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Bioengineering
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Advances in Smart Sensing and Data Computing for Sleep Analysis,...
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Advances in Smart Sensing and Data Computing for Sleep Analysis, Sleep Disorders Detection and Epileptic Seizure Detection and Prediction

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 12218

Special Issue Editors

Dr. Chen Chen

E-Mail Website
Guest Editor
Human Phenome Institute, Fudan University, Shanghai 201203, China
Interests: sleep analysis; epileptic seizure detection; wearable sensor systems; biomedical signal processing; personalized health monitoring
Prof. Dr. Wei Chen

E-Mail Website
Guest Editor
School of Information Science and Technology, Fudan University, Shanghai 200433, China
Interests: medical monitoring system; patient health monitoring; neonatal monitoring; brain activity monitoring; smart sleep; smart rehabilitation system; wireless body area networks Photo:
Special Issues, Collections and Topics in MDPI journals
Dr. Maarten De Vos

E-Mail Website1 Website2
Guest Editor
Department of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium
Interests: engineering intelligent solutions for perinatal care; sleep monitoring and mobile; real-life brain monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Christos Chatzichristos

E-Mail Website
Guest Editor
Department of Electrical Engineering, KU Leuven, 3000 Leuven, Belgium
Interests: epileptic seizure detection; brain monitoring; personalized treatment; biomedical signal processing; machine learning for biomedical applications

Special Issue Information

Dear Colleagues,

Sleep and epilepsy are neurological activities that are reflected by physiological and behavioral signals. The relationship between sleep and epilepsy is complex and involves interacting mechanisms. Sleep disorders and sleep deprivation can activate epileptiform discharges, while epileptic seizures can contribute to sleep issues, e.g., altered sleep architecture, increased sleep fragmentation, decreased sleep efficiency, etc. Novel sensing and monitoring technologies, as well as data computing approaches to detect and anticipate sleep disorders and seizures, could not only assist disease diagnosis and therapeutic strategies adjustment but also help reveal the interactions underlying these activities.

Considering the latest achievements in advanced sensing technologies, monitoring systems, computational modeling methodologies, etc., this Special Issue aims at attracting original research papers focusing on recent advances in sensing and monitoring systems, and data computing approaches for sleep analysis, sleep disorder detection, and epileptic seizure detection and prediction.

Topics include, but are not limited to:

  • Smart sensing technologies for sleep or epileptic seizure monitoring;
  • Wearable or remote systems for sleep or epileptic seizure monitoring;
  • Signal processing for sleep analysis, sleep disorder detection, and epileptic seizure detection and prediction;
  • Machine learning for sleep analysis, sleep disorder detection, and epileptic seizure detection and prediction;
  • Internet of Things (IoT)-based systems for sleep disorders or epileptic seizure detection and prediction;
  • Computer-aided diagnosis systems for sleep disorders or epileptic seizure detection and prediction.

Dr. Chen Chen
Prof. Dr. Wei Chen
Dr. Maarten De Vos
Dr. Christos Chatzichristos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sleep analysis
  • sleep disorders detection
  • epileptic seizure detection and prediction

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Published Papers (5 papers)

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Research

14 pages, 3841 KiB  
Article
Time-Series Anomaly Detection Based on Dynamic Temporal Graph Convolutional Network for Epilepsy Diagnosis
by Guanlin Wu, Ke Yu, Hao Zhou, Xiaofei Wu and Sixi Su
Bioengineering 2024, 11(1), 53; https://doi.org/10.3390/bioengineering11010053 - 5 Jan 2024
Cited by 1 | Viewed by 1656
Abstract
Electroencephalography (EEG) is typical time-series data. Designing an automatic detection model for EEG is of great significance for disease diagnosis. For example, EEG stands as one of the most potent diagnostic tools for epilepsy detection. A myriad of studies have employed EEG to [...] Read more.
Electroencephalography (EEG) is typical time-series data. Designing an automatic detection model for EEG is of great significance for disease diagnosis. For example, EEG stands as one of the most potent diagnostic tools for epilepsy detection. A myriad of studies have employed EEG to detect and classify epilepsy, yet these investigations harbor certain limitations. Firstly, most existing research concentrates on the labels of sliced EEG signals, neglecting epilepsy labels associated with each time step in the original EEG signal—what we term fine-grained labels. Secondly, a majority of these studies utilize static graphs to depict EEG’s spatial characteristics, thereby disregarding the dynamic interplay among EEG channels. Consequently, the efficient nature of EEG structures may not be captured. In response to these challenges, we propose a novel seizure detection and classification framework—the dynamic temporal graph convolutional network (DTGCN). This method is specifically designed to model the interdependencies in temporal and spatial dimensions within EEG signals. The proposed DTGCN model includes a unique seizure attention layer conceived to capture the distribution and diffusion patterns of epilepsy. Additionally, the model incorporates a graph structure learning layer to represent the dynamically evolving graph structure inherent in the data. We rigorously evaluated the proposed DTGCN model using a substantial publicly available dataset, TUSZ, consisting of 5499 EEGs. The subsequent experimental results convincingly demonstrated that the DTGCN model outperformed the existing state-of-the-art methods in terms of efficiency and accuracy for both seizure detection and classification tasks. Full article
(This article belongs to the Special Issue Advances in Smart Sensing and Data Computing for Sleep Analysis, Sleep Disorders Detection and Epileptic Seizure Detection and Prediction)
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16 pages, 1185 KiB  
Article
SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables
by Irfan Al-Hussaini and Cassie S. Mitchell
Bioengineering 2023, 10(8), 918; https://doi.org/10.3390/bioengineering10080918 - 2 Aug 2023
Cited by 9 | Viewed by 2442
Abstract
This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of [...] Read more.
This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy. Full article
(This article belongs to the Special Issue Advances in Smart Sensing and Data Computing for Sleep Analysis, Sleep Disorders Detection and Epileptic Seizure Detection and Prediction)
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14 pages, 2299 KiB  
Article
Neonatal Seizure Detection Using a Wearable Multi-Sensor System
by Hongyu Chen, Zaihao Wang, Chunmei Lu, Feng Shu, Chen Chen, Laishuan Wang and Wei Chen
Bioengineering 2023, 10(6), 658; https://doi.org/10.3390/bioengineering10060658 - 29 May 2023
Cited by 2 | Viewed by 2318
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Smart Sensing and Data Computing for Sleep Analysis, Sleep Disorders Detection and Epileptic Seizure Detection and Prediction)
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15 pages, 1239 KiB  
Article
The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods
by Hangyu Zhu, Cong Fu, Feng Shu, Huan Yu, Chen Chen and Wei Chen
Bioengineering 2023, 10(5), 573; https://doi.org/10.3390/bioengineering10050573 - 10 May 2023
Cited by 7 | Viewed by 1990
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Smart Sensing and Data Computing for Sleep Analysis, Sleep Disorders Detection and Epileptic Seizure Detection and Prediction)
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16 pages, 7410 KiB  
Article
The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG
by Miguel Bhagubai, Kaat Vandecasteele, Lauren Swinnen, Jaiver Macea, Christos Chatzichristos, Maarten De Vos and Wim Van Paesschen
Bioengineering 2023, 10(4), 491; https://doi.org/10.3390/bioengineering10040491 - 20 Apr 2023
Cited by 4 | Viewed by 2626
Abstract
Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography [...] Read more.
Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm’s detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients. Full article
(This article belongs to the Special Issue Advances in Smart Sensing and Data Computing for Sleep Analysis, Sleep Disorders Detection and Epileptic Seizure Detection and Prediction)
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