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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 37419

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Prof. Dr. Naeem Ramzan

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School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK
Interests: artificial intelligence; affective computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

The domain of eHealth is emerging alongside the advancement of information and telecommunication technologies and the need for improved healthcare services. The global eHealth market is growing at an astonishing rate (CAGR of 22.4%). eHealth is mainly driven by continuous need for patient monitoring and accessibility of healthcare records at various locations. The adoption of Artificial Intelligence (AI), smart cloud storage and smart devices, i.e., smartphone, wearable sensors and other sensing devices around us, provides significant support for personalized healthcare services to the patients, doctors, and hospitals.

Due to eHealth evolution, there are remarkable overtures and challenges. eHealth needs to utilise recent advances in AI, sensors and communication techologies to provide innovative solution for healthcare, with particular focus on remote patient monitoring.

The aim of this Special Issue is to collect original research and review articles discussing ways to improve eHealth in an AI and sensor-based environment.

Potential topics include (but are not limited) to the following:

AI solutions for eHealth.
Emerging architecture and technologies for ehealth.
Remote patient monitoring.
Wireless sensor networks for eHealth.
Sensors for patient health-factors monitoring.
Sensor of Medical Things (SoMT) in eHealth.
Wearable sensors and systems used in eHealth.
Security and privacy issues in eHealth.
Machine and deep learning approaches for Health Data.

Prof. Naeem Ramzan
Guest Editor

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Published Papers (10 papers)

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Research

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14 pages, 703 KiB

Open AccessArticle

Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations

by Hadeel Alzoubi, Raid Alzubi and Naeem Ramzan

Sensors 2023, 23(9), 4439; https://doi.org/10.3390/s23094439 - 1 May 2023

Cited by 1 | Viewed by 3621

Abstract

Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This prediction can either be considered as an analysis model that can lead to a better understanding of gene functions that underlie human disease or as a black box in order to be used in decision support systems and in early disease detection. Deep learning techniques have gained more popularity recently. In this work, we propose a deep-learning framework for disease risk prediction. The proposed framework employs a multilayer perceptron (MLP) in order to predict individuals’ disease status. The proposed framework was applied to the Wellcome Trust Case-Control Consortium (WTCCC), the UK National Blood Service (NBS) Control Group, and the 1958 British Birth Cohort (58C) datasets. The performance comparison of the proposed framework showed that the proposed approach outperformed the other methods in predicting disease risk, achieving an area under the curve (AUC) up to 0.94. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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16 pages, 1005 KiB

Open AccessArticle

ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring

by Ali Rida Ismail, Slavisa Jovanovic, Naeem Ramzan and Hassan Rabah

Sensors 2023, 23(3), 1697; https://doi.org/10.3390/s23031697 - 3 Feb 2023

Cited by 13 | Viewed by 3368

Abstract

Increased life expectancy in most countries is a result of continuous improvements at all levels, starting from medicine and public health services, environmental and personal hygiene to the use of the most advanced technologies by healthcare providers. Despite these significant improvements, especially at the technological level in the last few decades, the overall access to healthcare services and medical facilities worldwide is not equally distributed. Indeed, the end beneficiary of these most advanced healthcare services and technologies on a daily basis are mostly residents of big cities, whereas the residents of rural areas, even in developed countries, have major difficulties accessing even basic medical services. This may lead to huge deficiencies in timely medical advice and assistance and may even cause death in some cases. Remote healthcare is considered a serious candidate for facilitating access to health services for all; thus, by using the most advanced technologies, providing at the same time high quality diagnosis and ease of implementation and use. ECG analysis and related cardiac diagnosis techniques are the basic healthcare methods providing rapid insights in potential health issues through simple visualization and interpretation by clinicians or by automatic detection of potential cardiac anomalies. In this paper, we propose a novel machine learning (ML) architecture for the ECG classification regarding five heart diseases based on temporal convolution networks (TCN). The proposed design, which implements a dilated causal one-dimensional convolution on the input heartbeat signals, seems to be outperforming all existing ML methods with an accuracy of

96.12 %

and an F1 score of

84.13 %

, using a reduced number of parameters (

10.2

K). Such results make the proposed TCN architecture a good candidate for low power consumption hardware platforms, and thus its potential use in low cost embedded devices for remote health monitoring. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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13 pages, 668 KiB

Open AccessArticle

Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models

by Maria Trigka and Elias Dritsas

Sensors 2023, 23(3), 1193; https://doi.org/10.3390/s23031193 - 20 Jan 2023

Cited by 26 | Viewed by 4124

Abstract

The heart is the most vital organ of the human body; thus, its improper functioning has a significant impact on human life. Coronary artery disease (CAD) is a disease of the coronary arteries through which the heart is nourished and oxygenated. It is due to the formation of atherosclerotic plaques on the wall of the epicardial coronary arteries, resulting in the narrowing of their lumen and the obstruction of blood flow through them. Coronary artery disease can be delayed or even prevented with lifestyle changes and medical intervention. Long-term risk prediction of coronary artery disease will be the area of interest in this work. In this specific research paper, we experimented with various machine learning (ML) models after the use or non-use of the synthetic minority oversampling technique (SMOTE), evaluating and comparing them in terms of accuracy, precision, recall and an area under the curve (AUC). The results showed that the stacking ensemble model after the SMOTE with 10-fold cross-validation prevailed over the other models, achieving an accuracy of 90.9 %, a precision of 96.7%, a recall of 87.6% and an AUC equal to 96.1%. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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21 pages, 395 KiB

