Deep learning approach to detect seizure using reconstructed phase space images

Ilakiyaselvan, N.; Khan, A. Nayeemulla; Shahina, A.

doi:10.7555/jbr.34.20190043

Cited by 52 publications

(10 citation statements)

References 39 publications

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“…The reconstructed phase space technique was used to create the phase space of a dynamical system represented by the EEG signal [99]. Thus, the feature vector representing the state change over time in phase space captures the system's dynamics.…”

Section: Other Feature Extraction Methodsmentioning

confidence: 99%

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Prasanna

Subathra

Mohammed

et al. 2021

JPM

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Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.

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Section: Other Feature Extraction Methodsmentioning

confidence: 99%

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Prasanna

Subathra

Mohammed

et al. 2021

JPM

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“…Time series was the most common target data type among the included studies, dominated by studies of sleep staging and seizure detection based on EEG data [19,32,[40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] and ECG analyses [23,24,[56][57][58][59][60][61][62][63][64][65][66][67][68][69][70]] (e.g. arrhythmia classification).…”

Section: Time Seriesmentioning

confidence: 99%

Transfer learning for non-image data in clinical research: A scoping review

et al. 2022

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Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.

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“…As a remedy, they suggested dataset labeling, deep learning algorithms investigation alongside the appropriate feature selection. In [ 87 ], authors proposed a deep learning (DL) approach to seizer detection by investigating the reconstructed phase space (RPS) instead of direct EEG signals that exhibits a chaotic and non-linear behavior make them inadequate for analysis. The approach exhibits accuracies of 98.5% and 95% for binary and tertiary classification, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

Ahmed,

Alotaibi,

Atta-ur-Rahman

et al. 2022

SN COMPUT. SCI.

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Epilepsy is the second most common neurological disease after Alzheimer. It is a disorder of the brain which results in recurrent seizures. Though the epilepsy in general is considered as a serious disorder, its effects in children are rather dangerous. It is mainly because it reasons a slower rate of development and a failure to improve certain skills among such children. Seizures are the most common symptom of epilepsy. As a regular medical procedure, the specialists record brain activity using an electroencephalogram (EEG) to observe epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate and depend on the specialist’s experience; therefore, automated detection of epileptic pediatric seizures might be an optimal solution. In this regard, several techniques have been investigated in the literature. This research aims to review the approaches to pediatric epilepsy seizures’ identification especially those based on machine learning, in addition to the techniques applied on the CHB-MIT scalp EEG database of epileptic pediatric signals.

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Deep learning approach to detect seizure using reconstructed phase space images

Cited by 52 publications

References 39 publications

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Transfer learning for non-image data in clinical research: A scoping review

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

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