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Bioengineering
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Biomedical Data Mining and Machine Learning for Disease Diagnosis and...
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Biomedical Data Mining and Machine Learning for Disease Diagnosis and Health Informatics

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 10216

Special Issue Editors

Dr. Yunfeng Wu

E-Mail Website
Guest Editor
School of Informatics, Xiamen University, Xiamen 361005, China
Interests: biomedical signal processing; artificial intelligence; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Meihong Wu

E-Mail Website
Guest Editor
School of Information Science and Technology, Xiamen University, Xiamen, China
Interests: cognitive science; biomedical signal processing; machine learning

Special Issue Information

Dear Colleagues,

Biomedical data mining can provide effective computer engineering solutions and mathematical models, which assist physicians in better interpreting the medical data and understanding the pathological progress. Machine learning is an important subfield of artificial intelligence. Today, machine learning algorithms are widely utilized in biomedical applications, including physiological signal processing, healthcare monitoring, gene sequencing, drug development, and medical image analysis.

This Special Issue seeks high-quality communication, article, and review submissions that present state-of-the-art machine learning algorithms and advanced biomedical data mining paradigms in the scopes of computer-aided diagnosis and health informatics. The Special Issue aims to exchange ideas in the research community and promotes international collaborations for developing medical diagnostic systems.

The topics of interest include but are not limited to the following application fields for biomedical data mining and machine learning:

  • Biomedical and health informatics;
  • Wearable sensor data processing;
  • Biomedical signal analysis;
  • Physiological monitoring;
  • Public heath informatics;
  • Medical imaging;
  • Neural and rehabilitation engineering;
  • Biomedical data mining;
  • Bioinformatics;
  • Computational biology applications;
  • Machine learning for and biomechanics;
  • Support decision making for medical diagnoses.

Dr. Yunfeng Wu
Dr. Meihong Wu
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

  • physiological signal analysis
  • machine learning
  • biomedical data mining
  • health informatics
  • computer-aided diagnosis

Published Papers (5 papers)

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Editorial

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4 pages, 166 KiB  
Editorial
Biomedical Data Mining and Machine Learning for Disease Diagnosis and Health Informatics
by Yunfeng Wu and Meihong Wu
Bioengineering 2024, 11(4), 364; https://doi.org/10.3390/bioengineering11040364 - 11 Apr 2024
Viewed by 294
Abstract
Powered by biomedical data mining and machine learning technologies, smart healthcare uses cutting-edge medical innovative tools to facilitate the development of sophisticated decision support systems for disease diagnosis and health informatics [...] Full article
(This article belongs to the Special Issue Biomedical Data Mining and Machine Learning for Disease Diagnosis and Health Informatics)

