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A Generalized Linear Model for an ECG-based Neonatal Seizure Detector
Insights
This study developed an Electrocardiography-based Neonatal Seizure Detector (NSD) using Heart Rate Variability. The ECG-based NSD shows promise for faster seizure detection in newborns, especially when Electroencephalography is unavailable.
Area of Science:
- Neonatal Neurology
- Biomedical Engineering
- Cardiology
Background:
- Neonatal seizures are a critical neurological emergency with complex recognition.
- Current diagnosis relies on Electroencephalography (EEG), requiring specialized expertise.
- There is a need for faster, less invasive seizure detection methods.
Purpose of the Study:
- To develop and validate an Electrocardiography (ECG)-based Neonatal Seizure Detector (NSD).
- To utilize Heart Rate Variability (HRV) features for seizure detection.
- To assess the feasibility of ECG as a simpler diagnostic tool.
Main Methods:
- Development of a Generalized Linear Model using HRV features.
- Validation on a public dataset of 52 neonatal subjects (33 with seizures, 19 seizure-free).
- Evaluation of the detector's performance in identifying seizure events.
Main Results:
- The ECG-based NSD achieved a 69% Concatenated Area Under the ROC Curve (AUCcc).
- HRV features effectively identified cardio-regulatory alterations during neonatal seizures.
- The method showed particular efficacy for seizures related to Hypoxic-Ischaemic Encephalopathies.
Conclusions:
- ECG-based NSDs are a viable tool for supporting timely neonatal seizure diagnosis.
- This approach offers a practical alternative when EEG is not readily accessible.
- The findings support the clinical integration of ECG-based seizure detection in neonates.
Abstract:
Seizures represent one of the most challenging issues of the neonatal period's neurological emergency. Due to the heterogeneity of etiologies and clinical characteristics, seizures recognition is tricky and time-consuming. Currently, the gold standard for seizure diagnosis is Electroencephalography (EEG), whose correct interpretation requires a highly specialized team. Thus, to speed up and facilitate the detection of ictal events, several EEG-based Neonatal Seizure Detectors (NSDs) have been proposed in the literature. Research is currently exploiting more simple and less invasive approaches, such as Electrocardiography (ECG). This work aims at developing an ECG-based NSD using a Generalized Linear Model with features extracted from Heart Rate Variability (HRV) measures as input. The method is validated on a public dataset of 52 subjects (33 with seizures and 19 seizure-free). Achieved encouraging results show 69% Concatenated Area Under the ROC Curve (AUCcc) for the automatic detection of windows with seizure events, confirming that HRV features can be useful to catch the cardio-regulatory system alterations due to neonatal seizure events, particularly those related to Hypoxic-Ischaemic Encephalopathies. Thus, results suggest the use of ECG-based NSDs in clinical practice, especially when a timely diagnosis is needed and EEG technologies are not readily available.Clinical Relevance- An ECG-based Neonatal Seizure Detector could be a valid support to speed up the diagnosis of neonatal seizures, especially when EEG technologies for infants' neurological assessment are not readily available.
