Special Issue "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: 20 December 2022 | Viewed by 1617

Special Issue Editors

Prof. Dr. Yi-Hung Liu
E-Mail Website
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
E-Mail Website
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 2000 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 (3 papers)

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Research

Article
Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device
Biosensors 2022, 12(8), 605; https://doi.org/10.3390/bios12080605 - 05 Aug 2022
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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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Article
Classification of Dysphonic Voices in Parkinson’s Disease with Semi-Supervised Competitive Learning Algorithm
Biosensors 2022, 12(7), 502; https://doi.org/10.3390/bios12070502 - 09 Jul 2022
Viewed by 392
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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Article
Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording
Biosensors 2022, 12(7), 475; https://doi.org/10.3390/bios12070475 - 30 Jun 2022
Viewed by 365
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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