Research

Jump to: Editorial

22 pages, 1701 KiB  
Article
Physiological Signal Analysis and Stress Classification from VR Simulations Using Decision Tree Methods
by Syem Ishaque, Naimul Khan and Sridhar Krishnan
Bioengineering 2023, 10(7), 766; https://doi.org/10.3390/bioengineering10070766 - 26 Jun 2023
Cited by 1 | Viewed by 1274
Abstract
Stress is induced in response to any mental, physical or emotional change associated with our daily experiences. While short term stress can be quite beneficial, prolonged stress is detrimental to the heart, muscle tissues and immune system. In order to be proactive against [...] Read more.
Stress is induced in response to any mental, physical or emotional change associated with our daily experiences. While short term stress can be quite beneficial, prolonged stress is detrimental to the heart, muscle tissues and immune system. In order to be proactive against these symptoms, it is important to assess the impact of stress due to various activities, which is initially determined through the change in the sympathetic (SNS) and parasympathetic (PNS) nervous systems. After acquiring physiological data wirelessly through captive electrocardiogram (ECG), galvanic skin response (GSR) and respiration (RESP) sensors, 21 time, frequency, nonlinear, GSR and respiration features were manually extracted from 15 subjects ensuing a baseline phase, virtual reality (VR) roller coaster simulation, color Stroop task and VR Bubble Bloom game. This paper presents a comprehensive physiological analysis of stress from an experiment involving a VR video game Bubble Bloom to manage stress levels. A personalized classification and regression tree (CART) model was developed using a novel Gini index algorithm in order to effectively classify binary classes of stress. A novel K-means feature was derived from 11 other features and used as an input in the Decision Tree (DT) algorithm, strong learners Ensemble Gradient Boosting (EGB) and Extreme Gradient Boosting (XGBoost (XGB)) embedded in a pipeline to classify 5 classes of stress. Results obtained indicate that heart rate (HR), approximate entropy (ApEN), low frequency and high frequency ratio (LF/HF), low frequency (LF), standard deviation (SD1), GSR and RESP all reduced and high frequency (HF) increased following the VR Bubble Bloom game phase. The personalized CART model was able to classify binary stress with 87.75% accuracy. It proved to be more effective than other related studies. EGB was able to classify binary stress with 100% accuracy, which outperformed every other related study. XGBoost and DT were able to classify five classes of stress with 72.22% using the novel K-means feature. This feature produced less error and better model performance in comparison to using all the features. Results substantiate that our proposed methods were more effective for stress classification than most related studies. Full article
(This article belongs to the Special Issue Biomedical Data Mining and Machine Learning for Disease Diagnosis and Health Informatics)
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15 pages, 3124 KiB  
Article
Identifying Intraoperative Spinal Cord Injury Location from Somatosensory Evoked Potentials’ Time-Frequency Components
by Hanlei Li, Songkun Gao, Rong Li, Hongyan Cui, Wei Huang, Yongcan Huang and Yong Hu
Bioengineering 2023, 10(6), 707; https://doi.org/10.3390/bioengineering10060707 - 11 Jun 2023
Cited by 1 | Viewed by 1005
Abstract
Excessive distraction in corrective spine surgery can lead to iatrogenic distraction spinal cord injury. Diagnosis of the location of the spinal cord injury helps in early removal of the injury source. The time-frequency components of the somatosensory evoked potential have been reported to [...] Read more.
Excessive distraction in corrective spine surgery can lead to iatrogenic distraction spinal cord injury. Diagnosis of the location of the spinal cord injury helps in early removal of the injury source. The time-frequency components of the somatosensory evoked potential have been reported to provide information on the location of spinal cord injury, but most studies have focused on contusion injuries of the cervical spine. In this study, we established 19 rat models of distraction spinal cord injury at different levels and collected the somatosensory evoked potentials of the hindlimb and extracted their time-frequency components. Subsequently, we used k-medoid clustering and naive Bayes to classify spinal cord injury at the C5 and C6 level, as well as spinal cord injury at the cervical, thoracic, and lumbar spine, respectively. The results showed that there was a significant delay in the latency of the time-frequency components distributed between 15 and 30 ms and 50 and 150 Hz in all spinal cord injury groups. The overall classification accuracy was 88.28% and 84.87%. The results demonstrate that the k-medoid clustering and naive Bayes methods are capable of extracting the time-frequency component information depending on the spinal cord injury location and suggest that the somatosensory evoked potential has the potential to diagnose the location of a spinal cord injury. Full article
(This article belongs to the Special Issue Biomedical Data Mining and Machine Learning for Disease Diagnosis and Health Informatics)
