Titre :

Early automated classification of neonatal hypoxic-ischemic encephalopathy - An aid to the decision to use therapeutic hypothermia.

Auteur(s) :

Lacan, Laure [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Betrouni, Nacim [Auteur]
Lille Neurosciences & Cognition (LilNCog) - U 1172
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Chaton, Laurence [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Lamblin, Marie-Dominique [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Flamein, Florence [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Boukhris, Mohamed Riadh [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Derambure, Philippe [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Nguyen The Tich, Sylvie [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]

Titre de la revue :

Clinical Neurophysiology

Nom court de la revue :

Clin Neurophysiol

Numéro :

166

Pagination :

108-116

Date de publication :

2024-10

ISSN :

1872-8952

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

Objective The study aimed to address the challenge of early assessment of neonatal hypoxic-ischemic encephalopathy (HIE) severity to identify candidates for therapeutic hypothermia (TH). The objective was to develop an ...
Lire la suite >Objective The study aimed to address the challenge of early assessment of neonatal hypoxic-ischemic encephalopathy (HIE) severity to identify candidates for therapeutic hypothermia (TH). The objective was to develop an automated classification model for neonatal EEGs, enabling accurate HIE severity assessment 24/7. Methods EEGs recorded within 6 h of life after perinatal anoxia were visually graded into 3 severity groups (HIE French Classification) and quantified using 6 qEEG markers measuring amplitude, continuity and frequency content. Machine learning models were developed on a dataset of 90 EEGs and validated on an independent dataset of 60 EEGs. Results The selected model achieved an overall accuracy of 80.6% in the development phase and 80% in the validation phase. Notably, the model accurately identified 28 out of 30 children for whom TH was indicated after visual EEG analysis, with only 2 cases (moderate EEG abnormalities) not recommended for cooling. Conclusions The combination of clinically relevant qEEG markers led to the development of an effective automated EEG classification model, particularly suited for the post-anoxic latency phase. This model successfully discriminated neonates requiring TH. Significance The proposed model has potential as a bedside clinical decision support tool for TH.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
Inserm
CHU Lille

Collections :

• Lille Neurosciences & Cognition (LilNCog) - U 1172
• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2024-10-05T21:00:57Z
2025-03-07T10:28:30Z