大约三分之一的癫痫患者会出现耐药性癫痫发作;早期发现癫痫发作有助于提高安全性,减少病人的焦虑,增加独立性,并实现急性治疗。近年来,在不同疾病中使用人工智能技术和机器学习算法,包括癫痫,显著增加。本研究的主要目的是确定MJNNeuroserveis开发的MJN-SERAS人工智能算法是否,可以使用患者特定的数据早期检测癫痫发作,以创建基于脑电图训练的个性化数学模型,定义为在即将到来的癫痫发作最初开始之前对它们的程序化识别,通常在几分钟内,诊断为癫痫的患者。回顾性,横截面,观察,多中心研究,以确定人工智能算法的敏感性和特异性。我们搜索了三个西班牙医疗中心的癫痫单元数据库,并选择了在2017年1月至2021年2月之间评估的50名患者,这些患者被诊断为难治性局灶性癫痫,并在3至5天之间进行了视频脑电图监测记录。每个病人至少发作3次,持续时间超过5s,每次发作之间的间隔大于1h。排除标准包括年龄<18岁,颅内脑电图监测,和严重的精神病,神经学,或系统性疾病。该算法使用我们的学习算法从脑电图数据中识别出发作前和发作间模式,并与高级癫痫学家的评估作为金标准进行比较。使用该特征数据集训练每个患者的个体数学模型。总共回顾了1963h的49个视频脑电图记录,每位患者平均39.26小时。视频EEG监测记录了309次癫痫发作,随后由癫痫学家分析。mjn-SERAS算法对119次癫痫发作进行了训练,对188次癫痫发作进行了拆分测试。统计分析包括来自每个模型的数据,并报告10个假阴性(未检测到视频EEG记录的发作)和22个假阳性(在30分钟内没有临床相关性或异常EEG信号的警报检测)。具体来说,自动MJN-SERASAI算法的灵敏度为94.7%(95%;CI94.67-94.73),与平均值(调和平均值或平均值)和91%的阳性预测值所代表的参考性能相比,F评分的特异性为92.2%(95%;CI92.17-92.23),在独立于患者的模型中,假阳性率为每24小时0.55。这种用于早期癫痫发作检测的特定于患者的AI算法在灵敏度和假阳性率方面显示出有希望的结果。尽管该算法需要在专门的服务器云上进行训练和计算的计算要求很高,它的实时计算量低,允许其在嵌入式设备上实现在线癫痫发作检测。
Around one-third of epilepsy patients develop drug-resistant seizures; early detection of seizures could help improve safety, reduce patient anxiety, increase independence, and enable acute treatment. In recent years, the use of artificial intelligence techniques and machine learning algorithms in different diseases, including epilepsy, has increased significantly. The main objective of this
study is to determine whether the mjn-SERAS artificial intelligence algorithm developed by MJN Neuroserveis, can detect seizures early using patient-specific data to create a personalized mathematical model based on EEG training, defined as the programmed recognition of oncoming seizures before they are primarily initiated, usually within a period of a few minutes, in patients diagnosed of epilepsy. Retrospective, cross-sectional, observational, multicenter
study to determine the sensitivity and specificity of the artificial intelligence algorithm. We searched the database of the Epilepsy Units of three Spanish medical centers and selected 50 patients evaluated between January 2017 and February 2021, diagnosed with refractory focal epilepsy who underwent video-EEG monitoring recordings between 3 and 5 days, a minimum of 3 seizures per patient, lasting more than 5 s and the interval between each seizure was greater than 1 h. Exclusion criteria included age <18 years, intracranial EEG monitoring, and severe psychiatric, neurological, or systemic disorders. The algorithm identified pre-ictal and interictal patterns from EEG data using our learning algorithm and was compared to a senior epileptologist\'s evaluation as a gold standard. Individual mathematical models of each patient were trained using this feature dataset. A total of 1963 h of 49 video-EEG recordings were reviewed, with an average of 39.26 h per patient. The video-EEG monitoring recorded 309 seizures as subsequently analyzed by the epileptologists. The mjn-SERAS algorithm was trained on 119 seizures and split testing was performed on 188 seizures. The statistical analysis includes the data from each model and reports 10 false negatives (no detection of episodes recorded by video-EEG) and 22 false positives (alert detected without clinical correlation or abnormal EEG signal within 30 min). Specifically, the automated mjn-SERAS AI algorithm achieved a sensitivity of 94.7% (95 %; CI 94.67-94.73), and an F-Score representing specificity of 92.2% (95 %; CI 92.17-92.23) compared to the reference performance represented by a mean (harmonic mean or average) and a positive predictive value of 91%, with a false positive rate of 0.55 per 24 h in the patient-independent model. This patient-specific AI algorithm for early seizure detection shows promising results in terms of sensitivity and false positive rate. Although the algorithm requires high computational requirements on specialized servers cloud for training and computing, its computational load in real-time is low, allowing its implementation on embedded devices for online seizure detection.