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Kim T, Seo DY, Bae JH, Han J. Barefoot walking improves cognitive ability in adolescents. THE KOREAN JOURNAL OF PHYSIOLOGY & PHARMACOLOGY : OFFICIAL JOURNAL OF THE KOREAN PHYSIOLOGICAL SOCIETY AND THE KOREAN SOCIETY OF PHARMACOLOGY 2024; 28:295-302. [PMID: 38926837 PMCID: PMC11211751 DOI: 10.4196/kjpp.2024.28.4.295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/25/2024] [Accepted: 04/02/2024] [Indexed: 06/28/2024]
Abstract
Walking can have a positive impact on cognitive function in adolescents. This study aimed to compare the effects of walking with sneakers and barefoot on cognitive ability in adolescents. Fifty-nine adolescent male students were included in the study and assigned to the control (n = 20), sneaker (n = 19), and barefoot (n = 20) groups. The barefoot and sneakers group performed a 40-min walking exercise four times a week for 12 weeks during the morning physical activity time, while the control group performed self-study. Electroencephalogram (EEG) and brain activity variables were measured before and after the exercise program. The results showed that after 12 weeks, the barefoot group had a significant decrease in Gamma and H-beta waves and a significant increase in sensorimotor rhythm (SMR) and Alpha waves. Conversely, the control group showed a significant decrease in SMR waves and increase in Theta waves. The sneaker group showed a significant decrease in SMR waves alone. In an eyes-open resting state, the barefoot group showed a significant increase in H-beta, M-beta, SMR, and Alpha waves. The barefoot group also had a significant increase in cognitive speed and concentration and a significant decrease in brain stress. Taken together, barefoot walking can effectively enhance cognitive ability in adolescents, as demonstrated by the significant variation in EEG activity. This research highlights the potential benefits of barefoot walking as a simple and effective form of exercise for enhancing cognitive function in adolescents.
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Affiliation(s)
- Taehun Kim
- Department of Physical Education, College of Education, Kyungpook National University, Daegu 41566, Korea
| | - Dae Yun Seo
- Basic Research Laboratory, Department of Physiology, College of Medicine, Smart Marine Therapeutic Center, Cardiovascular and Metabolic Disease Center, Inje University, Busan 47392, Korea
| | - Jun Hyun Bae
- Able-Art Sport, Department Theory, Hyupsung University, Hwaseong 18330, Korea
| | - Jin Han
- Basic Research Laboratory, Department of Physiology, College of Medicine, Smart Marine Therapeutic Center, Cardiovascular and Metabolic Disease Center, Inje University, Busan 47392, Korea
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2
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Antonoudiou P, Basu T, Maguire J. Semi-automated seizure detection using interpretable machine learning models. RESEARCH SQUARE 2024:rs.3.rs-4361048. [PMID: 38854086 PMCID: PMC11160878 DOI: 10.21203/rs.3.rs-4361048/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Despite the vast number of seizure detection publications there are no validated open-source tools for automating seizure detection based on electrographic recordings. Researchers instead rely on manual curation of seizure detection that is highly laborious, inefficient, error prone, and heavily biased. Here we developed an open-source software called SeizyML that uses sensitive machine learning models coupled with manual validation of detected events reducing bias and promoting efficient and accurate detection of electrographic seizures. We compared the validity of four interpretable machine learning models (decision tree, gaussian naïve bayes, passive aggressive classifier, and stochastic gradient descent classifier) on an extensive electrographic seizure dataset that we collected from chronically epileptic mice. We find that the gaussian naïve bayes and stochastic gradient descent models achieved the highest precision and f1 scores, while also detecting all seizures in our mouse dataset and only require a small amount of data to train the model and achieve good performance. Further, we demonstrate the utility of this approach to detect electrographic seizures in a human EEG dataset. This approach has the potential to be a transformative research tool overcoming the analysis bottleneck that slows research progress.
