Fractal features for localization of temporal lobe epileptic foci using SPECT imaging

Lopes, Renaud; Steinling, M.; Szurhaj, William; Maouche, S.; Dubois, Patrick; Betrouni, Nacim

doi:10.1016/j.compbiomed.2010.03.001

Cited by 16 publications

(10 citation statements)

References 27 publications

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“…Of interest, limited correlation was noted between the results of expert review and the neural network classifier, suggesting that the neural network did not recapitulate manual analysis . Multifractal analysis of ictal and interictal single‐photon emission computed tomography (SPECT) data, examined using an SVM classifier, has also been shown to accurately localize TLE in 19 of 20 cases, comparing favorably to conventional image subtraction . A number of studies have also applied machine learning techniques to morphometric analysis of structural MRI using T1‐weighted and fluid‐attenuated inversion recovery (FLAIR) sequences, both for detection and lateralization of TLE.…”

Section: Machine Learning In the Diagnosis Of Epilepsymentioning

confidence: 99%

Machine learning applications in epilepsy

2019

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Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre‐surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.

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Section: Machine Learning In the Diagnosis Of Epilepsymentioning

confidence: 99%

Machine learning applications in epilepsy

2019

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“…This antiepileptogenic side effect of these sedatives, although beneficial in the treatment of seizures, is detrimental in children who are hospitalized to capture a seizure with nuclear imaging. These children, as a consequence of their interictal sedation with traditional sedatives, may not exhibit a subsequent seizure as an inpatient, and it is this seizure that is necessary to identify the hyperperfused area or areas that could indicate epileptic foci (12).…”

Section: Sedation Databasementioning

confidence: 99%

Dexmedetomidine Offers an Option for Safe and Effective Sedation for Nuclear Medicine Imaging in Children

Mason¹,

Robinson²,

Fontaine³

et al. 2013

Radiology

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“…Recent neuroimaging advances have stimulated interest in using pattern extraction based on machine learning to perform brain image classification. Machine learning techniques have demonstrated potential for improving epileptogenic lateralization using various structural and metabolic imaging tests, including MRI (85–95%) and SPECT (95%) . Functional connectivity MRI (fcMRI) has recently gained popularity in TLE connectivity research due to the characterization of TLE as a "network disease" .…”

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confidence: 99%

Computer‐automated focus lateralization of temporal lobe epilepsy using fMRI

Chiang

Levin

Haneef

2014

Magnetic Resonance Imaging

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Purpose To compare the performance of computer-automated diagnosis using fMRI interictal graph theory (CADFIG) to that achieved in standard clinical practice with MRI, for lateralizing the affected hemisphere in temporal lobe epilepsy (TLE). Materials and Methods Interictal resting state fMRI and high-resolution MRI were performed on 14 left and 10 right TLE patients. Functional topology measures were calculated from fMRI using graph theory, and used to lateralize the epileptogenic hemisphere using quadratic discriminant analysis. Leave-one-out cross-validation prediction accuracy of CADFIG was compared to performance based on expert manual analysis (MA) of MRI, using video EEG as the “gold standard” for focus lateralization. Results CADFIG correctly lateralized 95.8% (23/24) of cases, compared to 66.7% (16/24) with expert MA of MRI. Combining MA with CADFIG allowed all cases (24/24) to be correctly lateralized. CADFIG correctly identified the affected hemisphere for all patients (8/8) where MRI failed to lateralize. Conclusion CADFIG based on fMRI lateralized the affected hemisphere in TLE with superior performance compared to expert MA of MRI. These results demonstrate that functional patterns in fMRI can be used with automated machine learning for diagnostic lateralization in TLE. Addition of fMRI-based tests to existing protocols for identifying the affected hemisphere in pre-surgical assessment can improve diagnostic accuracy and surgical outcome in TLE.

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Fractal features for localization of temporal lobe epileptic foci using SPECT imaging

Cited by 16 publications

References 27 publications

Machine learning applications in epilepsy

Machine learning applications in epilepsy

Dexmedetomidine Offers an Option for Safe and Effective Sedation for Nuclear Medicine Imaging in Children

Computer‐automated focus lateralization of temporal lobe epilepsy using fMRI

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