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Wu JCH, Liao NC, Yang TH, Hsieh CC, Huang JA, Pai YW, Huang YJ, Wu CL, Lu HHS. Deep-Learning-Based Automated Anomaly Detection of EEGs in Intensive Care Units. Bioengineering (Basel) 2024; 11:421. [PMID: 38790288 PMCID: PMC11118603 DOI: 10.3390/bioengineering11050421] [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: 03/27/2024] [Revised: 04/20/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
An intensive care unit (ICU) is a special ward in the hospital for patients who require intensive care. It is equipped with many instruments monitoring patients' vital signs and supported by the medical staff. However, continuous monitoring demands a massive workload of medical care. To ease the burden, we aim to develop an automatic detection model to monitor when brain anomalies occur. In this study, we focus on electroencephalography (EEG), which monitors the brain electroactivity of patients continuously. It is mainly for the diagnosis of brain malfunction. We propose the gated-recurrent-unit-based (GRU-based) model for detecting brain anomalies; it predicts whether the spike or sharp wave happens within a short time window. Based on the banana montage setting, the proposed model exploits characteristics of multiple channels simultaneously to detect anomalies. It is trained, validated, and tested on separated EEG data and achieves more than 90% testing performance on sensitivity, specificity, and balanced accuracy. The proposed anomaly detection model detects the existence of a spike or sharp wave precisely; it will notify the ICU medical staff, who can provide immediate follow-up treatment. Consequently, it can reduce the medical workload in the ICU significantly.
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Affiliation(s)
- Jacky Chung-Hao Wu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan; (J.C.-H.W.); (T.-H.Y.); (C.-C.H.)
| | - Nien-Chen Liao
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung 407219, Taiwan; (N.-C.L.); (Y.-J.H.)
- Department of Neurology, Neurological Institute, Taichung Veterans General Hospital, Taichung 407219, Taiwan; (J.-A.H.); (Y.-W.P.)
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Ta-Hsin Yang
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan; (J.C.-H.W.); (T.-H.Y.); (C.-C.H.)
| | - Chen-Cheng Hsieh
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan; (J.C.-H.W.); (T.-H.Y.); (C.-C.H.)
| | - Jin-An Huang
- Department of Neurology, Neurological Institute, Taichung Veterans General Hospital, Taichung 407219, Taiwan; (J.-A.H.); (Y.-W.P.)
- Department of Health Business Administration, Hungkuang University, Taichung 433304, Taiwan
| | - Yen-Wei Pai
- Department of Neurology, Neurological Institute, Taichung Veterans General Hospital, Taichung 407219, Taiwan; (J.-A.H.); (Y.-W.P.)
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402202, Taiwan
| | - Yi-Jhen Huang
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung 407219, Taiwan; (N.-C.L.); (Y.-J.H.)
| | - Chieh-Liang Wu
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung 407219, Taiwan; (N.-C.L.); (Y.-J.H.)
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402202, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan; (J.C.-H.W.); (T.-H.Y.); (C.-C.H.)
