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Wu X, Yang J, Shao Y, Chen X. Mental fatigue assessment by an arbitrary channel EEG based on morphological features and LSTM-CNN. Comput Biol Med 2023; 167:107652. [PMID: 37950945 DOI: 10.1016/j.compbiomed.2023.107652] [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: 12/20/2022] [Revised: 10/05/2023] [Accepted: 10/31/2023] [Indexed: 11/13/2023]
Abstract
In order to achieve more sensitive mental fatigue assessment (MFA) based on an arbitrary channel EEG, this study proposed a series of feature extraction methods that combine mathematical morphology (MM), as well as an LSTM-CNN architecture. Firstly, 37 subjects had their resting-state EEGs collected at rested wakefulness (RW) and after 24 h of sleep deprivation (SD) using a 30-channel EEG acquisition device, the RW and SD groups were regarded as the negative and positive groups of mental fatigue, respectively, and the EEG collection were further categorized into two conditions: eye-opened state (EO) and eye-closed state (EC). Then, since MM can reflect the morphological characteristics of EEG rhythms and their potentials relatively independently of the time-frequency analysis and phase calculation, the MM methods were found to better reflect the mental fatigue after SD statistically, whether for single features (ANOVA: p<0.000001), multiple features (clustering by K-means, t-test: p<0.01), or time series feature spaces (calculating CD, t-test: p<0.01) of a single channel. Finally, the LSTM-CNN enhanced the generalization ability when dealing with different single-channel EEG by combining GRUs with convolutional layers: comparing the AUCs of different architectures for MFA based on an arbitrary channel, LSTM-CNN (0.992) > LSTM network (0.94) > CNN (0.831) > MLP (0.754). Moreover, the use of MM also improved the accuracy of analyzed architectures, and the true/false positive rate (TPR/FPR) of the LSTM-CNN architecture for MFA based on an arbitrary channel reached 97.024 %/3.497 %, which provided a feasible solution for the arbitrary channel EEG-based MFA.
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Affiliation(s)
- Xiaolong Wu
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China; Shunde Innovation School, University of Science and Technology Beijing, Guangdong, China
| | - Jianhong Yang
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China; Shunde Innovation School, University of Science and Technology Beijing, Guangdong, China; Technical Support Center for Prevention and Control of Disastrous Accidents in Metal Smelting, University of Science and Technology Beijing, Beijing, China.
| | - Yongcong Shao
- School of Psychology, Beijing Sport University, Beijing, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China
| | - Xuewei Chen
- Institute of Environmental and Operational Medicine, Academy of Military Sciences, Tianjin, China
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Wu X, Yang J. The superiority verification of morphological features in the EEG-based assessment of depression. J Neurosci Methods 2022; 381:109690. [PMID: 36007848 DOI: 10.1016/j.jneumeth.2022.109690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/16/2022] [Accepted: 08/19/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Xiaolong Wu
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Shunde Graduate School, University of Science and Technology Beijing, Guangdong 528399, China.
| | - Jianhong Yang
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Shunde Graduate School, University of Science and Technology Beijing, Guangdong 528399, China; Technical Support Center for Prevention and Control of Disastrous Accidents in Metal Smelting, University of Science and Technology Beijing, Beijing 100083, China.
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Dinh TH, Singh AK, Linh Trung N, Nguyen DN, Lin CT. EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-based Ranking. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1548-1556. [PMID: 35635834 DOI: 10.1109/tnsre.2022.3179255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).
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Wang Z, Mengoni P. Seizure classification with selected frequency bands and EEG montages: a Natural Language Processing approach. Brain Inform 2022; 9:11. [PMID: 35622175 PMCID: PMC9142724 DOI: 10.1186/s40708-022-00159-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/17/2022] [Indexed: 11/10/2022] Open
Abstract
Individualized treatment is crucial for epileptic patients with different types of seizures. The differences among patients impact the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection can ease the manual time-consuming and labor-intensive procedure for diagnose seizure in the clinical setting. In this paper, we present an electroencephalography (EEG) frequency bands (sub-bands) and montages selection (sub-zones) method for classifier training that exploits Natural Language Processing from individual patients' clinical report. The proposed approach is targeting for individualized treatment. We integrated the prior knowledge from patient's reports into the classifier-building process, mimicking the authentic thinking process of experienced neurologist's when diagnosing seizure using EEG. The keywords from clinical documents are mapped to the EEG data in terms of frequency bands and scalp EEG electrodes. The data of experiments are from the Temple University Hospital EEG seizure corpus, and the dataset is divided based on each group of patients with same seizure type and same recording electrode references. The classifier includes Random Forest, Support Vector Machine and Multi-Layer Perceptron. The classification performance indicates that competitive results can be achieve with a small portion of EEG the data. Using the sub-zones selection for Generalized Seizures (GNSZ) on all three electrodes, data are reduced by nearly 50% while the performance metrics remain at the same level with the whole frequency and zones. Moreover, when selecting by sub-zones and sub-bands together for GNSZ with Linked Ears reference, the data range reduced to 0.3% of whole range, and the performance deviates less than 3% from the results with whole range of data. Results show that using proposed approach may lead to more efficient implementations of the seizure classifier to be executed on power-efficient devices for long lasting real-time seizures detection.
