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Artificial Intelligence and Wearable Sensors for Biosignal Measurement and Processing
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Artificial Intelligence and Wearable Sensors for Biosignal Measurement and Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4391

Special Issue Editors

Dr. Ming Huang

E-Mail Website
Guest Editor
1. School of Data Science, Nagoya City University, Nagoya 467-8501, Japan
2. Division of Information Science, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
Interests: health informatics for health promotion and disease management
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Zhu

E-Mail Website
Guest Editor
Biomedical Information Engineering Lab, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan
Interests: biomedical signal; biomedical image; biomedical information processing and medical instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, "Artificial Intelligence and Wearable Sensors for Biosignal Measurement and Processing", explores the latest research and developments in the fields of artificial intelligence (AI) and wearable sensors for biosignal measurement and processing. The use of wearable sensors and AI has gained increasing attention in recent years, as they offer a non-invasive and cost-effective approach to monitoring health and diagnosing various conditions. This Special Issue covers a range of topics, including the integration of AI with wearable devices for monitoring vital signs, the use of AI for improving the accuracy and reliability of biosignal measurements, and the potential of AI to revolutionize healthcare through personalized medicine and disease diagnosis. The papers in this Special Issue showcase the innovative and cutting-edge work being undertaken in the field of AI and wearable sensors, and demonstrate exciting possibilities for the future of healthcare, sports and wellness. Overall, this Special Issue aims to provide a comprehensive overview of the current state of research in this exciting and rapidly evolving field.

Dr. Ming Huang
Dr. Xin Zhu
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. Sensors 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 2600 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

  • vital signs
  • digital biomarkers
  • artificial intelligence
  • preventive medicine
  • privacy

Published Papers (3 papers)

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Research

12 pages, 4878 KiB  
Article
The Stress Phase Angle Measurement Using Spectral Domain Optical Coherence Tomography
by Yuqian Zhao, Zhibo Zhu, Huiwen Jiang, Yao Yu, Jian Liu, Jingmin Luan, Yi Wang and Zhenhe Ma
Sensors 2023, 23(17), 7597; https://doi.org/10.3390/s23177597 - 01 Sep 2023
Cited by 1 | Viewed by 815
Abstract
The stress phase angle (SPA), defined as the temporal phase angle between circumferential stress (CS) in the arterial wall and wall shear stress (WSS), is utilized to investigate the interactions between CS and WSS. SPA serves as an important parameter for the early [...] Read more.
The stress phase angle (SPA), defined as the temporal phase angle between circumferential stress (CS) in the arterial wall and wall shear stress (WSS), is utilized to investigate the interactions between CS and WSS. SPA serves as an important parameter for the early diagnosis of cardiovascular disease. In this study, we proposed a novel method for measuring SPA using spectral domain optical coherence tomography (SD-OCT). The multi-M-mode scan strategy is adopted for interference spectrum acquisition. The phases of CS and WSS are extracted from the corresponding structural and flow velocity images of SD-OCT. The method is validated by measuring SPA in the outflow tract (OFT) of chick embryonic hearts and the common carotid artery of mice. To the best of our knowledge, this is the first time that OCT has been used for SPA measurement. Full article
(This article belongs to the Special Issue Artificial Intelligence and Wearable Sensors for Biosignal Measurement and Processing)
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14 pages, 3118 KiB  
Article
Estimating Blood Pressure during Exercise with a Cuffless Sphygmomanometer
by Kenta Hayashi, Yuka Maeda, Takumi Yoshimura, Ming Huang and Toshiyo Tamura
Sensors 2023, 23(17), 7399; https://doi.org/10.3390/s23177399 - 24 Aug 2023
Viewed by 1218
Abstract
Accurately measuring blood pressure (BP) is essential for maintaining physiological health, which is commonly achieved using cuff-based sphygmomanometers. Several attempts have been made to develop cuffless sphygmomanometers. To increase their accuracy and long-term variability, machine learning methods can be applied for analyzing photoplethysmogram [...] Read more.
Accurately measuring blood pressure (BP) is essential for maintaining physiological health, which is commonly achieved using cuff-based sphygmomanometers. Several attempts have been made to develop cuffless sphygmomanometers. To increase their accuracy and long-term variability, machine learning methods can be applied for analyzing photoplethysmogram (PPG) signals. Here, we propose a method to estimate the BP during exercise using a cuffless device. The BP estimation process involved preprocessing signals, feature extraction, and machine learning techniques. To ensure the reliability of the signals extracted from the PPG, we employed the skewness signal quality index and the RReliefF algorithm for signal selection. Thereafter, the BP was estimated using the long short-term memory (LSTM)-based neural network. Seventeen young adult males participated in the experiments, undergoing a structured protocol composed of rest, exercise, and recovery for 20 min. Compared to the BP measured using a non-invasive voltage clamp-type continuous sphygmomanometer, that estimated by the proposed method exhibited a mean error of 0.32 ± 7.76 mmHg, which is equivalent to the accuracy of a cuff-based sphygmomanometer per regulatory standards. By enhancing patient comfort and improving healthcare outcomes, the proposed approach can revolutionize BP monitoring in various settings, including clinical, home, and sports environments. Full article
(This article belongs to the Special Issue Artificial Intelligence and Wearable Sensors for Biosignal Measurement and Processing)
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16 pages, 844 KiB  
Article
Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM
by Hezam Albaqami, Ghulam Mubashar Hassan and Amitava Datta
Sensors 2023, 23(13), 5960; https://doi.org/10.3390/s23135960 - 27 Jun 2023
Cited by 2 | Viewed by 1978
Abstract
Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain [...] Read more.
Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet–Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2.0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model. Full article
(This article belongs to the Special Issue Artificial Intelligence and Wearable Sensors for Biosignal Measurement and Processing)
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