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首页> 外文期刊>Epilepsia Open >The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non‐epileptic seizures
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The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non‐epileptic seizures

机译：实用的自动门诊设备，用于检测和区分癫痫和精神性非癫痫发作

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Objective Accurate differentiation between epileptic seizures (ES) and psychogenic non‐epileptic seizures (PNES) can be challenging based on history alone. Inpatient video EEG monitoring (VEM) is often needed for a definitive diagnosis. However, VEM is highly resource intensive, is of limited availability, and cannot be undertaken over long periods. Previous research has shown that time‐frequency analysis of accelerometer data could be utilized to differentiate between ES and PNES. Using a seizure detection and classification algorithm, we sought to examine the diagnostic utility of an automated analysis with an ambulatory accelerometer. Methods A wrist‐worn device was used to collect accelerometer data from patients during VEM admission, for diagnostic evaluation of convulsive seizures. An automated process, that involved the use of K‐means clustering and support vector machines, was used to detect and classify each seizure as ES or PNES. The results were compared with VEM diagnoses determined by epileptologists blinded to the accelerometer data. Results Twenty‐four convulsive seizures, consisting of at least 20?seconds of sustained continuous activity, recorded from 11 patients during inpatient VEM (13 PNES from five patients and 11 ES from six patients) were included for analysis. The automated system detected all convulsive seizures (ES, PNES) from 661?hours of recording with 67 false alarms (2.4 per 24?hours). The sensitivity and specificity for classifying ES from PNES were 72.7% and 100%, respectively. The positive and negative predictive values for classifying PNES were 81.3% and 100%, respectively. There was no significant difference between the classification results obtained from the automation process and the VEM diagnoses. Significance This automated system can potentially provide a wearable out‐of‐hospital seizure diagnostic monitoring system.
机译：目的仅仅根据病史，准确区分癫痫发作（ES）和精神病性非癫痫发作（PNES）可能具有挑战性。明确诊断通常需要住院视频EEG监视（VEM）。但是，VEM占用大量资源，可用性有限，并且不能长期使用。先前的研究表明，加速度计数据的时频分析可用于区分ES和PNES。使用癫痫发作检测和分类算法，我们试图检查动态加速度计的自动分析的诊断工具。方法：在VEM入院期间，使用腕戴式设备收集患者的加速度计数据，用于诊断性评估抽搐性癫痫发作。涉及使用K-means聚类和支持向量机的自动化过程用于检测每个癫痫发作并将其分类为ES或PNES。将结果与对加速计数据不了解的癫痫专家确定的VEM诊断进行了比较。结果包括11例住院VEM期间记录的24例惊厥性癫痫发作，包括至少20s持续持续活动（包括5例患者的13 PNES和6例患者的11 ES），以进行分析。自动化系统检测到记录超过661小时的所有惊厥性癫痫发作（ES，PNES），并发出67次虚警（每24小时2.4次）。从PNES分类ES的敏感性和特异性分别为72.7％和100％。对PNES进行分类的阳性和阴性预测值分别为81.3％和100％。从自动化过程和VEM诊断获得的分类结果之间没有显着差异。重要性该自动化系统可以潜在地提供可穿戴的院外癫痫发作诊断监测系统。

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