Mason Federico, Scarabello Anna, Taruffi Lisa, Pasini Elena, Calandra-Buonaura Giovanna, Vignatelli Luca, Bisulli Francesca
Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy.
IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy.
J Clin Med. 2024 Jan 27;13(3):747. doi: 10.3390/jcm13030747.
The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events.
癫痫患者（PwE）面临的最关键负担是癫痫发作，其不可预测性严重影响生活质量。能够检测甚至预测发作事件的实时预警系统的设计将改善癫痫管理，为癫痫患者及其护理人员带来巨大益处。过去，各种研究表明，自主心脏控制的显著变化预示着癫痫发作的开始，这可以通过心率变异性（HRV）进行评估。本文对基于HRV检测或预测发作事件的方法进行了文献综述。在PubMed数据库上的初步搜索返回了402篇论文，其中72篇符合纳入标准并被纳入综述。这些结果表明，由于发作期过渡期间自主神经变化更为显著，新生儿和儿科患者的癫痫发作检测更为准确。此外，传统指标往往无法捕捉心脏自主神经变化，应采用更先进的方法，考虑非线性HRV特征和用于处理它们的机器学习工具。最后，研究用于心脏监测的可穿戴系统的研究表明，对于发作期自主心脏控制出现显著变化的患者，HRV是一种有效的癫痫发作检测生物标志物。
