Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units Through EEG, ECG and Video Recordings: A Survey

被引:15
作者
Olmi, Benedetta [1 ]
Frassineti, Lorenzo [1 ,2 ]
Lanata, Antonio [1 ]
Manfredi, Claudia [1 ]
机构
[1] Univ Firenze, Dept Informat Engn, I-50139 Florence, Italy
[2] Univ Siena, Dept Med Biotechnol, I-53100 Siena, Italy
关键词
Pediatrics; Electroencephalography; Electrocardiography; Video recording; Training; Monitoring; Hospitals; Deep learning; ECG; EEG; HRV; machine learning; neonatal seizures; neonatal seizure detection; NICUs; NSD; video analysis; seizure detection; MOTOR-ACTIVITY SIGNALS; QUANTIFYING MOTION; EXTRACTION; ALGORITHM; STRENGTH; NEWBORNS;
D O I
10.1109/ACCESS.2021.3118227
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely, effective and efficient clinical intervention. The continuous video electroencephalogram (v-EEG) is the gold standard for monitoring neonatal seizures, but it requires specialized equipment and expert staff available 24/24h. The purpose of this study is to present an overview of the main Neonatal Seizure Detection (NSD) systems developed during the last ten years that implement Artificial Intelligence techniques to detect and report the temporal occurrence of neonatal seizures. Expert systems based on the analysis of EEG, ECG and video recordings are investigated, and their usefulness as support tools for the medical staff in detecting and diagnosing neonatal seizures in NICUs is evaluated. EEG-based NSD systems show better performance than systems based on other signals. Recently ECG analysis, particularly the related HRV analysis, seems to be a promising marker of brain damage. Moreover, video analysis could be helpful to identify inconspicuous but pathological movements. This study highlights possible future developments of the NSD systems: a multimodal approach that exploits and combines the results of the EEG, ECG and video approaches and a system able to automatically characterize etiologies might provide additional support to clinicians in seizures diagnosis.
引用
收藏
页码:138174 / 138191
页数:18
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