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Harvey BJ, Olah VJ, Aiani LM, Rosenberg LI, Pedersen NP. Classifier for the Rapid Simultaneous Determination of Sleep-Wake States and Seizures in Mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.07.536063. [PMID: 37066377 PMCID: PMC10104108 DOI: 10.1101/2023.04.07.536063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Independent automated scoring of sleep-wake and seizures have recently been achieved; however, the combined scoring of both states has yet to be reported. Mouse models of epilepsy typically demonstrate an abnormal electroencephalographic (EEG) background with significant variability between mice, making combined scoring a more difficult classification problem for manual and automated scoring. Given the extensive EEG variability between epileptic mice, large group sizes are needed for most studies. As large datasets are unwieldy and impractical to score manually, automatic seizure and sleep-wake classification are warranted. To this end, we developed an accurate automated classifier of sleep-wake states, seizures, and the post-ictal state. Our benchmark was a classification accuracy at or above the 93% level of human inter-rater agreement. Given the failure of parametric scoring in the setting of altered baseline EEGs, we adopted a machine-learning approach. We created several multi-layer neural network architectures that were trained on human-scored training data from an extensive repository of continuous recordings of electrocorticogram (ECoG), left and right hippocampal local field potential (HPC-L and HPC-R), and electromyogram (EMG) in the murine intra-amygdala kainic acid model of medial temporal lobe epilepsy. We then compared different network models, finding a bidirectional long short-term memory (BiLSTM) design to show the best performance with validation and test portions of the dataset. The SWISC (sleep-wake and the ictal state classifier) achieved >93% scoring accuracy in all categories for epileptic and non-epileptic mice. Classification performance was principally dependent on hippocampal signals and performed well without EMG. Additionally, performance is within desirable limits for recording montages featuring only ECoG channels, expanding its potential scope. This accurate classifier will allow for rapid combined sleep-wake and seizure scoring in mouse models of epilepsy and other neurologic diseases with varying EEG abnormalities, thereby facilitating rigorous experiments with larger numbers of mice.
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Affiliation(s)
- Brandon J. Harvey
- Graduate Program in Neuroscience, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
- Department of Neurology, University of California, Davis, 1515 Newton Court, Davis, CA 95618, USA
| | - Viktor J. Olah
- Department of Cell Biology, Emory University, 615 Michael St., Atlanta, GA 30322, USA
| | - Lauren M. Aiani
- Department of Genetics, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Lucie I. Rosenberg
- Department of Neurology, University of California, Davis, 1515 Newton Court, Davis, CA 95618, USA
| | - Nigel P. Pedersen
- Department of Neurology, University of California, Davis, 1515 Newton Court, Davis, CA 95618, USA
- Department of Genetics, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
- Centers for Neuro-Engineering and Medicine, Center for Neuroscience, University of California, Davis, CA 95618, USA
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Ein Shoka AA, Dessouky MM, El-Sayed A, Hemdan EED. EEG seizure detection: concepts, techniques, challenges, and future trends. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-31. [PMID: 37362745 PMCID: PMC10071471 DOI: 10.1007/s11042-023-15052-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/07/2022] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of awareness. Consequently, epilepsy patients face problems in daily life due to precautions they must take to adapt to this condition, particularly when they use heavy equipment, e.g., vehicle derivation. Epilepsy studies rely primarily on electroencephalography (EEG) signals to evaluate brain activity during seizures. It is troublesome and time-consuming to manually decide the location of seizures in EEG signals. The automatic detection framework is one of the principal tools to help doctors and patients take appropriate precautions. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that are performed on EEG data, a generally extracted feature from the signal, and a detailed view on classification procedures used in this problem and provide insights on the difficulties and future research directions in this innovative theme. Therefore, this paper presents a review of work on recent methods for the epileptic seizure process along with providing perspectives and concepts to researchers to present an automated EEG-based epileptic seizure detection system using IoT and machine learning classifiers for remote patient monitoring in the context of smart healthcare systems. Finally, challenges and open research points in EEG seizure detection are investigated.
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Affiliation(s)
- Athar A. Ein Shoka
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
| | - Mohamed M. Dessouky
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
- Department of Computer Science & Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Ayman El-Sayed
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
| | - Ezz El-Din Hemdan
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
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5
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Dash DP, Kolekar MH, Chakraborty C, Khosravi MR. Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3552512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Epilepsy is one of the significant neurological disorders affecting nearly 65 million people worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were proposed for efficient seizure detection using intracranial and surface EEG signals. In the last decade, various machine learning techniques based on seizure detection approaches were proposed. This paper discusses different machine learning and deep learning techniques for seizure detection using intracranial and surface EEG signals. A wide range of machine learning techniques such as support vector machine (SVM) classifiers, artificial neural network (ANN) classifier, and deep learning techniques such as a convolutional neural network (CNN) classifier, long-short term memory (LSTM) network for seizure detection are compared in this paper. The effectiveness of time-domain features, frequency domain features, and time-frequency domain features are discussed along with different machine learning techniques. Along with EEG, other physiological signals such as electrocardiogram are used to enhance seizure detection accuracy which are discussed in this paper. In recent years deep learning techniques based on seizure detection have found good classification accuracy. In this paper, an LSTM deep learning-network-based approach is implemented for seizure detection and compared with state-of-the-art methods. The LSTM based approach achieved 96.5% accuracy in seizure-nonseizure EEG signal classification. Apart from analyzing the physiological signals, sentiment analysis also has potential to detect seizure.
