Overview of a Sleep Monitoring Protocol for a Large Natural Population

Liu, Minghui; Zhu, Hangyu; Tang, Jinbu; Chen, Hongyu; Chen, Chen; Luo, Jingchun; Chen, Wei

doi:10.1007/s43657-023-00102-4

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Based on previous research on wearable neonatal vital monitoring systems [ 10 ], we designed a wearable multi-sensor platform (MSP) for neonatal seizure monitoring, as shown in Figure 2 . Compared with the design of first-generation smart clothing, the whole structure of second-generation smart clothing adopts an open front-end design to expose more skin during continuous monitoring and reduce the impact of clothing on newborns.…”

Section: Methodsmentioning

confidence: 99%

“…Monitoring seizures from a variety of electrophysiological and behavioral In addition to abnormal EEG signals, a seizure is usually manifested by behavioral and physiological signal changes, such as repeated movements of the arms, hands, legs and eyes and is sometimes accompanied by muscle contraction at a fluctuant velocity in the opposite direction as well as changes in the patterns of electrocardiogram (ECG) and respiration [1,6,7]. With the rapid development of related technologies in Body Sensor Networks (BSNs) over the past decade [8][9][10], researchers are searching for new automatic monitoring methods that differ from video EEG methods for monitoring seizures in chil-dren [11,12]. Monitoring seizures from a variety of electrophysiological and behavioral signals obtained using diverse modern signal processing methods, including electrocardiogram, myoelectricity, electrodermal activity and respiratory and behavioral signals, has been studied [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Neonatal Seizure Detection Using a Wearable Multi-Sensor System

Chen,

Wang,

et al. 2023

Bioengineering

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neonatal Seizure Detection Using a Wearable Multi-Sensor System

Chen,

Wang,

et al. 2023

Bioengineering

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“…Fourth, in terms of signal acquisition, EMG signals are also easy to acquire. Meanwhile, the submental EMG signal is of great significance for the differential staging of wake and REM stages (being especially important for the differential diagnosis of REM sleep behavior disorder) [51]. In future work, we could try to use both EOG and EMG modalities as inputs to the network, to explore a portable sleep monitoring method with a better staging effect.…”

Section: Limitations and Future Workmentioning

confidence: 99%

The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods

Zhu

Shu

et al. 2023

Bioengineering

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The influence of the coupled electroencephalography (EEG) signal in electrooculography (EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and prefrontal EEG are collected at close range, it is not clear whether EEG couples in EOG or not, and whether or not the EOG signal can achieve good sleep staging results due to its intrinsic characteristics. In this paper, the effect of a coupled EEG signal in an EOG signal on automatic sleep staging is explored. The blind source separation algorithm was used to extract a clean prefrontal EEG signal. Then the raw EOG signal and clean prefrontal EEG signal were processed to obtain EOG signals coupled with different EEG signal contents. Afterwards, the coupled EOG signals were fed into a hierarchical neural network, including a convolutional neural network and recurrent neural network for automatic sleep staging. Finally, an exploration was performed using two public datasets and one clinical dataset. The results showed that using a coupled EOG signal could achieve an accuracy of 80.4%, 81.1%, and 78.9% for the three datasets, slightly better than the accuracy of sleep staging using the EOG signal without coupled EEG. Thus, an appropriate content of coupled EEG signal in an EOG signal improved the sleep staging results. This paper provides an experimental basis for sleep staging with EOG signals.

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“…proposed the concept of representing biosignals as time-dependent graphs and presented GraphS4mer, a general graph neural network (GNN) architecture that improves performance on biosignal classification tasks by modeling spatiotemporal dependencies in biosignals [52] . Gupta et al applied DL to capture the mechanism of visual asymmetry through psychological experiments [53] . In the future, researchers may be able to use EOG-HCI in psychological and neurological research to contribute to the monitoring and treatment of some visual diseases.…”

Section: Future Research and Trendsmentioning

confidence: 99%

Review of electrooculography-based human-computer interaction: recent technologies, challenges and future trends

Tao,

Huang,

Chen

et al. 2023

Conn Health Telemed

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Electrooculography-based Human-Computer Interaction (EOG-HCI) is an emerging field. Research in this domain aims to capture eye movement patterns by measuring the corneal-retinal potential difference. This enables translating eye movements into commands, facilitating human-computer interaction through eye movements. This paper reviews articles published from 2002 to 2022 in the EOG-HCI domain, aiming to provide a comprehensive analysis of the current developments and challenges in this field. It includes a detailed and systematic analysis of EOG signal electrode arrangement, hardware design for EOG signal acquisition, commonly used features, and algorithms. Representative studies in each section are presented to help readers quickly grasp the common technologies in this field. Furthermore, the paper emphasizes the analysis of interaction design within the EOG-HCI domain, categorizing different interaction task types and modalities to provide insights into prevalent interaction research. The focus of current research in the field is revealed by examining commonly used evaluation metrics. Lastly, a user-centered EOG-HCI research model is proposed to visually present the current research status in the EOG-HCI field from the perspective of users. Additionally, we highlight the challenges and opportunities in this field.

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Overview of a Sleep Monitoring Protocol for a Large Natural Population

Cited by 8 publications

References 35 publications

Neonatal Seizure Detection Using a Wearable Multi-Sensor System

Neonatal Seizure Detection Using a Wearable Multi-Sensor System

The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods

Review of electrooculography-based human-computer interaction: recent technologies, challenges and future trends

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