Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis

An, Nan; Ye, Xiaolai; Liu, Qiangqiang; Xu, Junjie; Zhang, Pu-Ming

doi:10.1016/j.eplepsyres.2020.106475

Cited by 5 publications

(4 citation statements)

References 54 publications

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“…Moreover, An et al . [ 45 ] convert the 128–256 channels into a single channel time-frequency because it may reflect the brain region's epileptogenicity. Our study used 4–10 channels based on the channel activity shown by the higher energy level.…”

Section: Discussionmentioning

confidence: 99%

Quantitative analysis of inter- and intrahemispheric coherence on epileptic electroencephalography signal

2022

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Section: Discussionmentioning

confidence: 99%

Quantitative analysis of inter- and intrahemispheric coherence on epileptic electroencephalography signal

2022

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“…In the surgical treatment of drug-resistant epilepsy, the physical location and number of implanted electrodes are restricted due to the complexity of brain structure (Guye et al, 2006;An et al, 2020;Frauscher, 2020). On the contrary, TI electrical stimulation could realize tunable stimulation at deep brain targets without the movement of electrodes, which might support the treatment of epilepsy.…”

Section: Clinical Applicationsmentioning

confidence: 99%

A novel non-invasive brain stimulation technique: “Temporally interfering electrical stimulation”

Guo

He²,

Zhang³

et al. 2023

Front. Neurosci.

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For decades, neuromodulation technology has demonstrated tremendous potential in the treatment of neuropsychiatric disorders. However, challenges such as being less intrusive, more concentrated, using less energy, and better public acceptance, must be considered. Several novel and optimized methods are thus urgently desiderated to overcome these barriers. In specific, temporally interfering (TI) electrical stimulation was pioneered in 2017, which used a low-frequency envelope waveform, generated by the superposition of two high-frequency sinusoidal currents of slightly different frequency, to stimulate specific targets inside the brain. TI electrical stimulation holds the advantages of both spatial targeting and non-invasive character. The ability to activate deep pathogenic targets without surgery is intriguing, and it is expected to be employed to treat some neurological or psychiatric disorders. Recently, efforts have been undertaken to investigate the stimulation qualities and translation application of TI electrical stimulation via computational modeling and animal experiments. This review detailed the most recent scientific developments in the field of TI electrical stimulation, with the goal of serving as a reference for future research.

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“…In the first step of intelligent SOZ localization, epileptic markers’ detection plays a crucial role [ 14 , 16 , 17 ]. Research in recent years has suggested multiple promising biomarkers for SOZ localization, such as spikes [ 17 ], high-frequency oscillations (HFOs) (ripples (Rs), fast ripples (FRs), ripples co-occurring with FRs(R&FRs)) [ 8 , 18 , 19 , 20 ], and digital features of interictal epileptiform discharges [ 21 , 22 , 23 ]. Among these, for spikes detection, we demonstrated that deep learning can detect subtle changes in SEEG [ 24 ], and a more adaptive and highly interpretable SEEG-Net was then designed [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Localization of Seizure Onset Zone Based on Multi-Epileptogenic Biomarkers Analysis of Single-Contact from Interictal SEEG

Wang

Yang

et al. 2022

Bioengineering

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Successful surgery on drug-resistant epilepsy patients (DRE) needs precise localization of the seizure onset zone (SOZ). Previous studies analyzing this issue still face limitations, such as inadequate analysis of features, low sensitivity and limited generality. Our study proposed an innovative and effective SOZ localization method based on multiple epileptogenic biomarkers (spike and HFOs), and analysis of single-contact (MEBM-SC) to address the above problems. We extracted contacts epileptic features from signal distributions and signal energy based on machine learning and end-to-end deep learning. Among them, a normalized pathological ripple rate was designed to reduce the disturbance of physiological ripple and enhance the performance of SOZ localization. Then, a feature selection algorithm based on Shapley value and hypothetical testing (ShapHT+) was used to limit interference from irrelevant features. Moreover, an attention mechanism and a focal loss algorithm were used on the classifier to learn significant features and overcome the unbalance of SOZ/nSOZ contacts. Finally, we provided an SOZ prediction and visualization on magnetic resonance imaging (MRI). Ten patients with DRE were selected to verify our method. The experiment performed cross-validation and revealed that MEBM-SC obtains higher sensitivity. Additionally, the spike has better sensitivity while HFOs have better specificity, and the combination of these biomarkers can achieve the best performance. The study confirmed that MEBM-SC can increase the sensitivity and accuracy of SOZ localization and help clinicians to perform a precise and reliable preoperative evaluation based on interictal SEEG.

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Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis

Cited by 5 publications

References 54 publications

Quantitative analysis of inter- and intrahemispheric coherence on epileptic electroencephalography signal

Quantitative analysis of inter- and intrahemispheric coherence on epileptic electroencephalography signal

A novel non-invasive brain stimulation technique: “Temporally interfering electrical stimulation”

Automatic Localization of Seizure Onset Zone Based on Multi-Epileptogenic Biomarkers Analysis of Single-Contact from Interictal SEEG

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