Impact Statement-
This review paper gives a summary of different research work related to epileptic seizure detection using machine learning and deep learning techniques. Manual seizure detetion is time consuming and requires expertise. So the artificial intelligence techniques such as machine learning and deep learning techniques are used for automatic seizure detection. Different physiological signals are used for seizure detection. Different researchers are working on developing automatic seizure detection using EEG, ECG, accelerometer, sentiment analysis. There is a need for a review paper that can discuss previous techniques and give further research direction. We have discussed different techniques for seizure detection with an accuracy comparison table. It can help the researcher to get an overview of both surface and intracranial EEG-based seizure detection approaches. The new researcher can easily compare different models and decide the model they want to start working on. A deep learning model is discussed to give a practical application of seizure detection. Sentiment analysis is another dimension of seizure detection and summerizing it will give a new prospective to the reader.
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Sand D, Arkadir D, Abu Snineh M, Marmor O, Israel Z, Bergman H, Hassin-Baer S, Israeli-Korn S, Peremen Z, Geva AB, Eitan R. Deep Brain Stimulation Can Differentiate Subregions of the Human Subthalamic Nucleus Area by EEG Biomarkers. Front Syst Neurosci 2021; 15:747681. [PMID: 34744647 PMCID: PMC8565520 DOI: 10.3389/fnsys.2021.747681] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/16/2021] [Indexed: 01/10/2023] Open
Abstract
Introduction: Precise lead localization is crucial for an optimal clinical outcome of subthalamic nucleus (STN) deep brain stimulation (DBS) treatment in patients with Parkinson's disease (PD). Currently, anatomical measures, as well as invasive intraoperative electrophysiological recordings, are used to locate DBS electrodes. The objective of this study was to find an alternative electrophysiology tool for STN DBS lead localization. Methods: Sixty-one postoperative electrophysiology recording sessions were obtained from 17 DBS-treated patients with PD. An intraoperative physiological method automatically detected STN borders and subregions. Postoperative EEG cortical activity was measured, while STN low frequency stimulation (LFS) was applied to different areas inside and outside the STN. Machine learning models were used to differentiate stimulation locations, based on EEG analysis of engineered features. Results: A machine learning algorithm identified the top 25 evoked response potentials (ERPs), engineered features that can differentiate inside and outside STN stimulation locations as well as within STN stimulation locations. Evoked responses in the medial and ipsilateral fronto-central areas were found to be most significant for predicting the location of STN stimulation. Two-class linear support vector machine (SVM) predicted the inside (dorso-lateral region, DLR, and ventro-medial region, VMR) vs. outside [zona incerta, ZI, STN stimulation classification with an accuracy of 0.98 and 0.82 for ZI vs. VMR and ZI vs. DLR, respectively, and an accuracy of 0.77 for the within STN (DLR vs. VMR)]. Multiclass linear SVM predicted all areas with an accuracy of 0.82 for the outside and within STN stimulation locations (ZI vs. DLR vs. VMR). Conclusions: Electroencephalogram biomarkers can use low-frequency STN stimulation to localize STN DBS electrodes to ZI, DLR, and VMR STN subregions. These models can be used for both intraoperative electrode localization and postoperative stimulation programming sessions, and have a potential to improve STN DBS clinical outcomes.
