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 (AUC(cc)) 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.

A Generalized Linear Model for an ECG-based Neonatal Seizure Detector / Frassineti, Lorenzo; Manfredi, Claudia; Olmi, Benedetta; Lanata, Antonio. - ELETTRONICO. - 2021:(2021), pp. 471-474. (Intervento presentato al convegno 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021) [10.1109/EMBC46164.2021.9630841].

A Generalized Linear Model for an ECG-based Neonatal Seizure Detector

Frassineti, Lorenzo;Manfredi, Claudia;Olmi, Benedetta;Lanata, Antonio
2021

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 (AUC(cc)) 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.
2021
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Pages 471 - 4742021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Frassineti, Lorenzo; Manfredi, Claudia; Olmi, Benedetta; Lanata, Antonio
File in questo prodotto:
File Dimensione Formato  
A_Generalized_Linear_Model_for_an_ECG-based_Neonatal_Seizure_Detector.pdf

accesso aperto

Tipologia: Pdf editoriale (Version of record)
Licenza: Open Access
Dimensione 834.47 kB
Formato Adobe PDF
834.47 kB Adobe PDF

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1285862
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 6
social impact