- Department of Statistics and Data Science, Cornell University, Ithaca, NY 14853, USA
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Zhao X, Zhao Q, Tanaka T, Solé-Casals J, Zhou G, Mitsuhashi T, Sugano H, Yoshida N, Cao J. Classification of the Epileptic Seizure Onset Zone Based on Partial Annotation. Cogn Neurodyn 2023; 17:703-713. [PMID: 37265654 PMCID: PMC10229525 DOI: 10.1007/s11571-022-09857-4] [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: 12/20/2021] [Revised: 06/28/2022] [Accepted: 07/11/2022] [Indexed: 02/01/2023] Open
Abstract
Epilepsy is a chronic disorder caused by excessive electrical discharges. Currently, clinical experts identify the seizure onset zone (SOZ) channel through visual judgment based on long-time intracranial electroencephalogram (iEEG), which is a very time-consuming, difficult and experience-based task. Therefore, there is a need for high-accuracy diagnostic aids to reduce the workload of clinical experts. In this article, we propose a method in which, the iEEG is split into the 20-s segment and for each patient, we ask clinical experts to label a part of the data, which is used to train a model and classify the remaining iEEG data. In recent years, machine learning methods have been successfully applied to solve some medical problems. Filtering, entropy and short-time Fourier transform (STFT) are used for extracting features. We compare them to wavelet transform (WT), empirical mode decomposition (EMD) and other traditional methods with the aim of obtaining the best possible discriminating features. Finally, we look for their medical interpretation, which is important for clinical experts. We achieve high-performance results for SOZ and non-SOZ data classification by using the labeled iEEG data and support vector machine (SVM), fully connected neural network (FCNN) and convolutional neural network (CNN) as classification models. In addition, we introduce the positive unlabeled (PU) learning to further reduce the workload of clinical experts. By using PU learning, we can learn a binary classifier with a small amount of labeled data and a large amount of unlabeled data. This can greatly reduce the amount and difficulty of annotation work by clinical experts. All together, we show that using 105 minutes of labeled data we achieve a classification result of 91.46% on average for multiple patients.
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Affiliation(s)
- Xuyang Zhao
- Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Tokyo, Japan
- Tensor Learning Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Qibin Zhao
- Tensor Learning Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Toshihisa Tanaka
- Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Tokyo, Japan
- Tensor Learning Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, Department of Engineering, University of Vic - Central University of Catalonia, Barcelona, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Guoxu Zhou
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | | | | | | | - Jianting Cao
- Tensor Learning Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Graduate School of Engineering, Saitama Institute of Technology, Fukaya, Japan
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3
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Epileptic seizure focus detection from interictal electroencephalogram: a survey. Cogn Neurodyn 2023; 17:1-23. [PMID: 36704629 PMCID: PMC9871145 DOI: 10.1007/s11571-022-09816-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/15/2022] [Accepted: 04/21/2022] [Indexed: 01/29/2023] Open
Abstract
Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.
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4
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Absar N, Das EK, Shoma SN, Khandaker MU, Miraz MH, Faruque MRI, Tamam N, Sulieman A, Pathan RK. The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction. Healthcare (Basel) 2022; 10:healthcare10061137. [PMID: 35742188 PMCID: PMC9222326 DOI: 10.3390/healthcare10061137] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/26/2022] Open
Abstract
The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.
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Affiliation(s)
- Nurul Absar
- Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh; (N.A.); (E.K.D.); (S.N.S.)
| | - Emon Kumar Das
- Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh; (N.A.); (E.K.D.); (S.N.S.)
| | - Shamsun Nahar Shoma
- Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh; (N.A.); (E.K.D.); (S.N.S.)
| | - Mayeen Uddin Khandaker
- Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia
- Department of General Educational Development, Faculty of Science and Information Technology, Daffodil International University, DIU Rd, Dhaka 1341, Bangladesh
- Correspondence: author:
| | - Mahadi Hasan Miraz
- Department of Business Analytics, Sunway University, Petaling Jaya 47500, Selangor, Malaysia;
| | - M. R. I. Faruque
- Space Science Center, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia;
| | - Nissren Tamam
- Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Abdelmoneim Sulieman
- Department of Radiology and Medical Imaging, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia;
| | - Refat Khan Pathan
- Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia;
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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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6
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KHOUMA O, DIOP I, FALL PA, NDIAYE ML, FARSSI SM, OUSSAMATOU AM, DIOUF B. Novel Classification Method of Spikes Morphology in EEG Signal Using Machine Learning. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.procs.2019.01.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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7
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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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8
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Abd El-Samie FE, Alotaiby TN, Khalid MI, Alshebeili SA, Aldosari SA. A Review of EEG and MEG Epileptic Spike Detection Algorithms. IEEE ACCESS 2018; 6:60673-60688. [DOI: 10.1109/access.2018.2875487] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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9
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Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:3035606. [PMID: 29118962 PMCID: PMC5651155 DOI: 10.1155/2017/3035606] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 08/06/2017] [Accepted: 09/13/2017] [Indexed: 11/18/2022]
Abstract
Epilepsy is a neurological disorder that affects millions of people worldwide. Monitoring the brain activities and identifying the seizure source which starts with spike detection are important steps for epilepsy treatment. Magnetoencephalography (MEG) is an emerging epileptic diagnostic tool with high-density sensors; this makes manual analysis a challenging task due to the vast amount of MEG data. This paper explores the use of eight statistical features and genetic programing (GP) with the K-nearest neighbor (KNN) for interictal spike detection. The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification. The effectiveness of the proposed approach has been evaluated using real MEG data obtained from 28 epileptic patients. It has achieved a 91.75% average sensitivity and 92.99% average specificity.
