Computer Assisted Analysis System of Electroencephalogram for Diagnosing Epilepsy

Ahmad, Malik Anas; Khan, Nadeem Ahmad; Majeed, Waqas

doi:10.1109/icpr.2014.583

Cited by 11 publications

(7 citation statements)

References 23 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is systemic survey indicates that conventional ML algorithms (ANN, SVM, KNN) contribute well to the processing of brain datasets (CHB-MIT, BONN, Kaggle, Fribourg, and Bern Barcelona) for seizure detection [106][107][108][109][110][111][112][113][114][115][116][117][118][119][120]. However, each method has some pros and cons.…”

Section: Discussionmentioning

confidence: 99%

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

Ahmad

Wang

Zhu

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG’s noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG’s low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.

show abstract

Section: Discussionmentioning

confidence: 99%

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

Ahmad

Wang

Zhu

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Using a cost-sensitive SVM that can handle the imbalanced class distribution of interictal and preictal samples, the EEG collected from 18 patients in the Freiburg EEG database could be classified with an average sensitivity of 97.5% and a false alarm rate of 0.27/hr. A similar approach (i.e., frequency domain-based EEG features + SVM classifier) has been tested on other datasets [ 19 , 20 , 21 ], with performance ranging between 90 and 92% prediction sensitivity. Williamson et al [ 22 ] proposed to utilize multivariate EEG features instead of the popular univariate features such as the power spectral density to capture patterns involving multiple EEG channels.…”

Section: Related Workmentioning

confidence: 99%

Multi-Channel Vision Transformer for Epileptic Seizure Prediction

2022

View full text Add to dashboard Cite

Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness. Around 30% of epileptic patients continue to have seizures despite taking anti-seizure medication. The ability to predict the future occurrence of seizures would enable the patients to take precautions against probable injuries and administer timely treatment to abort or control impending seizures. In this study, we introduce a Transformer-based approach called Multi-channel Vision Transformer (MViT) for automated and simultaneous learning of the spatio-temporal-spectral features in multi-channel EEG data. Continuous wavelet transform, a simple yet efficient pre-processing approach, is first used for turning the time-series EEG signals into image-like time-frequency representations named Scalograms. Each scalogram is split into a sequence of fixed-size non-overlapping patches, which are then fed as inputs to the MViT for EEG classification. Extensive experiments on three benchmark EEG datasets demonstrate the superiority of the proposed MViT algorithm over the state-of-the-art seizure prediction methods, achieving an average prediction sensitivity of 99.80% for surface EEG and 90.28–91.15% for invasive EEG data.

show abstract

“…Papers were excluded if they described topics of neuroscience, neurobiology, or neurological conditions-including cell structure, cortex, and (f)MRI research (n = 22 [ 3 , 54 , 59 , 72 , 92 , 94 , 95 , 96 , 98 , 103 , 108 , 115 , 117 , 131 , 146 , 180 , 183 , 185 , 195 , 199 , 214 , 220 ]), and in one case epilepsy [ 5 ]. We also excluded neurodevelopmental disorders such as autism or ADHD [ 60 , 175 ] that present primarily as behavioral conditions.…”

Section: Record Selectionmentioning

confidence: 99%

“…Thus, to define and extract mental health specific behaviors (e.g., from sensor and interaction data) a number of approaches were applied. Most commonly (n = 8), the researchers used (i) questionnaires or standardized clinical scales 5 to screen for specific mental health symptoms and their severity within a study population [ 44 , 67 , 140 , 153 , 168 , 201 , 217 , 218 ]. For assessments of symptoms of depression, this commonly included the CES-D [ 153 ], BDI [ 44 ], and PHQ [ 140 , 201 , 217 , 218 ].…”

Section: Data Collection and Related Conceptualizations Of Mental Heamentioning

confidence: 99%

“…Here, most papers (n = 6) focused on affiliation behaviors, whereby membership to an online mental health community [ 57 , 64 , 89 , 141 , 133 , 165 ], or engagement with mental health content (e.g . , using hashtags of #anxiety, #depression 5 Examples of assessment scales used include, for depression : PHQ-8 and PHQ-9 [ 97 ], Epidemiological Study Depression Scale [ 9 ], and MADRS depression rating system [ 126 ]; for mania the Mania Rating Scale (MRS) [ 213 ]; for mood HAMD mood scores (Hamilton rating scale for depression) [ 50 ]; for affect : PANAS for positive and negative affect [ 202 ], Photographic Affect meter (PAM) [ 148 ]; for stress : Coping Stress Questionnaire (CSQ) [ 159 ]; Trauma-Focused Coping Self-Efficacy (CSE-T) [ 16 ]; Perceived Stress Scale (PSS) [ 38 ]; PTSD severity checklist (PCL) [ 203 ]; for mental wellbeing : eight-item flourishing scale [ 46 ] and SF-36 Mental Health Score ( w w w.optum.com/sf36 ); for levels of social inclusion and connectedness: 20-item UCLA loneliness scale [ 163 ]; and for physical activity : Yale Physical Activity Survey (YPAS) [ 47 ]. 6 EMAs are often short questions designed to capture in-situ real time information about a person's experience [ 128 ].…”

Section: Data Collection and Related Conceptualizations Of Mental Heamentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in Mental Health

Thieme

Belgrave

Doherty

2020

ACM Trans. Comput.-Hum. Interact.

255

View full text Add to dashboard Cite

High prevalence of mental illness and the need for effective mental health care, combined with recent advances in AI, has led to an increase in explorations of how the field of machine learning (ML) can assist in the detection, diagnosis and treatment of mental health problems. ML techniques can potentially offer new routes for learning patterns of human behavior; identifying mental health symptoms and risk factors; developing predictions about disease progression; and personalizing and optimizing therapies. Despite the potential opportunities for using ML within mental health, this is an emerging research area, and the development of effective ML-enabled applications that are implementable in practice is bound up with an array of complex, interwoven challenges. Aiming to guide future research and identify new directions for advancing development in this important domain, this article presents an introduction to, and a systematic review of, current ML work regarding psycho-socially based mental health conditions from the computing and HCI literature. A quantitative synthesis and qualitative narrative review of 54 papers that were included in the analysis surfaced common trends, gaps, and challenges in this space. Discussing our findings, we (i) reflect on the current state-of-the-art of ML work for mental health, (ii) provide concrete suggestions for a stronger integration of human-centered and multidisciplinary approaches in research and development, and (iii) invite more consideration of the potentially far-reaching personal, social, and ethical implications that ML models and interventions can have, if they are to find widespread, successful adoption in real-world mental health contexts.

show abstract

Computer Assisted Analysis System of Electroencephalogram for Diagnosing Epilepsy

Cited by 11 publications

References 23 publications

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

Multi-Channel Vision Transformer for Epileptic Seizure Prediction

Machine Learning in Mental Health

Contact Info

Product

Resources

About