Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Neurology, Aarhus University Hospital, Aarhus, Denmark.
Seizure. 2023 Apr;107:155-161. doi: 10.1016/j.seizure.2023.04.012. Epub 2023 Apr 13.
Wearable automated detection devices of focal epileptic seizures are needed to alert patients and caregivers and to optimize the medical treatment. Heart rate variability (HRV)-based seizure detection devices have presented good detection sensitivity. However, false alarm rates (FAR) are too high.
In this phase-2 study we pursued to decrease the FAR, by using patient-adaptive logistic regression machine learning (LRML) to improve the performance of a previously published HRV-based seizure detection algorithm. ECG-data were prospectively collected using a dedicated wearable electrocardiogram-device during long-term video-EEG monitoring. Sixty-two patients had 174 seizures during 4,614 h recording. The dataset was divided into training-, cross-validation-, and test-sets (chronological) in order to avoid overfitting. Patients with >50 beats/min change in heart rate during first recorded seizure were selected as responders. We compared 18 LRML-settings to find the optimal algorithm.
The patient-adaptive LRML-classifier in combination with using only responders to train the initial decision boundary was superior to both the generic approach and including non-responders to train the LRML-classifier. Using the optimal setting of the LRML in responders in the test dataset yielded a sensitivity of 78.2% and FAR of 0.62/24 h. The FAR was reduced by 31% compared to the previous method, upholding similar sensitivity.
The novel, patient-adaptive LRML seizure detection algorithm outperformed both the generic approach and the previously published patient-tailored method. The proposed method can be implemented in a wearable online HRV-based seizure detection system alerting patients and caregivers of seizures and improve seizure-count which may help optimizing the patient treatment.
需要可穿戴式自动检测局灶性癫痫发作的设备来提醒患者和护理人员，并优化治疗方案。基于心率变异性（HRV）的癫痫发作检测设备已表现出良好的检测灵敏度。但是，假警报率（FAR）过高。
在这项 2 期研究中，我们通过使用患者自适应逻辑回归机器学习（LRML）来改进先前发表的基于 HRV 的癫痫发作检测算法的性能，以降低 FAR。使用专用可穿戴心电图设备在长程视频脑电图监测期间前瞻性地采集心电图数据。62 名患者在 4614 小时的记录中经历了 174 次癫痫发作。为了避免过拟合，数据集按训练集、交叉验证集和测试集（按时间顺序）进行划分。选择心率在首次记录的癫痫发作中变化超过 50 次/分钟的患者作为反应者。我们比较了 18 个 LRML 设置以找到最佳算法。
患者自适应 LRML 分类器与仅使用反应者来训练初始决策边界相结合，优于通用方法和包括非反应者来训练 LRML 分类器。在测试数据集中，使用反应者的最佳 LRML 设置可实现 78.2%的灵敏度和 0.62/24 小时的 FAR。与之前的方法相比，FAR 降低了 31%，同时保持了相似的灵敏度。
新型患者自适应 LRML 癫痫发作检测算法优于通用方法和之前发表的患者定制方法。该方法可应用于可穿戴式在线 HRV 基于癫痫发作检测系统，提醒患者和护理人员癫痫发作，并提高癫痫发作计数，这可能有助于优化患者治疗。
