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

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 11762

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Special Issue Editors

Dr. Flavio Bertini

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Department of Mathematical, Physical and Computer Sciences, University of Parma, 43124 Parma, Italy
Interests: big data; data analysis; health data analysis; data mining; information retrieval; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Rahimeh Rouhi

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Guest Editor

1. Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
2. Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
Interests: machine learning; deep learning; image processing; computer aided diagnosis; data analysis; artificial intelligence

Prof. Dr. Enrique Lopez Droguett

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Guest Editor

Department of Civil and Environmental Engineering, Garrick Institute for the Risk Sciences, University of California, Los Angeles, CA 90095, USA
Interests: optimization; simulation; risk assessment; risk management; advanced machine learning

Special Issue Information

Dear Colleagues,

In the last decade, we have witnessed an increasing diffusion of devices and sensors capable of generating a large amount of useful data to evaluate and make decisions about the health and care of people. This process has some peculiarities that, on the other hand, represent interesting challenges to developing applicable solutions. In particular, the heterogeneity of the sources and the incompleteness of the collected information pave the way for the development of innovative storage, integration, and analysis solutions.

In particular, it is of utmost importance to develop solutions that can guarantee reliability, scalability, and security of the decision-making process and, at the same time, that facilitate the development of data analysis models, also with the use of advanced technologies such as machine learning and artificial intelligence.

This Special Issue aims to explore recent trends in large-data analytics and the application of machine learning methods for healthcare. In particular, original contributions that explore innovative data models for healthcare data; novel data analytics theories and methods; and review articles to the challenging problems of decision-making in healthcare; effectiveness and feasibility of computational solutions in the real world; and trust and privacy are welcome for this Special Issue.

Dr. Flavio Bertini
Dr. Rahimeh Rouhi
Prof. Dr. Enrique Lopez Droguett
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

health data
data analysis
data model
data integration
machine learning
deep learning

Published Papers (3 papers)

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Research

15 pages, 4815 KiB

Open AccessArticle

Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety

by Furkat Safarov, Farkhod Akhmedov, Akmalbek Bobomirzaevich Abdusalomov, Rashid Nasimov and Young Im Cho

Sensors 2023, 23(14), 6459; https://doi.org/10.3390/s23146459 - 17 Jul 2023

Cited by 17 | Viewed by 6679

Abstract

Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has been proven to be one of the most effective approaches for the detection of drowsiness. Robust and accurate drowsiness detection systems can be developed by leveraging deep learning to learn complex coordinate patterns using visual data. Deep learning algorithms have emerged as powerful techniques for drowsiness detection because of their ability to learn automatically from given inputs and feature extractions from raw data. Eye-blinking-based drowsiness detection was applied in this study, which utilized the analysis of eye-blink patterns. In this study, we used custom data for model training and experimental results were obtained for different candidates. The blinking of the eye and mouth region coordinates were obtained by applying landmarks. The rate of eye-blinking and changes in the shape of the mouth were analyzed using computer-vision techniques by measuring eye landmarks with real-time fluctuation representations. An experimental analysis was performed in real time and the results proved the existence of a correlation between yawning and closed eyes, classified as drowsy. The overall performance of the drowsiness detection model was 95.8% accuracy for drowsy-eye detection, 97% for open-eye detection, 0.84% for yawning detection, 0.98% for right-sided falling, and 100% for left-sided falling. Furthermore, the proposed method allowed a real-time eye rate analysis, where the threshold served as a separator of the eye into two classes, the “Open” and “Closed” states. Full article

(This article belongs to the Special Issue Recent Trends in Large-Data Analytics and Machine Learning for Healthcare)

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32 pages, 3430 KiB

Open AccessArticle

Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling

by Alramzana Nujum Navaz, Mohamed Adel Serhani, Hadeel T. El Kassabi and Ikbal Taleb

Sensors 2023, 23(14), 6443; https://doi.org/10.3390/s23146443 - 16 Jul 2023

Cited by 1 | Viewed by 1519

Abstract

Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g., the edge). However, data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, it is crucial to keep an eye on data quality and spot problems as quickly as possible, so that they do not mislead clinical judgments and lead to the wrong course of action. In this article, a novel approach called federated data quality profiling (FDQP) is proposed to assess the quality of the data at the edge. FDQP is inspired by federated learning (FL) and serves as a condensed document or a guide for node data quality assurance. The FDQP formal model is developed to capture the quality dimensions specified in the data quality profile (DQP). The proposed approach uses federated feature selection to improve classifier precision and rank features based on criteria such as feature value, outlier percentage, and missing data percentage. Extensive experimentation using a fetal dataset split into different edge nodes and a set of scenarios were carefully chosen to evaluate the proposed FDQP model. The results of the experiments demonstrated that the proposed FDQP approach positively improved the DQ, and thus, impacted the accuracy of the federated patient similarity network (FPSN)-based machine learning models. The proposed data-quality-aware federated PSN architecture leveraging FDQP model with data collected from edge nodes can effectively improve the data quality and accuracy of the federated patient similarity network (FPSN)-based machine learning models. Our profiling algorithm used lightweight profile exchange instead of full data processing at the edge, which resulted in optimal data quality achievement, thus improving efficiency. Overall, FDQP is an effective method for assessing data quality in the edge computing environment, and we believe that the proposed approach can be applied to other scenarios beyond patient monitoring. Full article

(This article belongs to the Special Issue Recent Trends in Large-Data Analytics and Machine Learning for Healthcare)

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15 pages, 873 KiB

Open AccessArticle

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features

by Cheng-Yu Tsai, Huei-Tyng Huang, Hsueh-Chien Cheng, Jieni Wang, Ping-Jung Duh, Wen-Hua Hsu, Marc Stettler, Yi-Chun Kuan, Yin-Tzu Lin, Chia-Rung Hsu, Kang-Yun Lee, Jiunn-Horng Kang, Dean Wu, Hsin-Chien Lee, Cheng-Jung Wu, Arnab Majumdar and Wen-Te Liu

Sensors 2022, 22(22), 8630; https://doi.org/10.3390/s22228630 - 9 Nov 2022

Cited by 7 | Viewed by 2563

Abstract

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures. Full article

(This article belongs to the Special Issue Recent Trends in Large-Data Analytics and Machine Learning for Healthcare)

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