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Biosensors
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Biomedical Signal Processing in Healthcare and Disease Diagnosis
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Biomedical Signal Processing in Healthcare and Disease Diagnosis

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensors and Healthcare".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 28917

Special Issue Editors

Prof. Dr. Yi-Hung Liu

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Guest Editor
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
Interests: neurophysiological signal processing; computer-aided diagnosis for diseases; brain–computer interface
Dr. Tzyy-Ping Jung

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Guest Editor
Institute for Neural Computation, University of California, San Diego, CA 92093, USA
Interests: biomedical signal processing; brain–computer interface; neural engineering
Dr. Chien-Te Wu

E-Mail Website
Guest Editor
International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo 113-0033, Japan
Interests: neuroimaging; cognitive neuroscience; functional connectivity analysis for brain diseases
Dr. Paul C.-P. Chao

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Interests: biomedical sensing; medical image and signal processing

Special Issue Information

Dear Colleagues,

The research topic of biomedical signal processing has been studied for more than two decades. However, with the rapid advancement of biosensing, IoT, AI, and embedded/edge/cloud computing technologies, it has been shown that there is a need to re-evaluate the effectiveness of conventional methods and/or to develop new biomedical signal processing methods with high validity and reliability for their application in healthcare and disease diagnosis. For example, telemedicine has recently proved its importance in healthcare, especially during the current COVID-19 pandemic. A critical and urgent concern is how to provide people with timely and accurate screening, diagnosis, and treatment. Real-time biomedical signal processing carried out from a remote location using portable devices or apps could represent a solution. In this context, designing biomedical signal processing methods with low computational complexity is crucial. As software as a medical device (SaMD) and digital medicine become increasingly common in clinical practice, a central question is how to improve the effectiveness (sensitivity, specificity, etc.) of a biomarker in the diagnosis of a specific disease, validate the robustness (e.g., reliability) of the marker across hospitals, and even examine the association between the biomarker and the pathological mechanism of the disease (i.e., interpretability).

This Special Issue aims to provide a cross-disciplinary forum for international researchers to share and exchange their research outcomes in biomedical signal processing, with a focus on medical signals and images in clinical practice. We invite researchers to submit original works focusing on the design and/or demonstration of advanced biomedical signal processing methods for healthcare and disease diagnosis, including preprocessing, feature extraction, classification, and prediction. We also solicit papers relating to the development of biomedical signal-actuated healthcare systems. Review articles comparing state-of-the-art biomedical signal processing methods in healthcare and disease diagnosis are also welcome.

Prof. Dr. Yi-Hung Liu
Dr. Tzyy-Ping Jung
Dr. Chien-Te Wu
Dr. Paul C.-P. Chao
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. Biosensors 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

  • biomedical signal
  • disease diagnosis
  • healthcare
  • machine learning
  • artificial intelligence

Published Papers (11 papers)

