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Neonatal Seizure Detection Using a Wearable Multi-Sensor System

Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For examp...

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Autores principales: Chen, Hongyu, Wang, Zaihao, Lu, Chunmei, Shu, Feng, Chen, Chen, Wang, Laishuan, Chen, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10294985/
https://www.ncbi.nlm.nih.gov/pubmed/37370589
http://dx.doi.org/10.3390/bioengineering10060658
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author Chen, Hongyu
Wang, Zaihao
Lu, Chunmei
Shu, Feng
Chen, Chen
Wang, Laishuan
Chen, Wei
author_facet Chen, Hongyu
Wang, Zaihao
Lu, Chunmei
Shu, Feng
Chen, Chen
Wang, Laishuan
Chen, Wei
author_sort Chen, Hongyu
collection PubMed
description Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant’s movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children’s Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures.
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spelling pubmed-102949852023-06-28 Neonatal Seizure Detection Using a Wearable Multi-Sensor System Chen, Hongyu Wang, Zaihao Lu, Chunmei Shu, Feng Chen, Chen Wang, Laishuan Chen, Wei Bioengineering (Basel) Article Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant’s movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children’s Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures. MDPI 2023-05-29 /pmc/articles/PMC10294985/ /pubmed/37370589 http://dx.doi.org/10.3390/bioengineering10060658 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Hongyu
Wang, Zaihao
Lu, Chunmei
Shu, Feng
Chen, Chen
Wang, Laishuan
Chen, Wei
Neonatal Seizure Detection Using a Wearable Multi-Sensor System
title Neonatal Seizure Detection Using a Wearable Multi-Sensor System
title_full Neonatal Seizure Detection Using a Wearable Multi-Sensor System
title_fullStr Neonatal Seizure Detection Using a Wearable Multi-Sensor System
title_full_unstemmed Neonatal Seizure Detection Using a Wearable Multi-Sensor System
title_short Neonatal Seizure Detection Using a Wearable Multi-Sensor System
title_sort neonatal seizure detection using a wearable multi-sensor system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10294985/
https://www.ncbi.nlm.nih.gov/pubmed/37370589
http://dx.doi.org/10.3390/bioengineering10060658
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