Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review.
Federico Mason, Anna Scarabello, Lisa Taruffi, Elena Pasini, Giovanna Calandra-Buonaura, Luca Vignatelli, Francesca Bisulli
Author Information
Federico Mason: Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy. ORCID
Anna Scarabello: Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy.
Lisa Taruffi: Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy.
Elena Pasini: IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy. ORCID
Giovanna Calandra-Buonaura: Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy.
Luca Vignatelli: IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy. ORCID
Francesca Bisulli: Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy. ORCID
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.
