The detection of Evoked Potential with variable latency and multiple trial using Consensus matching pursuit

Hadriche, Abir; Jmail, Nawel; Kachouri, Abdennaceur; Ghariani, Hamadi

doi:10.1109/atsip.2014.6834628

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…These results (effect of frequency range and SNR on the capacity of separation between transient and ripples activities) were in agreement with a previous work for other type of electrophysiological signals [ 8 , 11 , 24 , 38 ].…”

Section: Resultssupporting

confidence: 93%

“…For MEG signal, we used topography maps to assess capacity of recognition and automatic detection of transient using our advanced methods. Our results were in concordance with previous works [ 8 , 11 , 24 , 38 ]. In the second time, we proposed to estimate robustness of four distributed inverse problem methods: MNE, eLORETA, dSPM and cMEM in characterization and recognition of unhealthy cortical tissue: regions of excessive discharges.…”

Section: Discussionsupporting

confidence: 94%

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Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events

Hadriche

Behy

Amal

et al. 2021

Computational and Mathematical Methods in Medicine

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Characterizing epileptogenic zones EZ (sources responsible of excessive discharges) would assist a neurologist during epilepsy diagnosis. Locating efficiently these abnormal sources among magnetoencephalography (MEG) biomarker is obtained by several inverse problem techniques. These techniques present different assumptions and particular epileptic network connectivity. Here, we proposed to evaluate performances of distributed inverse problem in defining EZ. First, we applied an advanced technique based on Singular Value Decomposition (SVD) to recover only pure transitory activities (interictal epileptiform discharges). We evaluated our technique’s robustness in separation between transitory and ripples versus frequency range, transitory shapes, and signal to noise ratio on simulated data (depicting both epileptic biomarkers and respecting time series and spectral properties of realistic data). We validated our technique on MEG signal using detector precision on 5 patients. Then, we applied four methods of inverse problem to define cortical areas and neural generators of excessive discharges. We computed network connectivity of each technique. Then, we confronted obtained noninvasive networks to intracerebral EEG transitory network connectivity using nodes in common, connection strength, distance metrics between concordant nodes of MEG and IEEG, and average propagation delay. Coherent Maximum Entropy on the Mean (cMEM) proved a high matching between MEG network connectivity and IEEG based on distance between active sources, followed by Exact low-resolution brain electromagnetic tomography (eLORETA), Dynamical Statistical Parametric Mapping (dSPM), and Minimum norm estimation (MNE). Clinical performance was interesting for entire methods providing in an average of 73.5% of active sources detected in depth and seen in MEG, and vice versa, about 77.15% of active sources were detected from MEG and seen in IEEG. Investigated problem techniques succeed at least in finding one part of seizure onset zone. dSPM and eLORETA depict the highest connection strength among all techniques. Propagation delay varies in this range [18, 25]ms, knowing that eLORETA ensures the lowest propagation delay (18 ms) and the closet one to IEEG propagation delay.

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Section: Resultssupporting

confidence: 93%

Section: Discussionsupporting

confidence: 94%

Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events

Hadriche

Behy

Amal

et al. 2021

Computational and Mathematical Methods in Medicine

Self Cite

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show abstract

“…There is Software capable to inspect both invasive and non invasive registration, other for anatomical images. For epileptic biomarker, spiky events present the highest analyzed biomarker in epilepsy for decades [7], but recently studies are oriented to oscillatory events from low gamma to very high gamma HFO [5]. Studying epileptic oscillatory events depends straightly on type of used registration, since HFO are much more seen in SEEG than in non invasive technique.…”

Section: Discussionmentioning

confidence: 99%

A Build Up of Seizure Prediction and Detection Software: A Review Article

Hadriche¹,

Jmail²

2021

austinneurosurgopenaccess

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Introduction: Neurological diseases are much often due to our stressed daily life, and epilepsy is considered as a second cause of hospitalization in neurological illness. It is about 30% of epileptic cases where medicine would not stop or control seizure; hence, a surgical intervention is required to delineate abnormal hyperexcitable cortical tissue. Defining these epileptogenic zones is a challenge that require physiological and anatomical acquisition. Discussion: Clinicians, researcher and engineer researcher are multiplying advanced techniques in order to exploit these acquisitions for a better diagnosis. Several software are used to enhance epilepsy diagnosis. Here we proposed a software that rely on space-time evolution of inter- ictal gamma oscillations. Conclusion: Our proposed software would predict a build up of seizure during monitoring of stereo-electroencephalographic SEEG recording. It allows also detection of seizure during analysis and diagnosis of SEEG. This software would assist neurologist in recognition of seizure and in defining epileptogenic zone EZ.

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“…There is Software capable to inspect both invasive and non invasive registration, other for anatomical images. For epileptic biomarker, spiky events present the highest analyzed biomarker in epilepsy for decades [7,8], but recently studies are oriented to oscillatory events from low gamma to very high gamma HFO [5]. Studying epileptic oscillatory events depends straightly on type of used registration, since HFO are much more seen in SEEG than in non invasive technique.…”

Section: Discussionmentioning

confidence: 99%

A build up of seizure prediction and detection Software: A review

Hadriche¹,

Jmail²

2021

J Clin Images Med Case Rep

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Introduction: Neurological diseases are much often due to our stressed daily life, and epilepsy is considered as a second cause of hospitalization in neurological illness. It is about 30% of epileptic cases where medicine would not stop or control seizure; hence, a surgical intervention is required to delineate abnormal hyperexcitable cortical tissue. Defining these epileptogenic zones is a challenge that require physiological and anatomical acquisition. Discussion: Clinicians, researcher and engineer researcher are multiplying advanced techniques in order to exploit these acquisitions for a better diagnosis. Several software are used to enhance epilepsy diagnosis. Here we proposed a software that rely on spacetime evolution of inter- ictal gamma oscillations. Conclusion: Our proposed software would predict a build up of seizure during monitoring of stereo-electroencephalographic SEEG recording. It allows also detection of seizure during analysis and diagnosis of SEEG. This software would assist neurologist in recognition of seizure and in defining epileptogenic zone EZ. Keywords: Pharmaco-ressistant epilepsy; SEEG; Seizure build up; Prediction; Detection.

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The detection of Evoked Potential with variable latency and multiple trial using Consensus matching pursuit

Cited by 6 publications

References 12 publications

Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events

Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events

A Build Up of Seizure Prediction and Detection Software: A Review Article

A build up of seizure prediction and detection Software: A review

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