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Biosensors
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Advanced Machine Learning Techniques for Modeling, Signal Processing, and Intelligent...
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Special Issue "Advanced Machine Learning Techniques for Modeling, Signal Processing, and Intelligent Circuits and Biosensors"

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Intelligent Biosensors and Bio-Signal Processing".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 10110

Special Issue Editors

Dr. Riccardo Pernice

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Guest Editor
Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: portable/wearable monitoring systems; electrocardiographic (ECG) and photoplethysmographic (PPG) acquisition sensors and systems; biomedical signal processing; autonomic nervous system; heart rate variability (HRV) analysis; brain–heart interactions
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chi-Hua Chen

E-Mail Website
Guest Editor
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
Interests: machine learning; internet of things; healthcare systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Genggeng Liu

E-Mail
Guest Editor
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
Interests: computational intelligence; Internet of things; IC design algorithm
Dr. Lingjuan Lyu
E-Mail Website
Guest Editor
Department of Computer Science, National University of Singapore, Singapore 119077, Singapore
Interests: privacy-preserving; machine/deep learning; Internet of Things
Prof. Dr. Hsiao-Ting Tseng

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Guest Editor
Department of Information Management, National United University, Miaoli 36063, Taiwan
Interests: healthcare information analysis; AI; clinical decision support system; Internet of Things
Prof. Dr. Liang-Hung Wang

E-Mail Website
Guest Editor
College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
Interests: biosignal procession; ECG signal acquisition; biosignal feature detection; AI algorithm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The latest advanced machine learning techniques have contributed to great developments in various areas of interest to the academic and engineering community. In terms of methodologies employed in this field, it has seen the rise of deep learning techniques and the novel methodologies such as generative adversarial networks and natural language generation (including deep learning, reinforcement learning, transfer, and extreme learning), but may also promote the development of deep learning algorithms or methods, optimization approaches, novel hardware, software, network, and communication architectures.

The purpose of the Special Issue “Advanced Machine Learning Techniques for Modeling, Signal Processing, and Intelligent Circuits and Biosensors” is to provide a forum for academic researcher, engineers, and nature and social scientists, and practitioners to present new academic research and industrial development on machine learning for modeling, signal processing, and intelligent circuits and biosensors. This Special Issue will publish original research papers in the machine learning field, covering new theories, algorithms, circuits, systems, and biosensors, as well as novel implementations and applications. The Special Issue invites papers on various disciplines of biosensor techniques and applications that address but are not limited to the following topics:

  • Research on sensors networks for engineering applications
  • Software and hardware architectures for sensorial systems
  • Electrical and mechanical engineering, manufacturing, failure detection, energy management, smart grid management, and optimization
  • Robotics and automation, computer vision, and pattern recognition applications
  • Transportation management, environmental engineering, surveying and geospatial engineering, spatial planning, and remote sensing
  • Materials science and engineering
  • Machine/deep learning and computer-aided design, manufacturing, and diagnosis engineering
  • Evolutionary intelligent optimization and applications
  • Data analysis with machine learning at the edge, and on the move, including localization, personalization, and optimization
  • Biomedical circuits and systems engineering
  • Biomedical sensing and health outcomes
  • Biomedical image reconstruction and quantitative image analysis
  • Biosignal processing of wearable sensors, mobile sensors, EEG headcaps and headbands, ECG sensors, breathing monitors, EMG sensors, and temperature sensors

Dr. Riccardo Pernice
Prof. Dr. Chi-Hua Chen
Prof. Dr. Genggeng Liu
Dr. Lingjuan Lyu
Prof. Dr. Hsiao-Ting Tseng
Prof. Dr. Liang-Hung Wang

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

  • Biosensors networks for engineering applications
  • Software and hardware architectures for sensorial systems
  • Machine/deep learning and computer- aided design, manufacturing, and diagnosis engineering
  • Biomedical circuits and systems engineering
  • Biomedical sensing and health outcomes

Published Papers (3 papers)