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Affiliation(s)
- Daniel Sand
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel.,Edmond and Lily Safra Center for Brain Research, Hebrew University of Jerusalem, Jerusalem, Israel.,Elminda Ltd., Herzliya, Israel
| | - David Arkadir
- Department of Neurology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Muneer Abu Snineh
- Department of Neurology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Odeya Marmor
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Zvi Israel
- Brain Division, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Functional Neurosurgery Unit, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Hagai Bergman
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel.,Edmond and Lily Safra Center for Brain Research, Hebrew University of Jerusalem, Jerusalem, Israel.,Functional Neurosurgery Unit, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Sharon Hassin-Baer
- Department of Neurology, Movement Disorders Institute, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Simon Israeli-Korn
- Department of Neurology, Movement Disorders Institute, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | | | - Amir B Geva
- Department of Electrical and Computer Engineering, Ben Gurion University, Beer-Sheva, Israel
| | - Renana Eitan
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel.,Brain Division, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Neuropsychiatry Unit, Jerusalem Mental Health Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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7
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Wei L, Boutouil H, R Gerbatin R, Mamad O, Heiland M, Reschke CR, Del Gallo F, F Fabene P, Henshall DC, Lowery M, Morris G, Mooney C. Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy. J Neural Eng 2021; 18. [PMID: 34607322 DOI: 10.1088/1741-2552/ac2ca0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 10/04/2021] [Indexed: 11/12/2022]
Abstract
Objective.Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals.Approach.In this study, a machine learning-based seizure detection approach, 'Epi-AI', which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach.Main results.Epi-AI demonstrated a superior sensitivity, 91.4%-98.8%, and specificity, 93.1%-98.8%, in Models I-III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I-III ranged from 66.9%-91.3% sensitivity and 60.8%-97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I-III at 73.2%-80.1% and 75.8%-98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity.Significance.Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.
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Affiliation(s)
- Lan Wei
- School of Computer Science, University College Dublin, Dublin, Ireland.,FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Halima Boutouil
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Rogério R Gerbatin
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Omar Mamad
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Mona Heiland
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Cristina R Reschke
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Federico Del Gallo
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.,School of Pharmacy, University of Camerino, Macerata, Italy
| | - Paolo F Fabene
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - David C Henshall
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Madeleine Lowery
- School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Gareth Morris
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,These authors contributed equally
| | - Catherine Mooney
- School of Computer Science, University College Dublin, Dublin, Ireland.,FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,These authors contributed equally
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8
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Saminu S, Xu G, Shuai Z, Abd El Kader I, Jabire AH, Ahmed YK, Karaye IA, Ahmad IS. A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal. Brain Sci 2021; 11:668. [PMID: 34065473 PMCID: PMC8160878 DOI: 10.3390/brainsci11050668] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 05/14/2021] [Accepted: 05/16/2021] [Indexed: 02/07/2023] Open
Abstract
The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.
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Affiliation(s)
- Sani Saminu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
- Biomedical Engineering Department, University of Ilorin, P.M.B 1515, Ilorin 240003, Nigeria;
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Zhang Shuai
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Isselmou Abd El Kader
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Adamu Halilu Jabire
- Department of Electrical and Electronics Engineering, Taraba State University, Jalingo 660242, Nigeria;
| | - Yusuf Kola Ahmed
- Biomedical Engineering Department, University of Ilorin, P.M.B 1515, Ilorin 240003, Nigeria;
| | - Ibrahim Abdullahi Karaye
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Isah Salim Ahmad
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
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Marshall GF, Gonzalez-Sulser A, Abbott CM. Modelling epilepsy in the mouse: challenges and solutions. Dis Model Mech 2021; 14:dmm.047449. [PMID: 33619078 PMCID: PMC7938804 DOI: 10.1242/dmm.047449] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
In most mouse models of disease, the outward manifestation of a disorder can be measured easily, can be assessed with a trivial test such as hind limb clasping, or can even be observed simply by comparing the gross morphological characteristics of mutant and wild-type littermates. But what if we are trying to model a disorder with a phenotype that appears only sporadically and briefly, like epileptic seizures? The purpose of this Review is to highlight the challenges of modelling epilepsy, in which the most obvious manifestation of the disorder, seizures, occurs only intermittently, possibly very rarely and often at times when the mice are not under direct observation. Over time, researchers have developed a number of ways in which to overcome these challenges, each with their own advantages and disadvantages. In this Review, we describe the genetics of epilepsy and the ways in which genetically altered mouse models have been used. We also discuss the use of induced models in which seizures are brought about by artificial stimulation to the brain of wild-type animals, and conclude with the ways these different approaches could be used to develop a wider range of anti-seizure medications that could benefit larger patient populations. Summary: This Review discusses the challenges of modelling epilepsy in mice, a condition in which the outward manifestation of the disorder appears only sporadically, and reviews possible solutions encompassing both genetic and induced models.
