1. Abstract 1.1. 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. 1.2. 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. 1.3. Conclusion: Our proposed software would predict a buildup 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
2. Introduction In 20% of cases, epilepsy is misdiagnosed for several reasons: non-specialized health personnel, and non-efficient analysis of physiological and anatomical registration. Moreover, this neurological disease requires a diagnostic wandering that dragged on average 5 years. About 70% of epileptic subjects could be seizure free if they had an appropriate treatment. Hence, the trend actually is oriented to develop more decision-support tools and improve diagnosis for epilepsy. 30% of epileptic cases are pharmaco ressistant, requiring invasive recording and surgical intervention to delineate epileptogenic zones responsible of excessive discharges and build up seizure. For a long time epileptic spikes are considered as the best biomarker that should be preprocessing to explore physiological registration and describing EZ [1]. Furthermore, scientists proved that oscillations from gamma to ripple to high frequency oscillation HFO could also be surveyed as an important biomarker to detect hyperexcitability tissue [2]. In this review we present a software to assist neurological clinician during pharmaco resistant epilepsy diagnosis; this softAbir ware rely on detecting interictal gamma oscillations in first step, then defining their spatio temporal map to predict a buildup of seizure during monitoring of SEEG signal [3,4]. This software could also detect seizure through analysis of interictal gamma oscillations. Detecting timing and spatial extent are highly important indices for clinician during epilepsy diagnosis.
3. Discussion Software to assist clinician during epilepsy diagnosis are very widespread, with basic principles that varies between registrations, and analyzed biomarkers [5,6]. 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. MEG did also depict more gamma oscillation in some cases than EEG. Hence, it seems promising to combine the study of different modalities of registration in order to detect efficient biomarker. In fact, studying and analyzing these biomarkers is coherent with the designation of region with excessive discharges and buildup of a seizure [3]. In this review, we present a software capable of detecting pure interictal gamma oscillations non-contaminated by epileptic spikes using “Despiking” of SEEG signal, in order to predict a buildup of seizure and detect a seizure among SEEG recordings [3,10]. This software is also capable to assist clinician in defining EZ since it depicts the space extent of cortical region with excessive discharges. Despiking is an advanced technique that relies on creating a spiky basis than projecting investigated signal on spiky model [3], resulting signal is pure interictal gamma oscillations [9]. In a second step, a spatio temporal analysis is applied to elucidate time of incoming seizure and responsible EZ.
4. Conclusion Pharmaco resistant epilepsy, is a traumatic disease, unforeseeable seizure with a lot of suffering, it is in this context that, diagnostic tools should be improved and boosted in order to recognize and predict occurrence of a buildup of a seizure. This proposed software will have positive impacts on the quality of clinicians’ diagnosis [14] as well as on well-being of epileptic patients. Our proposed software, in this review, ensures a prediction and detection of seizures occurrence through SEEG recordings in order to improve the diagnosis efficiency and physical as well as psychological state of pharmaco-resistant patient.
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Nawel Jmail. A Build Up of Seizure Prediction and Detection Software: A Review. Annals of Clinical and Medical Case Reports 2021