Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1785063312880304128 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10294985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chenhongyu neonatalseizuredetectionusingawearablemultisensorsystem AT wangzaihao neonatalseizuredetectionusingawearablemultisensorsystem AT luchunmei neonatalseizuredetectionusingawearablemultisensorsystem AT shufeng neonatalseizuredetectionusingawearablemultisensorsystem AT chenchen neonatalseizuredetectionusingawearablemultisensorsystem AT wanglaishuan neonatalseizuredetectionusingawearablemultisensorsystem AT chenwei neonatalseizuredetectionusingawearablemultisensorsystem |