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Affiliation(s)
- Grant F Marshall
- Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK.,Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh EH8 9XD, UK
| | - Alfredo Gonzalez-Sulser
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh EH8 9XD, UK.,Centre for Discovery Brain Sciences, 1 George Square, University of Edinburgh, Edinburgh EH8 9XD, UK
| | - Catherine M Abbott
- Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK .,Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh EH8 9XD, UK
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10
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Gong S, Xing K, Cichocki A, Li J. Deep Learning in EEG: Advance of the Last Ten-Year Critical Period. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3079712] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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11
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Cho KO, Lee SH, Jang HJ. Feasibility of fully automated classification of whole slide images based on deep learning. THE KOREAN JOURNAL OF PHYSIOLOGY & PHARMACOLOGY : OFFICIAL JOURNAL OF THE KOREAN PHYSIOLOGICAL SOCIETY AND THE KOREAN SOCIETY OF PHARMACOLOGY 2020; 24:89-99. [PMID: 31908578 PMCID: PMC6940498 DOI: 10.4196/kjpp.2020.24.1.89] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 11/25/2019] [Accepted: 11/27/2019] [Indexed: 12/11/2022]
Abstract
Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.
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Affiliation(s)
- Kyung-Ok Cho
- Department of Pharmacology, Seoul St. Mary's Hospital, Seoul 06591, Korea.,Department of Biomedicine & Health Sciences, Seoul St. Mary's Hospital, Seoul 06591, Korea.,Catholic Neuroscience Institute, Seoul St. Mary's Hospital, Seoul 06591, Korea
| | - Sung Hak Lee
- Department of Hospital Pathology, Seoul St. Mary's Hospital, Seoul 06591, Korea
| | - Hyun-Jong Jang
- Department of Biomedicine & Health Sciences, Seoul St. Mary's Hospital, Seoul 06591, Korea.,Catholic Neuroscience Institute, Seoul St. Mary's Hospital, Seoul 06591, Korea.,Department of Physiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
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12
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GRP-DNet: A gray recurrence plot-based densely connected convolutional network for classification of epileptiform EEG. J Neurosci Methods 2020; 347:108953. [PMID: 33007344 DOI: 10.1016/j.jneumeth.2020.108953] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 09/16/2020] [Accepted: 09/16/2020] [Indexed: 12/27/2022]
Abstract
BACKGROUND The classification of epileptiform electroencephalogram (EEG) signals has been treated as an important but challenging issue for realizing epileptic seizure detection. In this work, combing gray recurrence plot (GRP) and densely connected convolutional network (DenseNet), we developed a novel classification system named GRP-DNet to identify seizures and epilepsy from single-channel, long-term EEG signals. NEW METHODS The proposed GRP-DNet classification system includes three main modules: 1) input module takes an input long-term EEG signal and divides it into multiple short segments using a fixed-size non-overlapping sliding window (FNSW); 2) conversion module transforms short segments into GRPs and passes them to the DenseNet; 3) fusion and decision, the predicted label of each GRP is fused using a majority voting strategy to make the final decision. RESULTS Six different classification experiments were designed based on a publicly available benchmark database to evaluate the effectiveness of our system. Experimental results showed that the proposed GRP-DNet achieved an excellent classification accuracy of 100 % in each classification experiment, Furthermore, GRP-DNet gave excellent computational efficiency, which indicates its tremendous potential for developing an EEG-based online epilepsy diagnosis system. COMPARISON WITH EXISTING METHODS Our GRP-DNet system was superior to the existing competitive classification systems using the same database. CONCLUSIONS The GRP-DNet is a potentially powerful system for identifying and classifying EEG signals recorded from different brain states.
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13
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An S, Kang C, Lee HW. Artificial Intelligence and Computational Approaches for Epilepsy. J Epilepsy Res 2020; 10:8-17. [PMID: 32983950 PMCID: PMC7494883 DOI: 10.14581/jer.20003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 06/18/2020] [Accepted: 07/14/2020] [Indexed: 12/30/2022] Open
Abstract
Studies on treatment of epilepsy have been actively conducted in multiple avenues, but there are limitations in improving its efficacy due to between-subject variability in which treatment outcomes vary from patient to patient. Accordingly, there is a growing interest in precision medicine that provides accurate diagnosis for seizure types and optimal treatment for an individual epilepsy patient. Among these approaches, computational studies making this feasible are rapidly progressing in particular and have been widely applied in epilepsy. These computational studies are being conducted in two main streams: 1) artificial intelligence-based studies implementing computational machines with specific functions, such as automatic diagnosis and prognosis prediction for an individual patient, using machine learning techniques based on large amounts of data obtained from multiple patients and 2) patient-specific modeling-based studies implementing biophysical in-silico platforms to understand pathological mechanisms and derive the optimal treatment for each patient by reproducing the brain network dynamics of the particular patient per se based on individual patient's data. These computational approaches are important as it can integrate multiple types of data acquired from patients and analysis results into a single platform. If these kinds of methods are efficiently operated, it would suggest a novel paradigm for precision medicine.