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13 pages, 7837 KiB  
Article
PPG2ECGps: An End-to-End Subject-Specific Deep Neural Network Model for Electrocardiogram Reconstruction from Photoplethysmography Signals without Pulse Arrival Time Adjustments
by Qunfeng Tang, Zhencheng Chen, Rabab Ward, Carlo Menon and Mohamed Elgendi
Bioengineering 2023, 10(6), 630; https://doi.org/10.3390/bioengineering10060630 - 23 May 2023
Cited by 2 | Viewed by 2759
Abstract
Electrocardiograms (ECGs) provide crucial information for evaluating a patient’s cardiovascular health; however, they are not always easily accessible. Photoplethysmography (PPG), a technology commonly used in wearable devices such as smartwatches, has shown promise for constructing ECGs. Several methods have been proposed for ECG [...] Read more.
Electrocardiograms (ECGs) provide crucial information for evaluating a patient’s cardiovascular health; however, they are not always easily accessible. Photoplethysmography (PPG), a technology commonly used in wearable devices such as smartwatches, has shown promise for constructing ECGs. Several methods have been proposed for ECG reconstruction using PPG signals, but some require signal alignment during the training phase, which is not feasible in real-life settings where ECG signals are not collected at the same time as PPG signals. To address this challenge, we introduce PPG2ECGps, an end-to-end, patient-specific deep-learning neural network utilizing the W-Net architecture. This novel approach enables direct ECG signal reconstruction from PPG signals, eliminating the need for signal alignment. Our experiments show that the proposed model achieves mean values of 0.977 mV for Pearson’s correlation coefficient, 0.037 mV for the root mean square error, and 0.010 mV for the normalized dynamic time-warped distance when comparing reconstructed ECGs to reference ECGs from a dataset of 500 records. As PPG signals are more accessible than ECG signals, our proposed model has significant potential to improve patient monitoring and diagnosis in healthcare settings via wearable devices. Full article
(This article belongs to the Special Issue Biomedical Data Mining and Machine Learning for Disease Diagnosis and Health Informatics)
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20 pages, 1267 KiB  
Article
An Efficient Approach to Predict Eye Diseases from Symptoms Using Machine Learning and Ranker-Based Feature Selection Methods
by Ahmed Al Marouf, Md Mozaharul Mottalib, Reda Alhajj, Jon Rokne and Omar Jafarullah
Bioengineering 2023, 10(1), 25; https://doi.org/10.3390/bioengineering10010025 - 24 Dec 2022
Cited by 7 | Viewed by 3691
Abstract
The eye is generally considered to be the most important sensory organ of humans. Diseases and other degenerative conditions of the eye are therefore of great concern as they affect the function of this vital organ. With proper early diagnosis by experts and [...] Read more.
The eye is generally considered to be the most important sensory organ of humans. Diseases and other degenerative conditions of the eye are therefore of great concern as they affect the function of this vital organ. With proper early diagnosis by experts and with optimal use of medicines and surgical techniques, these diseases or conditions can in many cases be either cured or greatly mitigated. Experts that perform the diagnosis are in high demand and their services are expensive, hence the appropriate identification of the cause of vision problems is either postponed or not done at all such that corrective measures are either not done or done too late. An efficient model to predict eye diseases using machine learning (ML) and ranker-based feature selection (r-FS) methods is therefore proposed which will aid in obtaining a correct diagnosis. The aim of this model is to automatically predict one or more of five common eye diseases namely, Cataracts (CT), Acute Angle-Closure Glaucoma (AACG), Primary Congenital Glaucoma (PCG), Exophthalmos or Bulging Eyes (BE) and Ocular Hypertension (OH). We have used efficient data collection methods, data annotations by professional ophthalmologists, applied five different feature selection methods, two types of data splitting techniques (train-test and stratified k-fold cross validation), and applied nine ML methods for the overall prediction approach. While applying ML methods, we have chosen suitable classic ML methods, such as Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), AdaBoost (AB), Logistic Regression (LR), k-Nearest Neighbour (k-NN), Bagging (Bg), Boosting (BS) and Support Vector Machine (SVM). We have performed a symptomatic analysis of the prominent symptoms of each of the five eye diseases. The results of the analysis and comparison between methods are shown separately. While comparing the methods, we have adopted traditional performance indices, such as accuracy, precision, sensitivity, F1-Score, etc. Finally, SVM outperformed other models obtaining the highest accuracy of 99.11% for 10-fold cross-validation and LR obtained 98.58% for the split ratio of 80:20. Full article
(This article belongs to the Special Issue Biomedical Data Mining and Machine Learning for Disease Diagnosis and Health Informatics)
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