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Applied Sciences
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Machine Learning Based Biomedical Signal Processing
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Machine Learning Based Biomedical Signal Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 2195

Special Issue Editors

Dr. Lei Lu

E-Mail Website
Guest Editor
Department of Engineering Science, University of Oxford, Oxford, UK
Interests: clinical machine learning; data science; wearable sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Jiandong Zhou

E-Mail Website
Guest Editor
Division of Health Science, Warwick Medical School, University of Warwick, Coventry, UK
Interests: medical statistics; clinical machine learning; public health; epidemiology; predictive decision

Special Issue Information

Dear Colleagues,

Machine learning-based biomedical signal processing is reshaping healthcare at an unprecedented pace. Remarkable advancements have been achieved in addressing a range of practical challenges within healthcare domains, including digital health, telemedicine, mental health, assistive rehabilitation, chronic disease management, and human–computer interfaces. In addition, the seamless integration of biomedical signal processing harmoniously complements the ongoing research and innovation in wearable devices and the Internet of Things.

This Special Issue provides a platform for researchers, practitioners, and innovators to showcase these latest research achievements, findings, ideas, and reviews in the field of machine learning-based biomedical signal processing for diverse healthcare applications. These applications encompass, but are not limited to, early diagnosis, disease detection, personalised medicine, mental health, remote monitoring, disease prediction, and biomedical imaging. This will help to promote understanding, improve delivery outcomes, and provide treatment and prevention for health-related issues. Authors are encouraged to submit manuscripts for publication in (but not limited to) the following areas:

  • Biomedical signal processing and modelling;
  • Machine intelligence for diagnosis and predictive analysis;
  • Medical imaging, modelling, and simulation;
  • Multimodal learning for healthcare applications;
  • Big data analytics for biomedical applications;
  • Machine learning for telemedicine;
  • Machine learning in mental health and psychology;
  • Robotic systems and assistive rehabilitation;
  • Wearable technologies for remote monitoring;
  • Machine learning for human–machine interaction.

Dr. Lei Lu
Dr. Jiandong Zhou
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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 processing
  • machine learning
  • healthcare
  • diagnosis
  • modelling
  • predictive analysis

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Published Papers (2 papers)

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Research

12 pages, 1799 KiB  
Article
Subject-Independent Model for Reconstructing Electrocardiography Signals from Photoplethysmography Signals
by Yanke Guo, Shiyong Li, Zhencheng Chen and Qunfeng Tang
Appl. Sci. 2024, 14(13), 5773; https://doi.org/10.3390/app14135773 - 2 Jul 2024
Viewed by 859
Abstract
Electrocardiography (ECG) is the gold standard for monitoring vital signs and for diagnosing, controlling, and preventing cardiovascular diseases (CVDs). However, ECG requires continuous user participation, and cannot be used for continuous cardiac monitoring. In contrast to ECG, photoplethysmography (PPG) devices do not require [...] Read more.
Electrocardiography (ECG) is the gold standard for monitoring vital signs and for diagnosing, controlling, and preventing cardiovascular diseases (CVDs). However, ECG requires continuous user participation, and cannot be used for continuous cardiac monitoring. In contrast to ECG, photoplethysmography (PPG) devices do not require continued user involvement, and can offer ongoing and long-term detection capabilities. However, from a medical perspective, ECG can provide more information about the heart. Currently, most existing work contains different signals recorded from the same subject in training and test sets. This study proposes a neural network model based on a 1D convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network. This neural network model can directly reconstruct ECG signals from PPG signals. The learned features are captured from the CNN model and fed into the BiLSTM model. In order to verify the validity of the model, it is evaluated using the MIMIC II dataset in the completely subject-independent model (records are placed in a training set, and a test set appears once, but the test signal belongs to a record that is not in the training set). The Pearson’s correlation coefficient between the reconstructed ECG and the reference ECG of the proposed model is 0.963 in the completely subject-independence model. The results of the proposed model are better than those of several cited state-of-the-art models. The results of our trained model indicate that we can obtain reconstructed ECGs that are highly similar to reference ECGs in the completely subject-independent model. Full article
(This article belongs to the Special Issue Machine Learning Based Biomedical Signal Processing)
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12 pages, 2036 KiB  
Article
A Lightweight Convolutional Neural Network Method for Two-Dimensional PhotoPlethysmoGraphy Signals
by Feng Zhao, Xudong Zhang and Zhenyu He
Appl. Sci. 2024, 14(10), 3963; https://doi.org/10.3390/app14103963 - 7 May 2024
Cited by 1 | Viewed by 911
Abstract
Data information security on wearable devices has emerged as a significant concern among users, so it becomes urgent to explore authentication methods based on wearable devices. Using PhotoPlethysmoGraphy (PPG) signals for identity authentication has been proven effective in biometric authentication. This paper proposes [...] Read more.
Data information security on wearable devices has emerged as a significant concern among users, so it becomes urgent to explore authentication methods based on wearable devices. Using PhotoPlethysmoGraphy (PPG) signals for identity authentication has been proven effective in biometric authentication. This paper proposes a convolutional neural network authentication method based on 2D PPG signals applied to wearable devices. This method uses Markov Transition Field technology to convert one-dimensional PPG signal data into two-dimensional image data, which not only retains the characteristics of the signal but also enriches the spatial information. Afterward, considering that wearable devices usually have limited resources, a lightweight convolutional neural network model is also designed in this method, which reduces resource consumption and computational complexity while ensuring high performance. It is proved experimentally that this method achieves 98.62% and 96.17% accuracy on the training set and test set, respectively, an undeniable advantage compared to the traditional one-dimensional deep learning method and the classical two-dimensional deep learning method. Full article
(This article belongs to the Special Issue Machine Learning Based Biomedical Signal Processing)
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