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10
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Zahra A, Kanwal N, ur Rehman N, Ehsan S, McDonald-Maier KD. Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition. Comput Biol Med 2017; 88:132-141. [DOI: 10.1016/j.compbiomed.2017.07.010] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Revised: 06/24/2017] [Accepted: 07/06/2017] [Indexed: 10/19/2022]
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11
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lin D, Vasilakos AV, Tang Y, Yao Y. Neural networks for computer-aided diagnosis in medicine: A review. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.08.039] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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12
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Zacharaki EI, Mporas I, Garganis K, Megalooikonomou V. Spike pattern recognition by supervised classification in low dimensional embedding space. Brain Inform 2016; 3:73-83. [PMID: 27747608 PMCID: PMC4883172 DOI: 10.1007/s40708-016-0044-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 02/24/2016] [Indexed: 11/13/2022] Open
Abstract
Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts' manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min-1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.
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Affiliation(s)
- Evangelia I Zacharaki
- Department of Computer Engineering and Informatics, University of Patras, Patras, Greece.
- Center for Visual Computing, CentraleSupélec/Galen Team, INRIA, Paris, France.
| | - Iosif Mporas
- Department of Computer Engineering and Informatics, University of Patras, Patras, Greece
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Quintero-Rincón A, Pereyra M, D’Giano C, Batatia H, Risk M. A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals. ACTA ACUST UNITED AC 2016. [DOI: 10.1088/1742-6596/705/1/012032] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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14
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Tieng QM, Kharatishvili I, Chen M, Reutens DC. Mouse EEG spike detection based on the adapted continuous wavelet transform. J Neural Eng 2016; 13:026018. [PMID: 26859447 DOI: 10.1088/1741-2560/13/2/026018] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. APPROACH A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. MAIN RESULT The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. SIGNIFICANCE Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.
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Smart O, Burrell L. Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2015; 39:198-214. [PMID: 25580059 PMCID: PMC4285716 DOI: 10.1016/j.engappai.2014.12.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient.
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Affiliation(s)
- Otis Smart
- Corresponding author: Otis Smart, PhD, Department of Neurosurgery, Emory University School of Medicine, Woodruff Memorial Research Building, 101 Woodruff Circle, Room 6329, Atlanta, GA 30322, USA, , 404.423.8503 (phone), 404.712.8576 (fax)
| | - Lauren Burrell
- Intelligent Control Systems Laboratory, Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
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16
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kumar SU, Inbarani HH. A Novel Neighborhood Rough Set Based Classification Approach for Medical Diagnosis. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.procs.2015.03.216] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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17
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Analysis of EEG Signals for Detection of Epileptic Seizure Using Hybrid Feature Set. THEORY AND APPLICATIONS OF APPLIED ELECTROMAGNETICS 2015. [DOI: 10.1007/978-3-319-17269-9_6] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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18
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Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization. ScientificWorldJournal 2014; 2014:973063. [PMID: 25243236 PMCID: PMC4157008 DOI: 10.1155/2014/973063] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Accepted: 07/30/2014] [Indexed: 11/17/2022] Open
Abstract
Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.