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Research

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29 pages, 11252 KiB  
Article
Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models
by Amran Hossain, Mohammad Tariqul Islam, Tawsifur Rahman, Muhammad E. H. Chowdhury, Anas Tahir, Serkan Kiranyaz, Kamarulzaman Mat, Gan Kok Beng and Mohamed S. Soliman
Biosensors 2023, 13(3), 302; https://doi.org/10.3390/bios13030302 - 21 Feb 2023
Cited by 9 | Viewed by 2211
Abstract
Automated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and monitoring of the progression of brain disease. The manual detection, classification, and segmentation of tumors are extremely time-consuming but crucial tasks due to the [...] Read more.
Automated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and monitoring of the progression of brain disease. The manual detection, classification, and segmentation of tumors are extremely time-consuming but crucial tasks due to the tumor’s pattern. In this paper, we propose a new lightweight segmentation model called MicrowaveSegNet (MSegNet), which segments the brain tumor, and a new classifier called the BrainImageNet (BINet) model to classify the RMW images. Initially, three hundred (300) RMW brain image samples were obtained from our sensors-based microwave brain imaging (SMBI) system to create an original dataset. Then, image preprocessing and augmentation techniques were applied to make 6000 training images per fold for a 5-fold cross-validation. Later, the MSegNet and BINet were compared to state-of-the-art segmentation and classification models to verify their performance. The MSegNet has achieved an Intersection-over-Union (IoU) and Dice score of 86.92% and 93.10%, respectively, for tumor segmentation. The BINet has achieved an accuracy, precision, recall, F1-score, and specificity of 89.33%, 88.74%, 88.67%, 88.61%, and 94.33%, respectively, for three-class classification using raw RMW images, whereas it achieved 98.33%, 98.35%, 98.33%, 98.33%, and 99.17%, respectively, for segmented RMW images. Therefore, the proposed cascaded model can be used in the SMBI system. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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23 pages, 9057 KiB  
Article
A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images
by Amran Hossain, Mohammad Tariqul Islam, Sharul Kamal Abdul Rahim, Md Atiqur Rahman, Tawsifur Rahman, Haslina Arshad, Amit Khandakar, Mohamed Arslane Ayari and Muhammad E. H. Chowdhury
Biosensors 2023, 13(2), 238; https://doi.org/10.3390/bios13020238 - 07 Feb 2023
Cited by 15 | Viewed by 2685
Abstract
Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called microwave brain image network (MBINet) using a self-organized operational neural [...] Read more.
Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called microwave brain image network (MBINet) using a self-organized operational neural network (Self-ONN) is proposed to classify the reconstructed microwave brain (RMB) images into six classes. Initially, an experimental antenna sensor-based microwave brain imaging (SMBI) system was implemented, and RMB images were collected to create an image dataset. It consists of a total of 1320 images: 300 images for the non-tumor, 215 images for each single malignant and benign tumor, 200 images for each double benign tumor and double malignant tumor, and 190 images for the single benign and single malignant tumor classes. Then, image resizing and normalization techniques were used for image preprocessing. Thereafter, augmentation techniques were applied to the dataset to make 13,200 training images per fold for 5-fold cross-validation. The MBINet model was trained and achieved accuracy, precision, recall, F1-score, and specificity of 96.97%, 96.93%, 96.85%, 96.83%, and 97.95%, respectively, for six-class classification using original RMB images. The MBINet model was compared with four Self-ONNs, two vanilla CNNs, ResNet50, ResNet101, and DenseNet201 pre-trained models, and showed better classification outcomes (almost 98%). Therefore, the MBINet model can be used for reliably classifying the tumor(s) using RMB images in the SMBI system. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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11 pages, 1233 KiB  
Article
Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring
by I-Min Chiu, Chi-Yung Cheng, Po-Kai Chang, Chao-Jui Li, Fu-Jen Cheng and Chun-Hung Richard Lin
Biosensors 2023, 13(1), 23; https://doi.org/10.3390/bios13010023 - 25 Dec 2022
Cited by 2 | Viewed by 2142
Abstract
Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed [...] Read more.
Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia from electrocardiogram (ECG) data. We used the Medical Information Mart for Intensive Care III database as our source of data and obtained more than 20 ECG records from each included patient during a single hospital admission. We focused on lead II recordings, along with corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine algorithm to predict dysglycemia. The model was able to predict dysglycemia using a single heartbeat with an AUC of 0.92 ± 0.09, a sensitivity of 0.92 ± 0.10, and specificity of 0.84 ± 0.04. After applying 10 s majority voting, the AUC of the model’s dysglycemia prediction increased to 0.97 ± 0.06. This study showed that a personalized machine-learning algorithm can accurately detect dysglycemia from a single-lead ECG. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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13 pages, 3535 KiB  
Article
Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection
by Wei Wu, Longhua Ma, Bin Lian, Weiming Cai and Xianghong Zhao
Biosensors 2022, 12(12), 1087; https://doi.org/10.3390/bios12121087 - 28 Nov 2022
Cited by 1 | Viewed by 1692
Abstract
Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression. However, EEG signals are time-varying, and the distributions of the different subjects’ data are [...] Read more.
Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression. However, EEG signals are time-varying, and the distributions of the different subjects’ data are non-uniform, which poses a bad influence on depression detection. In this paper, the deep learning method with domain adaptation is applied to detect depression based on EEG signals. Firstly, the EEG signals are preprocessed and then transformed into pictures by two methods: the first one is to present the three channels of EEG separately in the same image, and the second one is the RGB synthesis of the three channels of EEG. Finally, the training and prediction are performed in the domain adaptation model. The results indicate that the domain adaptation model can effectively extract EEG features and obtain an average accuracy of 77.0 ± 9.7%. This paper proves that the domain adaptation method can effectively weaken the inherent differences of EEG signals, making the diagnosis of different users more accurate. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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20 pages, 5667 KiB  
Article
An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System
by Wen Chen, Shih-Kang Chen, Yi-Hung Liu, Yu-Jen Chen and Chin-Sheng Chen
Biosensors 2022, 12(10), 772; https://doi.org/10.3390/bios12100772 - 20 Sep 2022
Cited by 6 | Viewed by 2416
Abstract
Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain–computer interface (BCI) system [...] Read more.
Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain–computer interface (BCI) system and a steady-state visual evoked potential (SSVEP) to manipulate an electric wheelchair. While operating the human–machine interface, three types of SSVEP scenarios involving a real-time virtual stimulus are displayed on a monitor or mixed reality (MR) goggles to produce the EEG signals. Canonical correlation analysis (CCA) is used to classify the EEG signals into the corresponding class of command and the information transfer rate (ITR) is used to determine the effect. The experimental results show that the proposed SSVEP stimulus generates the EEG signals because of the high classification accuracy of CCA. This is used to control an electric wheelchair along a specific path. Simultaneous localization and mapping (SLAM) is the mapping method that is available in the robotic operating software (ROS) platform that is used for the wheelchair system for this study. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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25 pages, 2055 KiB  
Article
Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings
by Samuel Boudet, Agathe Houzé de l’Aulnoit, Laurent Peyrodie, Romain Demailly and Denis Houzé de l’Aulnoit
Biosensors 2022, 12(9), 691; https://doi.org/10.3390/bios12090691 - 27 Aug 2022
Cited by 5 | Viewed by 2721
Abstract
We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a [...] Read more.
We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a clinical alert system to assist the practitioner. Three models were developed and used to detect (i) FSs on the MHR channel (the FSMHR model), (ii) the MHR and FSs on the Doppler FHR sensor (the FSDop model), and (iii) FSs on the scalp ECG channel (the FSScalp model). The FSDop model was the most useful because FSs are far more frequent on the Doppler FHR channel. All three models were based on a multilayer, symmetric, GRU, and were trained on data recorded during the first and second stages of delivery. The FSMHR and FSDop models were also trained on antepartum recordings. The training dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. In an initial evaluation of routine clinical practice, 30 fully annotated recordings for each sensor type (mean duration: 5 h for MHR and Doppler sensors, and 3 h for the scalp ECG sensor) were analyzed. The sensitivity, positive predictive value (PPV) and accuracy were respectively 62.20%, 87.1% and 99.90% for the FSMHR model, 93.1%, 95.6% and 99.68% for the FSDop model, and 44.6%, 87.2% and 99.93% for the FSScalp model. We built a second test dataset with a more solid ground truth by selecting 45 periods (lasting 20 min, on average) on which the Doppler FHR and scalp ECG signals were recorded simultaneously. Using scalp ECG data, the experts estimated the true FHR value more reliably and thus annotated the Doppler FHR channel more precisely. The models achieved a sensitivity of 53.3%, a PPV of 62.4%, and an accuracy of 97.29%. In comparison, two experts (blinded to the scalp ECG data) respectively achieved a sensitivity of 15.7%, a PPV of 74.3%, and an accuracy of 96.91% and a sensitivity of 60.7%, a PPV of 83.5% and an accuracy of 98.24%. Hence, the models performed at expert level (better than one expert and worse than the other), although a well-trained expert with good knowledge of FSs could probably do better in some cases. The models and datasets have been included in the Fetal Heart Rate Morphological Analysis open-source MATLAB toolbox and can be used freely for research purposes. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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14 pages, 3559 KiB  
Article
A Detection-Service-Mobile Three-Terminal Software Platform for Point-of-Care Infectious Disease Detection System
by Xiangyi Su, Yile Fang, Haoran Liu, Yue Wang, Minjie Ji, Zhu Chen, Hui Chen, Song Li, Yan Deng, Lian Jin, Yuanying Zhang, Murugan Ramalingam and Nongyue He
Biosensors 2022, 12(9), 684; https://doi.org/10.3390/bios12090684 - 25 Aug 2022
Cited by 1 | Viewed by 1673
Abstract
The traditional infectious disease detection process is cumbersome, and there is only a single application scenario. In recent years, with the development of the medical industry and the impact of the epidemic situation, the number of infectious disease detection instruments based on nursing [...] Read more.
The traditional infectious disease detection process is cumbersome, and there is only a single application scenario. In recent years, with the development of the medical industry and the impact of the epidemic situation, the number of infectious disease detection instruments based on nursing point detection has been increasing. Due to this trend, many detection instruments and massive detection data urgently need to be managed. In addition, the experiment failed due to the abnormal fluorescence curve generated by a human operator or sample impurities. Finally, the geographic information system has also played an active role in spreading and preventing infectious diseases; this paper designs a “detection-service-mobile” three-terminal system to realize the control of diagnostic instruments and the comprehensive management of data. Machine learning is used to classify the enlarged curve and calculate the cycle threshold of the positive curve; combined with a geographic information system, the detection results are marked on the mobile terminal map to realize the visual display of the positive results of nucleic acid amplification detection and the early warning of infectious diseases. In the research, applying this system to portable field pathogen detection is feasible and practical. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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12 pages, 2568 KiB  
