Automated Epilepsy Diagnosis Using EEG With Test Set Evaluation

Panwar, Siddharth; Joshi, Sachindra; Gupta, Anubha; Agarwal, Puneet

doi:10.1109/tnsre.2019.2914603

Cited by 23 publications

(35 citation statements)

References 32 publications

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“…This is composed of a set of restingstate scalp EEG data from 5 healthy volunteers, and a set of intracranial EEG data from 5 people with drug-resistant epilepsy acquired during pre-surgical evaluations. Such patient sampling, intermixing of scalp and intracranial data, and sample size are not appropriate for development of a diagnostic tool; results are not applicable, nor generalizable to different datasets [80]. Authors with a background in computational sciences should make an effort to communicate with the medical field to understand the context and reality of clinical practice and avoid overpromising language which leaves studies vulnerable to being misinterpreted.…”

Section: Discussionmentioning

confidence: 99%

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Faiman

Smith

Hodsoll

et al. 2021

Epilepsy & Behavior

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Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.

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Section: Discussionmentioning

confidence: 99%

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Faiman

Smith

Hodsoll

et al. 2021

Epilepsy & Behavior

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“…Epilepsy patients with intellectual impairment have higher emotional, behavioral and cognitive problems, with difficulty in socializing and having achievements, particularly in schoolchildren. The direction of a positive relation indicates that if SPM score increases, the SHE score also increases (smaller handicap) 12 .…”

Section: Discussionmentioning

confidence: 99%

Association between Intelligence Level and Handicap Degree in Epilepsy Patients

Beslar¹,

Rahardjo²,

Suharjanti³

et al. 2020

IJFMT

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Background:. Epilepsy correlates with high limitation value, disability and function loss which can give negative impact to patient's quality of life. This negative impact correlates with either epilectic seizures (motoric, sensory and behavior) or effects of anti-epileptic drugs that can cause cognitive impairment, which in this case is intelligence aspect with handicap in epilepsy patients. We determined correlation between intelligence level and handicap degree in epilepsy patients. Methods:The research was conducted in patients of Neurological Department of Dr. Soetomo General Hospital, Surabaya, Indonesia, who met inclusion and exclusion criteria, consecutively from July 2013-December 2013. Intelligence test was conducted using Standard Progressive Matrices (SPM) test, while test on handicap degree was conducted using Subjective Handicap Epilepsy (SHE) test.Results: There were 40 research subjects (19 females and 21 males) with average age of 30.9±9.6. The average score of SHE was 70.08±16/69, while the average score of SPM was 88.53±10.34. There was a weak positive correlation and statistically significant correlation between SPM and SHE scores (r = 0.345 and p = 0.029). Conclusion:There was a correlation between intelligence level and handicap degree in epilepsy patients.

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“…These methods have achieved over 90% diagnostic accuracy in their own data sets, and most of the classification results can reach an AUC area above 0.9. Siddharth Panwar et al 20 verified the two models, 11,21 which are highly representative in the 10‐year research in the field of epilepsy diagnosis. They were evaluated on a new test dataset.…”

Section: Introductionmentioning

confidence: 94%

Multilayer weighted integrated self‐learning algorithm for automatic diagnosis of epileptic electroencephalogram signals

Zhao

Shi

et al. 2020

Computational Intelligence

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Epilepsy is a common mental disorder that affects about 70 million people worldwide. Epileptic electroencephalogram (EEG) signal, an important means to judge epileptic seizure, needs neurologists' prior knowledge to mark manually. This marking method is time‐consuming and laborious. Currently, the existing automated diagnosis methods have achieved good results on one benchmark EEG dataset, most of which can achieve accuracy of more than 0.95. However, the method has limitations on the dataset, and the accuracy of the diagnosis results on another new dataset drops sharply to nearly 0.5. Aiming at the existing EEG signal diagnosis lacks stability and generalization ability, this paper proposed a multilayer‐weighted integrated self‐learning algorithm for different classifiers. For this algorithm, weighted voting was first conducted on the the diagnostic results by different classifiers to obtain a result, which was weighted again to produce the final diagnostic results. This algorithm improves the problem that the traditional self‐learning algorithm is greatly affected by data noise, which shows a strong stability in different data sets and in clinical epileptic EEG signal data detection, so as to reduce the workload of neurologists and provide support and assistance for the diagnosis and treatment of epilepsy. The experiment result shows that the algorithm can improve the stability and reliability of EEG automatic diagnosis of epilepsy. The accuracy and AUC area of its classification in two different public data sets and clinical data can reach 0.80 to 0.95.

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Automated Epilepsy Diagnosis Using EEG With Test Set Evaluation

Cited by 23 publications

References 32 publications

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Association between Intelligence Level and Handicap Degree in Epilepsy Patients

Multilayer weighted integrated self‐learning algorithm for automatic diagnosis of epileptic electroencephalogram signals

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