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Affiliation(s)
- Sora An
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea
| | - Chaewon Kang
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Computational Medicine, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea
| | - Hyang Woon Lee
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Computational Medicine, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea
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14
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Rim B, Sung NJ, Min S, Hong M. Deep Learning in Physiological Signal Data: A Survey. SENSORS (BASEL, SWITZERLAND) 2020; 20:E969. [PMID: 32054042 PMCID: PMC7071412 DOI: 10.3390/s20040969] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 01/31/2020] [Accepted: 02/09/2020] [Indexed: 12/11/2022]
Abstract
Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.
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Affiliation(s)
- Beanbonyka Rim
- Department of Computer Science, Soonchunhyang University, Asan 31538, Korea
| | - Nak-Jun Sung
- Department of Computer Science, Soonchunhyang University, Asan 31538, Korea
| | - Sedong Min
- Department of Medical IT Engineering, Soonchunhyang University, Asan 31538, Korea
| | - Min Hong
- Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Korea
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15
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Comparison of different input modalities and network structures for deep learning-based seizure detection. Sci Rep 2020; 10:122. [PMID: 31924842 PMCID: PMC6954227 DOI: 10.1038/s41598-019-56958-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 12/16/2019] [Indexed: 02/07/2023] Open
Abstract
The manual review of an electroencephalogram (EEG) for seizure detection is a laborious and error-prone process. Thus, automated seizure detection based on machine learning has been studied for decades. Recently, deep learning has been adopted in order to avoid manual feature extraction and selection. In the present study, we systematically compared the performance of different combinations of input modalities and network structures on a fixed window size and dataset to ascertain an optimal combination of input modalities and network structures. The raw time-series EEG, periodogram of the EEG, 2D images of short-time Fourier transform results, and 2D images of raw EEG waveforms were obtained from 5-s segments of intracranial EEGs recorded from a mouse model of epilepsy. A fully connected neural network (FCNN), recurrent neural network (RNN), and convolutional neural network (CNN) were implemented to classify the various inputs. The classification results for the test dataset showed that CNN performed better than FCNN and RNN, with the area under the curve (AUC) for the receiver operating characteristics curves ranging from 0.983 to 0.984, from 0.985 to 0.989, and from 0.989 to 0.993 for FCNN, RNN, and CNN, respectively. As for input modalities, 2D images of raw EEG waveforms yielded the best result with an AUC of 0.993. Thus, CNN can be the most suitable network structure for automated seizure detection when applied to the images of raw EEG waveforms, since CNN can effectively learn a general spatially-invariant representation of seizure patterns in 2D representations of raw EEG.
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16
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Jang HJ, Cho KO. Applications of deep learning for the analysis of medical data. Arch Pharm Res 2019; 42:492-504. [PMID: 31140082 DOI: 10.1007/s12272-019-01162-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 05/20/2019] [Indexed: 02/06/2023]
Abstract
Over the past decade, deep learning has demonstrated superior performances in solving many problems in various fields of medicine compared with other machine learning methods. To understand how deep learning has surpassed traditional machine learning techniques, in this review, we briefly explore the basic learning algorithms underlying deep learning. In addition, the procedures for building deep learning-based classifiers for seizure electroencephalograms and gastric tissue slides are described as examples to demonstrate the simplicity and effectiveness of deep learning applications. Finally, we review the clinical applications of deep learning in radiology, pathology, and drug discovery, where deep learning has been actively adopted. Considering the great advantages of deep learning techniques, deep learning will be increasingly and widely utilized in a wide variety of different areas in medicine in the coming decades.
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Affiliation(s)
- Hyun-Jong Jang
- Department of Physiology, Department of Biomedicine & Health Sciences, Catholic Neuroscience Institute, College of Medicine, The Catholic University of Korea, Seoul, 06591, South Korea
| | - Kyung-Ok Cho
- Department of Pharmacology, Department of Biomedicine & Health Sciences, Catholic Neuroscience Institute, Institute of Aging and Metabolic Diseases, College of Medicine, The Catholic University of Korea, 222 Banpo-Daero, Seocho-Gu, Seoul, 06591, South Korea.
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