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19
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Liu YC, Lin CCK, Tsai JJ, Sun YN. Model-based spike detection of epileptic EEG data. SENSORS 2013; 13:12536-47. [PMID: 24048343 PMCID: PMC3821325 DOI: 10.3390/s130912536] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 09/06/2013] [Accepted: 09/13/2013] [Indexed: 11/16/2022]
Abstract
Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic electroencephalogram (EEG) data. In this paper, a new two-stage approach is proposed for epileptic spike detection. First, the k-point nonlinear energy operator (k-NEO) is adopted to detect all possible spike candidates, then a newly proposed spike model with slow wave features is applied to these candidates for spike classification. Experimental results show that the proposed system, using the AdaBoost classifier, outperforms the conventional method in both two- and three-class EEG pattern classification problems. The proposed system not only achieves better accuracy for spike detection, but also provides new ability to differentiate between spikes and spikes with slow waves. Though spikes with slow waves occur frequently in epileptic EEGs, they are not used in conventional spike detection. Identifying spikes with slow waves allows the proposed system to have better capability for assisting clinical neurologists in routine EEG examinations and epileptic diagnosis.
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Affiliation(s)
- Yung-Chun Liu
- Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan; E-Mail:
- Medical Device Innovation Center, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan
| | - Chou-Ching K. Lin
- Department of Neurology, National Cheng Kung University Hospital, No. 138, Sheng Li Road, Tainan City 704, Taiwan; E-Mails: (C.-C.K.L.); (J.-J.T.)
| | - Jing-Jane Tsai
- Department of Neurology, National Cheng Kung University Hospital, No. 138, Sheng Li Road, Tainan City 704, Taiwan; E-Mails: (C.-C.K.L.); (J.-J.T.)
| | - Yung-Nien Sun
- Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan; E-Mail:
- Medical Device Innovation Center, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +886-6-275-7575 (ext. 62526); Fax: +886-6-274-7076
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20
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Garg HK, Kohli AK. Nonstationary-epileptic-spike detection algorithm in EEG signal using SNEO. Biomed Eng Lett 2013. [DOI: 10.1007/s13534-013-0090-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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Nonclercq A, Foulon M, Verheulpen D, De Cock C, Buzatu M, Mathys P, Van Bogaert P. Cluster-based spike detection algorithm adapts to interpatient and intrapatient variation in spike morphology. J Neurosci Methods 2012; 210:259-65. [PMID: 22850558 DOI: 10.1016/j.jneumeth.2012.07.015] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Revised: 07/09/2012] [Accepted: 07/23/2012] [Indexed: 10/28/2022]
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22
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Faust O, Acharya UR, Tamura T. Formal Design Methods for Reliable Computer-Aided Diagnosis: A Review. IEEE Rev Biomed Eng 2012; 5:15-28. [DOI: 10.1109/rbme.2012.2184750] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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23
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Wang S, Lin CJ, Wu C, Chaovalitwongse WA. Early Detection of Numerical Typing Errors Using Data Mining Techniques. ACTA ACUST UNITED AC 2011. [DOI: 10.1109/tsmca.2011.2116006] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Ji Z, Sugi T, Goto S, Wang X, Ikeda A, Nagamine T, Shibasaki H, Nakamura M. An Automatic Spike Detection System Based on Elimination of False Positives Using the Large-Area Context in the Scalp EEG. IEEE Trans Biomed Eng 2011; 58:2478-88. [PMID: 21622069 DOI: 10.1109/tbme.2011.2157917] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Zhanfeng Ji
- Department of Advanced Systems Control Engineering, Saga University, 840-8502 Saga, Japan.
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25
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Hsu CC, Yu J. Obstructive Sleep Apnea Diagnosis from Electroencephalography Frequency Variation by Radial Basis Function Neural Network. COMPUTATIONAL COLLECTIVE INTELLIGENCE. TECHNOLOGIES AND APPLICATIONS 2010. [DOI: 10.1007/978-3-642-16732-4_29] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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26
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Halford JJ. Computerized epileptiform transient detection in the scalp electroencephalogram: Obstacles to progress and the example of computerized ECG interpretation. Clin Neurophysiol 2009; 120:1909-1915. [PMID: 19836303 DOI: 10.1016/j.clinph.2009.08.007] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2009] [Revised: 08/05/2009] [Accepted: 08/09/2009] [Indexed: 11/19/2022]
Affiliation(s)
- Jonathan J Halford
- Division of Adult Neurology, Department of Neurosciences, Medical University of South Carolina, Charleston, SC 29425, USA.