Open AccessArticle

Automated Detection of Substance-Use Status and Related Information from Clinical Text

by Raid Alzubi, Hadeel Alzoubi, Stamos Katsigiannis, Daune West and Naeem Ramzan

Sensors 2022, 22(24), 9609; https://doi.org/10.3390/s22249609 - 8 Dec 2022

Cited by 2 | Viewed by 1980

Abstract

This study aims to develop and evaluate an automated system for extracting information related to patient substance use (smoking, alcohol, and drugs) from unstructured clinical text (medical discharge records). The authors propose a four-stage system for the extraction of the substance-use status and related attributes (type, frequency, amount, quit-time, and period). The first stage uses a keyword search technique to detect sentences related to substance use and to exclude unrelated records. In the second stage, an extension of the NegEx negation detection algorithm is developed and employed for detecting the negated records. The third stage involves identifying the temporal status of the substance use by applying windowing and chunking methodologies. Finally, in the fourth stage, regular expressions, syntactic patterns, and keyword search techniques are used in order to extract the substance-use attributes. The proposed system achieves an F1-score of up to 0.99 for identifying substance-use-related records, 0.98 for detecting the negation status, and 0.94 for identifying temporal status. Moreover, F1-scores of up to 0.98, 0.98, 1.00, 0.92, and 0.98 are achieved for the extraction of the amount, frequency, type, quit-time, and period attributes, respectively. Natural Language Processing (NLP) and rule-based techniques are employed efficiently for extracting substance-use status and attributes, with the proposed system being able to detect substance-use status and attributes over both sentence-level and document-level data. Results show that the proposed system outperforms the compared state-of-the-art substance-use identification system on an unseen dataset, demonstrating its generalisability. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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17 pages, 4462 KiB

Open AccessArticle

OptiFit: Computer-Vision-Based Smartphone Application to Measure the Foot from Images and 3D Scans

by Riyad Bin Rafiq, Kazi Miftahul Hoque, Muhammad Ashad Kabir, Sayed Ahmed and Craig Laird

Sensors 2022, 22(23), 9554; https://doi.org/10.3390/s22239554 - 6 Dec 2022

Cited by 2 | Viewed by 4506

Abstract

The foot is a vital organ, as it stabilizes the impact forces between the human skeletal system and the ground. Hence, precise foot dimensions are essential not only for custom footwear design, but also for the clinical treatment of foot health. Most existing research on measuring foot dimensions depends on a heavy setup environment, which is costly and ineffective for daily use. In addition, there are several smartphone applications online, but they are not suitable for measuring the exact foot shape for custom footwear, both in clinical practice and public use. In this study, we designed and implemented computer-vision-based smartphone application OptiFit that provides the functionality to automatically measure the four essential dimensions (length, width, arch height, and instep girth) of a human foot from images and 3D scans. We present an instep girth measurement algorithm, and we used a pixel per metric algorithm for measurement; these algorithms were accordingly integrated with the application. Afterwards, we evaluated our application using 19 medical-grade silicon foot models (12 males and 7 females) from different age groups. Our experimental evaluation shows that OptiFit could measure the length, width, arch height, and instep girth with an accuracy of 95.23%, 96.54%, 89.14%, and 99.52%, respectively. A two-tailed paired t-test was conducted, and only the instep girth dimension showed a significant discrepancy between the manual measurement (MM) and the application-based measurement (AM). We developed a linear regression model to adjust the error. Further, we performed comparative analysis demonstrating that there were no significant errors between MM and AM, and the application offers satisfactory performance as a foot-measuring application. Unlike other applications, the iOS application we developed, OptiFit, fulfils the requirements to automatically measure the exact foot dimensions for individually fitted footwear. Therefore, the application can facilitate proper foot measurement and enhance awareness to prevent foot-related problems caused by inappropriate footwear. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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22 pages, 3172 KiB