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Affiliation(s)
- Ziwei Wang
- Institute of Interdisciplinary Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China
| | - Paolo Mengoni
- Department of Journalism, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China.
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Kleeva D, Soghoyan G, Komoltsev I, Sinkin M, Ossadtchi A. Fast parametric curve matching (FPCM) for automatic spike detection. J Neural Eng 2022; 19. [PMID: 35439749 DOI: 10.1088/1741-2552/ac682a] [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: 12/30/2021] [Accepted: 04/18/2022] [Indexed: 11/12/2022]
Abstract
Epilepsy is a widely spread neurological disease, whose treatment often requires resection of the pathological cortical tissue. Interictal spike analysis observed in the non-invasively collected EEG or MEG data offers a way to localize epileptogenic cortical structures for surgery planning purposes. While a plethora of automatic spike detection techniques have been developed each with its own assumptions and limitations, non of them is ideal and the best results are achieved when the output of several automatic spike detectors are combined. This is especially true in the low signal-to-noise ratio conditions. To this end we propose a novel biomimetic approach for automatic spike detection based on a constrained mixed spline machinery that we dub as fast parametric curve matching (FPCM). Using the peak-wave shape parametrization, the constrained parametric morphological model is constructed and convolved with the observed multichannel data to very efficiently determine mixed spline parameters corresponding to each time-point in the dataset. Then the logical predicates that directly map to the expected interictal event morphology allow us to accomplish the spike detection task. The results of simulations mimicking typical low SNR scenario show the robustness and high ROC AUC values of the FPCM method as compared to the spike detection performed by the means of more conventional approaches such as wavelet decomposition, template matching or simple amplitude thresholding. Applied to the real MEG and EEG data from the human patients and to ECoG data from the rat, the FPCM technique demonstrates reliable detection of the interictal events and localization of epileptogenic zones concordant with independent conclusions made by the epileptologist. Since the FPCM is computationally light, tolerant to high amplitude artifacts and flexible to accommodate verbalized descriptions of the arbitrary target morphology, it may complement the existing arsenal of means for analysis of noisy interictal datasets.
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Affiliation(s)
- Daria Kleeva
- Center for Bioelectric Interfaces, Higher School of Economics, Moscow, Russia
| | - Gurgen Soghoyan
- Center for Bioelectric Interfaces, Higher School of Economics, Moscow, Russia
| | - Ilia Komoltsev
- Laboratory of Functional Biochemistry of the Nervous System, Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia.,Moscow Research and Clinical Center for Neuropsychiatry of the Healthcare Department of Moscow, Moscow, Russia
| | - Mikhail Sinkin
- A I Evdokimov Moscow State University of Medicical Dentistry, Moscow, Russia.,N V Sklifosovsky Research Institute of Emergency Medicine, Moscow, Russia
| | - Alexei Ossadtchi
- Center for Bioelectric Interfaces, Higher School of Economics, Moscow, Russia.,AIRI, Artificial Intelligence Research Institute, Moscow, Russia
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Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Sharmila A, Geethanjali P. A review on the pattern detection methods for epilepsy seizure detection from EEG signals. ACTA ACUST UNITED AC 2019; 64:507-517. [PMID: 31026222 DOI: 10.1515/bmt-2017-0233] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Accepted: 12/05/2018] [Indexed: 11/15/2022]
Abstract
Over several years, research had been conducted for the detection of epileptic seizures to support an automatic diagnosis system to comfort the clinicians' encumbrance. In this regard, a number of research papers have been published for the identification of epileptic seizures. A thorough review of all these papers is required. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics. This consecutively necessitates the identification of the pattern recognition technique to efficiently differentiate EEG epileptic data from the EEG data of various conditions.
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Affiliation(s)
- Ashok Sharmila
- School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, Tamilnadu, India
| | - Purusothaman Geethanjali
- School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, Tamilnadu, India
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Abstract
Over many decades, research is being attempted for the detection of epileptic seizure to support for automatic diagnosis system to help clinicians from burdensome work. In this respect, an enormous number of research papers is published for identification of epileptic seizure. It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection of an epileptic seizure. More than 100 research papers have been discussed to discern the techniques for detecting the epileptic seizure. Further, the literature survey shows that the pattern recognition required to detect epileptic seizure varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG epileptic data from a various condition of EEG data.