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Tzallas AT, Tsipouras MG, Fotiadis DI. Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis. ACTA ACUST UNITED AC 2009; 13:703-10. [PMID: 19304486 DOI: 10.1109/titb.2009.2017939] [Citation(s) in RCA: 251] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Alexandros T Tzallas
- Unit of Medical Technology and Intelligent Information Systems, Department of Material Science and Technology, University of Ioannina, Ioannina 45110, Greece.
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28
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El-Gohary M, McNames J, Elsas S. User-guided interictal spike detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2008:821-4. [PMID: 19162783 DOI: 10.1109/iembs.2008.4649280] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In the diagnosis and treatment of epilepsy, long-term monitoring may be required to document and study interictal activities such as interictal spikes. However, visual inspection of the EEG done by an expert is too time consuming and researchers normally resort to automatic detection methods. We describe a new EEG user-guided interictal spike detection algorithm that only requires the user to annotate a few spikes. We use the annotations to build a template that captures the relevant features of spikes, and then use Mean Squared Error (MSE) test to detect all of the other spikes in the recording. The detected events are rank ordered so that the user can easily identify the true spikes and their time of occurrence. The true spikes are then annotated to the EEG signals and reported to the EEG expert for further evaluation. This design provides a compromise between the enormous time commitments necessary to annotate recordings by hand and the inability of fully-automatic spike detection algorithms to account for the variability between subjects. Because spike morphology and spatial distribution change considerably when patients go through cycles of wake and sleep in long-term monitoring, this detection algorithm uses multichannel multiple templates to detect more than one type of event. The algorithm is able to achieve an average sensitivity of 96% and an average of 4.8 false detections/ hour.
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Affiliation(s)
- Mahmoud El-Gohary
- Department of Electrical and Computer Engineering, Biomedical Signal Processing Laboratory, Portland State University, Portland, Oregon, USA.
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29
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Performance metrics for the accurate characterisation of interictal spike detection algorithms. J Neurosci Methods 2009; 177:479-87. [DOI: 10.1016/j.jneumeth.2008.10.010] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2008] [Revised: 10/06/2008] [Accepted: 10/08/2008] [Indexed: 11/20/2022]
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30
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31
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Oikonomou VP, Tzallas AT, Fotiadis DI. A Kalman filter based methodology for EEG spike enhancement. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2007; 85:101-8. [PMID: 17112632 DOI: 10.1016/j.cmpb.2006.10.003] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2005] [Revised: 10/03/2006] [Accepted: 10/04/2006] [Indexed: 05/12/2023]
Abstract
In this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives.
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Affiliation(s)
- V P Oikonomou
- Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece
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32
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Tzallas AT, Oikonomou VP, Fotiadis DI. Epileptic spike detection using a Kalman filter based approach. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2006; 2006:501-504. [PMID: 17945981 DOI: 10.1109/iembs.2006.260780] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The electroencephalogram (EEG) consists of an underlying background process with superimposed transient nonstationarities such as epileptic spikes (ESs). The detection of ESs in the EEG is of particular importance in the diagnosis of epilepsy. In this paper a new approach for detecting ESs in EEG recordings is presented. It is based on a time-varying autoregressive model (TVAR) that makes use of the nonstationarities of the EEG signal. The autoregressive (AR) parameters are estimated via Kalman filtering (KF). In our method, the EEG signal is first preprocessed to accentuate ESs and attenuate background activity, and then passed through a thresholding function to determine ES locations. The proposed method is evaluated using simulated signals as well as real inter-ictal EEGs.
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