Article
Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device
by Vijay Kumar Verma and Wen-Yen Lin
Biosensors 2022, 12(8), 605; https://doi.org/10.3390/bios12080605 - 05 Aug 2022
Cited by 2 | Viewed by 1941
Abstract
Chronic obstructive pulmonary disease (COPD) is a significantly concerning disease, and is ranked highest in terms of 30-day hospital readmission. Generally, physical activity (PA) of daily living reflects the health status and is proposed as a strong indicator of 30-day hospital readmission for [...] Read more.
Chronic obstructive pulmonary disease (COPD) is a significantly concerning disease, and is ranked highest in terms of 30-day hospital readmission. Generally, physical activity (PA) of daily living reflects the health status and is proposed as a strong indicator of 30-day hospital readmission for patients with COPD. This study attempted to predict 30-day hospital readmission by analyzing continuous PA data using machine learning (ML) methods. Data were collected from 16 patients with COPD over 3877 days, and clinical information extracted from the patients’ hospital records. Activity-based parameters were conceptualized and evaluated, and ML models were trained and validated to retrospectively analyze the PA data, identify the nonlinear classification characteristics of different risk factors, and predict hospital readmissions. Overall, this study predicted 30-day hospital readmission and prediction performance is summarized as two distinct approaches: prediction-based performance and event-based performance. In a prediction-based performance analysis, readmissions predicted with 70.35% accuracy; and in an event-based performance analysis, the total 30-day readmissions were predicted with a precision of 72.73%. PA data reflect the health status; thus, PA data can be used to predict hospital readmissions. Predicting readmissions will improve patient care, reduce the burden of medical costs burden, and can assist in staging suitable interventions, such as promoting PA, alternate treatment plans, or changes in lifestyle to prevent readmissions. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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17 pages, 969 KiB  
Article
Classification of Dysphonic Voices in Parkinson’s Disease with Semi-Supervised Competitive Learning Algorithm
by Guidong Bao, Mengchen Lin, Xiaoqian Sang, Yangcan Hou, Yixuan Liu and Yunfeng Wu
Biosensors 2022, 12(7), 502; https://doi.org/10.3390/bios12070502 - 09 Jul 2022
Cited by 6 | Viewed by 1861
Abstract
This article proposes a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson’s disease (PD). The acoustic parameters of voice records were grouped into the families of jitter, shimmer, harmonic-to-noise, frequency, and nonlinear measures, respectively. The linear correlations were computed [...] Read more.
This article proposes a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson’s disease (PD). The acoustic parameters of voice records were grouped into the families of jitter, shimmer, harmonic-to-noise, frequency, and nonlinear measures, respectively. The linear correlations were computed within each acoustic parameter family. According to the correlation matrix results, the jitter, shimmer, and harmonic-to-noise parameters presented as highly correlated in terms of Pearson’s correlation coefficients. Then, the principal component analysis (PCA) technique was implemented to eliminate the redundant dimensions of the acoustic parameters for each family. The Mann–Whitney–Wilcoxon hypothesis test was used to evaluate the significant difference of the PCA-projected features between the healthy subjects and PD patients. Eight dominant PCA-projected features were selected based on the eigenvalue threshold criterion and the statistical significance level (p < 0.05) of the hypothesis test. The SSCL algorithm proposed in this paper included the procedures of the competitive prototype seed selection, K-means optimization, and the nearest neighbor classifications. The pattern classification experimental results showed that the proposed SSCL method can provide the excellent diagnostic performances in terms of accuracy (0.838), recall (0.825), specificity (0.85), precision (0.846), F-score (0.835), Matthews correlation coefficient (0.675), area under the receiver operating characteristic curve (0.939), and Kappa coefficient (0.675), which were consistently better than those results of conventional KNN or SVM classifiers. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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15 pages, 3403 KiB  
Article
Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording
by Yuwei Zhang, Aihua Gu, Zhijun Xiao, Yantao Xing, Chenxi Yang, Jianqing Li and Chengyu Liu
Biosensors 2022, 12(7), 475; https://doi.org/10.3390/bios12070475 - 30 Jun 2022
Cited by 16 | Viewed by 3632
Abstract
Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) [...] Read more.
Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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Review

Jump to: Research

20 pages, 1693 KiB  
Review
A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain–Computer Interface Based on Movement-Related Cortical Potentials
by Ramadhan Rashid Said, Md Belal Bin Heyat, Keer Song, Chao Tian and Zhe Wu
Biosensors 2022, 12(12), 1134; https://doi.org/10.3390/bios12121134 - 06 Dec 2022
Cited by 14 | Viewed by 3659
Abstract
To enhance the treatment of motor function impairment, patients’ brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain–computer interface (BCI) field have been using movement-related cortical potential [...] Read more.
To enhance the treatment of motor function impairment, patients’ brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain–computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
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