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Article
Chronic Study on Brainwave Authentication in a Real-Life Setting: An LSTM-Based Bagging Approach
by
Liuyin Yang
,
Arno Libert
and
Marc M. Van Hulle
Biosensors 2021, 11(10), 404; https://doi.org/10.3390/bios11100404 - 18 Oct 2021
Cited by 3 | Viewed by 2037
Abstract
With the advent of the digital age, concern about how to secure authorized access to sensitive data is increasing. Besides traditional authentication methods, there is an interest in biometric traits such as fingerprints, the iris, facial characteristics, and, recently, brainwaves, primarily based on [...] Read more.
With the advent of the digital age, concern about how to secure authorized access to sensitive data is increasing. Besides traditional authentication methods, there is an interest in biometric traits such as fingerprints, the iris, facial characteristics, and, recently, brainwaves, primarily based on electroencephalography (EEG). Current work on EEG-based authentication focuses on acute recordings in laboratory settings using high-end equipment, typically equipped with 64 channels and operating at a high sampling rate. In this work, we validated the feasibility of EEG-based authentication in a real-world, out-of-laboratory setting using a commercial dry-electrode EEG headset and chronic recordings on a population of 15 healthy people. We used an LSTM-based network with bootstrap aggregating (bagging) to decode our recordings in response to a multitask scheme consisting of performed and imagined motor tasks, and showed that it improved the performance of the standard LSTM approach. We achieved an authentication accuracy, false acceptance rate (FAR), and false rejection rate (FRR) of 92.6%, 2.5%, and 5.0% for the performed motor task; 92.5%, 2.6%, and 4.9% for the imagined motor task; and 93.0%, 1.9%, and 5.1% for the combined tasks, respectively. We recommend the proposed method for time- and data-limited scenarios. Full article
(This article belongs to the Special Issue Advanced Machine Learning Techniques for Modeling, Signal Processing, and Intelligent Circuits and Biosensors)
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Article
Cross-Domain Transfer Learning for PCG Diagnosis Algorithm
by
Kuo-Kun Tseng
,
Chao Wang
,
Yu-Feng Huang
,
Guan-Rong Chen
,
Kai-Leung Yung
and
Wai-Hung Ip
Biosensors 2021, 11(4), 127; https://doi.org/10.3390/bios11040127 - 20 Apr 2021
Cited by 9 | Viewed by 2869
Abstract
Cardiechema is a way to reflect cardiovascular disease where the doctor uses a stethoscope to help determine the heart condition with a sound map. In this paper, phonocardiogram (PCG) is used as a diagnostic signal, and a deep learning diagnostic framework is proposed. [...] Read more.
Cardiechema is a way to reflect cardiovascular disease where the doctor uses a stethoscope to help determine the heart condition with a sound map. In this paper, phonocardiogram (PCG) is used as a diagnostic signal, and a deep learning diagnostic framework is proposed. By improving the architecture and modules, a new transfer learning and boosting architecture is mainly employed. In addition, a segmentation method is designed to improve on the existing signal segmentation methods, such as R wave to R wave interval segmentation and fixed segmentation. For the evaluation, the final diagnostic architecture achieved a sustainable performance with a public PCG database. Full article
(This article belongs to the Special Issue Advanced Machine Learning Techniques for Modeling, Signal Processing, and Intelligent Circuits and Biosensors)
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Article
Robust Single-Trial EEG-Based Authentication Achieved with a 2-Stage Classifier
by
Uladzislau Barayeu
,
Nastassya Horlava
,
Arno Libert
and
Marc Van Hulle
Biosensors 2020, 10(9), 124; https://doi.org/10.3390/bios10090124 - 13 Sep 2020
Cited by 8 | Viewed by 2956
Abstract
The risk of personal data exposure through unauthorized access has never been as imminent as today. To counter this, biometric authentication has been proposed: the use of distinctive physiological and behavioral characteristics as a form of identification and access control. One of the [...] Read more.
The risk of personal data exposure through unauthorized access has never been as imminent as today. To counter this, biometric authentication has been proposed: the use of distinctive physiological and behavioral characteristics as a form of identification and access control. One of the recent developments is electroencephalography (EEG)-based authentication. It builds on the subject-specific nature of brain responses which are difficult to recreate artificially. We propose an authentication system based on EEG signals recorded in response to a simple motor paradigm. Authentication is achieved with a novel two-stage decoder. In the first stage, EEG signal features are extracted using an inception- and a VGG-like deep learning neural network (NN) both of which we compare with principal component analysis (PCA). In the second stage, a support vector machine (SVM) is used for binary classification to authenticate the subject based on the extracted features. All decoders are trained on EEG motor-movement data recorded from 105 subjects. We achieved with the VGG-like NN-SVM decoder a false-acceptance rate (FAR) of 2.55% with an overall accuracy of 88.29%, a FAR of 3.33% with an accuracy of 87.47%, and a FAR of 2.89% with an accuracy of 90.68% for 8, 16, and 64 channels, respectively. With the Inception-like NN-SVM decoder we achieved a false-acceptance rate (FAR) of 4.08% with an overall accuracy of 87.29%, a FAR of 3.53% with an accuracy of 85.31%, and a FAR of 1.27% with an accuracy of 93.40% for 8, 16, and 64 channels, respectively. The PCA-SVM decoder achieved accuracies of 92.09%, 92.36%, and 95.64% with FARs of 2.19%, 2.17%, and 1.26% for 8, 16, and 64 channels, respectively. Full article
(This article belongs to the Special Issue Advanced Machine Learning Techniques for Modeling, Signal Processing, and Intelligent Circuits and Biosensors)
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