St-Jean Jérôme, Gharbi Oumayma, Toffa Dènahin Hinnoutondji, Tran Thi Phuoc Yen, Robert Manon, Nguyen Dang Khoa, Bou Assi Elie
Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, Quebec, Canada.
Department of Neuroscience, Université de Montréal, Montreal, Quebec, Canada.
Epilepsia Open. 2025 Jul 9. doi: 10.1002/epi4.70099.
In recent years, seizure detection using wearable technology has gained significant attention in research. Most studies, however, have focused on detecting generalized or focal to bilateral tonic-clonic seizures. This study evaluates the feasibility of using a biometric shirt to detect focal impaired awareness seizures (FIAS) by monitoring respiratory, electrocardiography, and accelerometry signals.
Patients with epilepsy were recruited at the University of Montreal Hospital Center epilepsy monitoring unit. Seizures were annotated by epileptologists based on simultaneous video-electroencephalographic recordings, blinded to the shirt data. Features were extracted from the respiratory, accelerometry, and electrocardiography signals using varying window sizes and steps. An XGBoost classifier was trained and tested using a nested leave-one-subject-out cross-validation. Post-processing included a firing power regularization method to reduce false alarms.
We recorded 113 FIAS from 27 patients who wore the shirt continuously for over 4750 hours. Using a firing power threshold of 0.65, we detected 71 seizures, resulting in a mean sensitivity of 66%, a 15% time in warning, and a false alarm rate (FAR) of 30 per 24 hours. A firing power threshold of 0.85 allowed us to reduce false alarms (8% time in warning, FAR of 21 per 24 hours) but resulted in a lower sensitivity of 57%. Performances varied across patients: sensitivity was high and FAR was low for some patients and vice versa for others, indicating variability in algorithm effectiveness across patients.
Our results demonstrate that detecting FIAS with a connected shirt could be feasible for certain patients, although the rate of false alarms remains an issue. Designing a personalized algorithm and selecting patients who exhibit significant physiological changes during seizures could make wearable-based FIAS detection more viable in the near future.
Novel mobile health technologies could transform epilepsy care by enabling continuous monitoring of seizures in everyday life. In this study, we used a biometric shirt to detect seizures with impaired awareness automatically. We used the shirt to measure breathing, heart activity, and movement in 27 patients with epilepsy in a hospital setting. Our algorithm detected up to two-thirds of seizures correctly. However, the number of incorrect alarms remains relatively high, with variable performances between patients. While the technology showed potential, these challenges highlight the need for further improvements and personalized care plans.
近年来，利用可穿戴技术进行癫痫发作检测在研究中受到了广泛关注。然而，大多数研究都集中在检测全身性或双侧强直阵挛性癫痫发作。本研究通过监测呼吸、心电图和加速度计信号，评估使用生物识别衬衫检测局灶性意识障碍性癫痫发作（FIAS）的可行性。
在蒙特利尔大学医院中心癫痫监测单元招募癫痫患者。癫痫发作由癫痫专家根据同步视频脑电图记录进行标注，对衬衫数据不知情。使用不同的窗口大小和步长从呼吸、加速度计和心电图信号中提取特征。使用嵌套留一受试者交叉验证对XGBoost分类器进行训练和测试。后处理包括一种触发功率正则化方法以减少误报。
我们记录了27名患者的113次FIAS，这些患者连续穿着衬衫超过4750小时。使用0.65的触发功率阈值，我们检测到71次癫痫发作，平均灵敏度为66%，预警时间为15%，每24小时误报率（FAR）为30次。0.85的触发功率阈值使我们能够减少误报（预警时间为8%，每24小时FAR为21次），但灵敏度降低至57%。不同患者的表现有所不同：一些患者的灵敏度高且FAR低，而另一些患者则相反，这表明算法在不同患者中的有效性存在差异。
我们的结果表明，对于某些患者，使用连接的衬衫检测FIAS可能是可行的，尽管误报率仍然是一个问题。设计个性化算法并选择在癫痫发作期间表现出明显生理变化的患者，可能会使基于可穿戴设备的FIAS检测在不久的将来更具可行性。
新型移动健康技术可以通过在日常生活中持续监测癫痫发作来改变癫痫护理。在本研究中，我们使用生物识别衬衫自动检测意识障碍性癫痫发作。我们在医院环境中使用衬衫测量了27名癫痫患者的呼吸、心脏活动和运动。我们的算法正确检测到了多达三分之二的癫痫发作。然而，误报数量仍然相对较高，不同患者之间的表现存在差异。虽然该技术显示出了潜力，但这些挑战凸显了进一步改进和个性化护理计划的必要性。