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Affiliation(s)
- A Sharmila
- a School of Electrical Engineering , VIT , Vellore , India
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A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing. MACHINES 2017. [DOI: 10.3390/machines5040021] [Citation(s) in RCA: 214] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Irum I, Sharif M, Raza M, Mohsin S. A Nonlinear Hybrid Filter for Salt & Pepper Noise Removal from Color Images. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/s1665-6423(15)30015-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Shibasaki H, Nakamura M, Sugi T, Nishida S, Nagamine T, Ikeda A. Automatic interpretation and writing report of the adult waking electroencephalogram. Clin Neurophysiol 2014; 125:1081-94. [DOI: 10.1016/j.clinph.2013.12.114] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Revised: 12/03/2013] [Accepted: 12/17/2013] [Indexed: 11/28/2022]
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A novel spatiotemporal analysis of peri-ictal spiking to probe the relation of spikes and seizures in epilepsy. Ann Biomed Eng 2014; 42:1606-17. [PMID: 24740852 DOI: 10.1007/s10439-014-1004-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2013] [Accepted: 03/28/2014] [Indexed: 10/25/2022]
Abstract
The relation between epileptic spikes and seizures is an important but still unresolved question in epilepsy research. Preclinical and clinical studies have produced inconclusive results on the causality or even on the existence of such a relation. We set to investigate this relation taking in consideration seizure severity and spatial extent of spike rate. We developed a novel automated spike detection algorithm based on morphological filtering techniques and then tested the hypothesis that there is a pre-ictal increase and post-ictal decrease of the spatial extent of spike rate. Peri-ictal (around seizures) spikes were detected from intracranial EEG recordings in 5 patients with temporal lobe epilepsy. The 94 recorded seizures were classified into two classes, based on the percentage of brain sites having higher or lower rate of spikes in the pre-ictal compared to post-ictal periods, with a classification accuracy of 87.4%. This seizure classification showed that seizures with increased pre-ictal spike rate and spatial extent compared to the post-ictal period were mostly (83%) clinical seizures, whereas no such statistically significant (α = 0.05) increase was observed peri-ictally in 93% of sub-clinical seizures. These consistent across patients results show the existence of a causal relation between spikes and clinical seizures, and imply resetting of the preceding spiking process by clinical seizures.
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Jia W, Sclabassi RJ, Pon LS, Scheuer ML, Sun M. Spike separation from EEG/MEG data using morphological filter and wavelet transform. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2008; 2006:6137-40. [PMID: 17946360 DOI: 10.1109/iembs.2006.259695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a circular structuring element. Our experimental results have shown that this method is highly effective in spike separation. Comparisons with the wavelet, bandpass filter, empirical mode decomposition (EMD), and independent component analysis (ICA) methods show that the new method is more effective in estimating both spike amplitudes and locations.
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Affiliation(s)
- Wenyan Jia
- Lab. for Comp. Neuroscience, Dept. of Neurosurg., Pittsburgh Univ., Pittsburgh, PA 15260, USA
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Xu G, Wang J, Zhang Q, Zhang S, Zhu J. A spike detection method in EEG based on improved morphological filter. Comput Biol Med 2007; 37:1647-52. [PMID: 17482156 DOI: 10.1016/j.compbiomed.2007.03.005] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2006] [Revised: 03/14/2007] [Accepted: 03/20/2007] [Indexed: 11/15/2022]
Abstract
In this paper, a spike detection method is introduced. Traditional morphological filter is improved for extracting spikes from epileptic EEG signals and two key problems are addressed: morphological operation design and structure elements optimization. An average weighted combination of open-closing and close-opening operation, which can eliminate statistical deflection of amplitude, is utilized to separate background EEG and spikes. Then, according to the characteristic of spike component, the structure elements are constructed with two parabolas and a new criterion is put forward to optimize the structure elements. The proposed method is evaluated using normal and epileptic EEG data recorded from 12 test subjects. A comparison between the improved morphological filter, traditional morphological filter and wavelet analysis with Mexican hat function is presented, which indicates that the improved morphological filter is superior in restraining background activities. We demonstrate that the average detection rate of the improved morphological filter is much higher than that of the other two methods, and there is no false detection for normal EEG signals with the proposed method.
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Affiliation(s)
- Guanghua Xu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China
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Lanquart JP, Dumont M, Linkowski P. QRS artifact elimination on full night sleep EEG. Med Eng Phys 2006; 28:156-65. [PMID: 15939658 DOI: 10.1016/j.medengphy.2005.04.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2004] [Revised: 03/18/2005] [Accepted: 04/12/2005] [Indexed: 10/25/2022]
Abstract
Spectral analysis is now a standard procedure for analyzing the electroencephalograms (EEG) obtained by polysomnographic recordings. These numerical methods assume an artifact-free EEG since artifacts create spurious spectral components. Our aim was the development of a QRS artifact removal technique that might be applied to full night EEG with a minimal human intervention. This technique should handle one EEG channel, with or without use of one ECG channel. Variance minimization, independent component analysis (ICA), morphological filters (MF) have been implemented. Careful attention has been given to define the MF structuring element. The tests on artifact-simulated and real data were checked on the residual ECG spectral components present in the cleaned EEG. The best results are obtained by the MF when the structuring element is an artifact template defined either directly on the EEG or on the ICA ECG component. Further developments are required to identify and subtract the T-wave artifacts.
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Affiliation(s)
- J-P Lanquart
- Sleep Laboratory, Department of Psychiatry, Erasme Academic Hospital Free University of Brussels, Belgium.
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