Dear Editor,Transcranial Magnetic Stimulation(TMS)has emerged as a promising therapeutic tool for various neurological and psychiatric conditions[1-3].However,despite its potential benefits,TMS is not without its discomfort issues[4,5],which are mainly related to target location,stimulus intensity,and treatment duration.The discomfort associated with TMS arises from several factors,including the physical sensations experienced during the procedure and potential adverse effects on the scalp and surrounding tissues.
To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems.The Detrended Fluctuation Analysis(DFA)exponent is chosen as the classification exponent,and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed.The DFA exponent exhibited a statistically significant variation among the pre-ictal,ictal period,and post-ictal stages.The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models,whereas the Naive Bayesian model necessitates the least amount of computational and storage space.The set of DFA exponents is employed as an intermediary variable in the machine learning process.The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety,specifically within the domain of neural modulation in epilepsy.
Zhikai YuBinghao YangPenghu WeiHang XuYongzhi ShanXiaotong FanHuaqiang ZhangChangming Wang a dJingjing WangShan YuGuoguang Zhao
Background: The use of assisted reproductive technique (ART) is becoming more common in infertility. During ART most patients undergo ovarian stimulation. In this study we study the correlation between ovarian reserve markers: Anti-Mullerian hormone (AMH) and antral follicle count (AFC), and the response to ovarian stimulation at in vitro fertilization (IVF) centres in Douala Cameroon. Methods: This was a hospital based cross-sectional sectional analytic study carried out over a period of 3 years, 4 months at Clinique de l’Aéroport, Clinique Odyssée and Clinique Urogyn. Inclusion criteria were: Female partners of infertile couples undergoing ovarian stimulation for an in vitro fertilization cycle, patients who had both ovaries and had done either AMH, AFC or both before ovarian stimulation. Patients were divided into three groups based on the number of oocytes retrieved: low ovarian response for ≤3 oocytes, normal ovarian response for 4 - 15 oocytes and high ovarian response for >15 oocytes. Data obtained was analyzed by SPSS version 25.0. Results: The ages of participants ranged from 20 - 4 7 years, with a mean age of 34.11 ± 5.11 years. Most of them had secondary infertility (57.9%). The GnRH antagonist protocol was mainly used, and ovulation was triggered using HCG predominantly. On Multivariate analysis, age and history of PCOS were significantly associated with ovarian response in the low and high ovarian response groups, respectively. Conclusion: AMH has a better predictive value than AFC, however, it is less sensitive but more specific than AFC.
Bilkissou MoustaphaTatah Humphry NengNancy Elage MungeYannick R. OnanaYaneu Junie NgahaDiane KamdemJean Marie AlimaAlphonse NgalameGeorges MangalaAstrid NdoloGervais MounchikpouMichelle MendouaAimée Timnou DjokamTchounzou RobertMichel Roger EkonoHenri EssomeCharlotte Nguefack Tchente
