Epileptic seizure prediction based on features extracted from lagged Poincaré plots

Behbahani, Soroor; Dabanloo, Nader Jafarnia; Nasrabadi, Ali Motie; Dourado, António

doi:10.1080/00207454.2022.2106435

Cited by 1 publication

(3 citation statements)

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“…Interestingly, Behbahani and colleagues suggested considering a different lag index j for analyzing the SD series, so that the i-th element of the SD series is given by SD i (j) = NN I i+j − NN I i . This latter approach allows us to investigate the HRV changes in different time scales [72].…”

Section: Hrv Featuresmentioning

confidence: 99%

“…Focusing on the non-linear HRV metrics, a total of N = 22 works considered the Poincaré plot. A single work extended the Poincaré analysis by comparing the points NN I i+j , NN I i for multiple lag indexes j [72]. A significant number of works (N = 10) estimated the complexity of the NNI series in terms of entropy.…”

Section: Hrv Featuresmentioning

confidence: 99%

“…By using the modified Cardiac Sympathetic Index (mCSI) to estimate the HRV, the designed system obtained a sensitivity of 100% in patients presenting significant tachycardia during the ictal phase. Alternative detection frameworks were proposed in [67,72,97], and the maximum performance (sensitivity of 100%) was obtained by Qaraqe and colleagues, who combined EEG and HRV features in a unique system.…”

Section: Adult Populationmentioning

confidence: 99%

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Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review

Mason,

Scarabello,

Taruffi

et al. 2024

JCM

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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.

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Section: Hrv Featuresmentioning

confidence: 99%

Section: Hrv Featuresmentioning

confidence: 99%

Section: Adult Populationmentioning

confidence: 99%

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