- Broad Institute of MIT and Harvard, Cambridge, MA, United States
- MGH Hydrocephalus and Neurodevelopmental Disorders Program, Massachusetts General Hospital, Boston, MA, United States
| | - William E. Butler
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, United States
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Abnormal EEG Signal Energy in the Elderly: A Wavelet Analysis of Event-related Potentials During a Stroop Task. J Neurosci Methods 2022; 376:109608. [PMID: 35487316 DOI: 10.1016/j.jneumeth.2022.109608] [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: 04/03/2021] [Revised: 01/17/2022] [Accepted: 04/21/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Previous work showed that elderly with excess in theta activity in their resting state electroencephalogram (EEG) are at higher risk of cognitive decline than those with a normal EEG. By using event-related potentials (ERP) during a counting Stroop task, our prior work showed that elderly with theta excess have a large P300 component compared with normal EEG group. This increased activity could be related to a higher EEG signal energy used during this task. NEW METHOD By wavelet analysis applied to ERP obtained during a counting Stroop task we quantified the energy in the different frequency bands of a group of elderly with altered EEG. RESULTS In theta and alpha bands, the total energy was higher in elderly subjects with theta excess, specifically in the stimulus categorization window (258-516 ms). Both groups solved the task with similar efficiency. COMPARISON WITH EXISTING METHODS The traditional ERP analysis in elderly compares voltage among conditions and groups for a given time windows, while the frequency composition is not usually examined. We complemented our previous ERP analysis using a wavelet methodology. Furthermore, we showed the advantages of wavelet analysis over Short Time Fourier Transform when exploring EEG signal during this task. CONCLUSIONS The higher EEG signal energy in ERP might reflect undergoing neurobiological mechanisms that allow the elderly with theta excess to cope with the cognitive task with similar behavioral results as the normal EEG group. This increased energy could promote a metabolic and cellular dysregulation causing a greater decline in cognitive function.
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Hossain MS, Chowdhury MEH, Reaz MBI, Ali SHM, Bakar AAA, Kiranyaz S, Khandakar A, Alhatou M, Habib R, Hossain MM. Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22093169. [PMID: 35590859 DOI: 10.1109/access.2022.3159155] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 05/27/2023]
Abstract
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.
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Affiliation(s)
- Md Shafayet Hossain
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | | | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Ahmad Ashrif A Bakar
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Mohammed Alhatou
- Neuromuscular Division, Department of Neurology, Al-Khor Branch, Hamad General Hospital, Doha 3050, Qatar
| | - Rumana Habib
- Department of Neurology, BIRDEM General Hospital, Dhaka 1000, Bangladesh
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Harishvijey A, Benadict Raja J. Automated technique for EEG signal processing to detect seizure with optimized Variable Gaussian Filter and Fuzzy RBFELM classifier. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103450] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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47
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ZHANG JUNAN, GU LIPING, CHEN YONGQIANG, ZHU GENG, OU LANG, WANG LIYAN, LI XIAOOU, ZHONG LICHANG. MULTI-FEATURE FUSION EMOTION RECOGNITION BASED ON RESTING EEG. J MECH MED BIOL 2022. [DOI: 10.1142/s0219519422400024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
An important task of brain–computer interface (BCI) research is to read or decode mental content from a large number of electroencephalographic (EEG) signals, which is an exciting, attractive, and challenging goal. In this paper, resting EEG data are collected from spontaneous EEG, and 16 random features such as correlation coefficient, covariance, and brainpower spectrum are extracted as reference vectors. In the subsequent emotion recognition experiment, the same feature information is extracted and separated from the EEG signal, and the translation and normalization processing are carried out based on the resting-state features. Finally, with the machine learning methods such as [Formula: see text]-means clustering and multi-feature fusion, the positive, negative, and neutral emotional characteristic parameters were correctly separated. In a group of 12 subjects, the correct recognition rate of visual evoked positive, negative, and neutral emotions reached 83.9%, which was better than the literature mentioned in this paper. Another highlight of this method is that it can quickly, accurately, and efficiently select the number of features with the best matching and the least resource consumption from multiple features and multiple potential acquisition points. Further analysis and comparison of EEG characteristics can find the relationship between specific stimuli and corresponding EEG characteristic signals.
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Affiliation(s)
- JUN-AN ZHANG
- Medical Instrumentation College, Shanghai University of Medicine and Health Sciences, Shanghai Wearable Medical Technology and Device Engineering Research Center, Shanghai 201318, P. R. China
| | - LIPING GU
- Department of Medical Ultrasound, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai 200240, P. R. China
| | - YONGQIANG CHEN
- Medical Instrumentation College, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
| | - GENG ZHU
- Medical Instrumentation College, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
| | - LANG OU
- West China Second University Hospital, Sichuan University, Chengdu 610044, P. R. China
| | - LIYAN WANG
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
| | - XIAOOU LI
- Medical Instrumentation College, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
- Shanghai Wearable Medical Technology and Device Engineering Research Center, Shanghai 200240, P. R. China
| | - LICHANG ZHONG
- Department of Medical Ultrasound, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai 200240, P. R. China
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Ambati R, Raja S, Al-Hameed M, John T, Arjoune Y, Shekhar R. Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG. SENSORS 2022; 22:s22051852. [PMID: 35271005 PMCID: PMC8914704 DOI: 10.3390/s22051852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/16/2022]
Abstract
Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture—the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of 91.14%±1.77% and a specificity of 98.77%±0.57% with 5 s epoch duration. The system’s latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.
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Affiliation(s)
- Ravi Ambati
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (R.A.); (T.J.); (Y.A.)
| | - Shanker Raja
- National Neuroscience Institute, King Fahad Medical City, Riyadh 12231, Saudi Arabia; (S.R.); (M.A.-H.)
| | - Majed Al-Hameed
- National Neuroscience Institute, King Fahad Medical City, Riyadh 12231, Saudi Arabia; (S.R.); (M.A.-H.)
| | - Titus John
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (R.A.); (T.J.); (Y.A.)
| | - Youness Arjoune
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (R.A.); (T.J.); (Y.A.)
| | - Raj Shekhar
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (R.A.); (T.J.); (Y.A.)
- Correspondence:
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Fast custom wavelet analysis technique for single molecule detection and identification. Nat Commun 2022; 13:1035. [PMID: 35210454 PMCID: PMC8873225 DOI: 10.1038/s41467-022-28703-z] [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: 05/14/2021] [Accepted: 01/20/2022] [Indexed: 02/01/2023] Open
Abstract
Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor. The technique is more accurate than simple peak-finding algorithms and several orders of magnitude faster than existing CWT methods, allowing for real-time data analysis during sensing for the first time. Performance is further increased by applying a custom wavelet to multi-peak signals as demonstrated using amplification-free detection of single bacterial DNAs. A 4x increase in detection rate, a 6x improved error rate, and the ability for extraction of experimental parameters are demonstrated. This cluster-based CWT analysis will enable high-performance, real-time sensing when signal-to-noise is hardware limited, for instance with low-cost sensors in point of care environments. The authors introduce an accurate, fast and efficient technique to analyze sensory data. They use a continuous wavelet transform concept to look for certain patterns in noisy raw data. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor.
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50
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Cherian R, Kanaga EG. Theoretical and Methodological Analysis of EEG based Seizure Detection and Prediction: An Exhaustive Review. J Neurosci Methods 2022; 369:109483. [DOI: 10.1016/j.jneumeth.2022.109483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/13/2022] [Accepted: 01/13/2022] [Indexed: 02/07/2023]
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