Open AccessArticle

A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features

by Tie Zhang, Zequan Huang, Yanbiao Zou, Jun Zhao and Yuwei Ke

Sensors 2022, 22(18), 7084; https://doi.org/10.3390/s22187084 - 19 Sep 2022

Cited by 3 | Viewed by 2245

Abstract

(1) Background: Incontinence and its complications pose great difficulties in the care of the disabled. Currently, invasive incontinence monitoring methods are too invasive, expensive, and bulky to be widely used. Compared with previous methods, bowel sound monitoring is the most commonly used non-invasive monitoring method for intestinal diseases and may even provide clinical support for doctors. (2) Methods: This paper proposes a method based on the features of bowel sound signals, which uses a BP classification neural network to predict bowel defecation and realizes a non-invasive collection of physiological signals. Firstly, according to the physiological function of human defecation, bowel sound signals were selected for monitoring and analysis before defecation, and a portable non-invasive bowel sound collection system was built. Then, the detector algorithm based on iterative kurtosis and the signal processing method based on Kalman filter was used to process the signal to remove the aliasing noise in the bowel sound signal, and feature extraction was carried out in the time domain, frequency domain, and time–frequency domain. Finally, BP neural network was selected to build a classification training method for the features of bowel sound signals. (3) Results: Experimental results based on real data sets show that the proposed method can converge to a stable state and achieve a prediction accuracy of 88.71% in 232 records, which is better than other classification methods. (4) Conclusions: The result indicates that the proposed method could provide a high-precision defecation prediction result for patients with fecal incontinence, so as to prepare for defecation in advance. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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16 pages, 7855 KiB

Open AccessArticle

Research on a Defecation Pre-Warning Algorithm for the Disabled Elderly Based on a Semi-Supervised Generative Adversarial Network

by Yanbiao Zou, Shenghong Wu, Tie Zhang and Yuanhang Yang

Sensors 2022, 22(17), 6704; https://doi.org/10.3390/s22176704 - 5 Sep 2022

Cited by 2 | Viewed by 2112

Abstract

The elderly population in China is continuously increasing, and the disabled account for a large proportion of the elderly population. An effective solution is urgently needed for incontinence among disabled elderly people. Compared with disposable adult diapers, artificial sphincter implantation and medication for incontinence, the defecation pre-warning method is more flexible and convenient. However, due to the complex human physiology and individual differences, its development is limited. Based on the aging trend of the population and clinical needs, this paper proposes a bowel sound acquisition system and a defecation pre-warning method and system based on a semi-supervised generative adversarial network. A network model was established to predict defecation using bowel sounds. The experimental results show that the proposed method can effectively classify bowel sounds with or without defecation tendency, and the accuracy reached 94.4%. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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21 pages, 671 KiB

Open AccessArticle

Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique

by Saad Irfan, Nadeem Anjum, Turke Althobaiti, Abdullah Alhumaidi Alotaibi, Abdul Basit Siddiqui and Naeem Ramzan

Sensors 2022, 22(15), 5606; https://doi.org/10.3390/s22155606 - 27 Jul 2022

Cited by 25 | Viewed by 5826

Abstract

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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Review

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25 pages, 2862 KiB

Open AccessReview

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review

by Saima Gulzar Ahmad, Tassawar Iqbal, Anam Javaid, Ehsan Ullah Munir, Nasira Kirn, Sana Ullah Jan and Naeem Ramzan

Sensors 2022, 22(12), 4362; https://doi.org/10.3390/s22124362 - 9 Jun 2022

Cited by 15 | Viewed by 6367

Abstract

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential for maternal and infant health, since maternal and infant health is vital for a healthy society. Over the last few years, researchers have delved into sensing and artificially intelligent healthcare systems for maternal and infant health. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. Since these healthcare systems deal with large amounts of data, significant development is also noted in the computing platforms. The relevant literature reports the potential impact of ICT-enabled systems for improving maternal and infant health. This article reviews wearable sensors and AI algorithms based on existing systems designed to predict the risk factors during and after pregnancy for both mothers and infants. This review covers sensors and AI algorithms used in these systems and analyzes each approach with its features, outcomes, and novel aspects in chronological order. It also includes discussion on datasets used and extends challenges as well as future work directions for researchers. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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11 pages, 264 KiB

Open AccessPerspective

Intra- and Extra-Hospitalization Monitoring of Vital Signs—Two Sides of the Same Coin: Perspectives from LIMS and Greenline-HT Study Operators

by Filomena Pietrantonio, Antonio Vinci, Massimo Maurici, Tiziana Ciarambino, Barbara Galli, Alessandro Signorini, Vincenzo Mirco La Fazia, Francescantonio Rosselli, Luca Fortunato, Rosa Iodice, Marco Materazzo, Alessandro Ciuca, Lamberto Carlo Maria Cicerchia, Matteo Ruggeri, Dario Manfellotto, Francesco Rosiello and Andrea Moriconi

Sensors 2023, 23(12), 5408; https://doi.org/10.3390/s23125408 - 7 Jun 2023

Cited by 4 | Viewed by 1693

Abstract

Background: In recent years, due to the epidemiological transition, the burden of very complex patients in hospital wards has increased. Telemedicine usage appears to be a potential high-impact factor in helping with patient management, allowing hospital personnel to assess conditions in out-of-hospital scenarios. Methods: To investigate the management of chronic patients during both hospitalization for disease and discharge, randomized studies (LIMS and Greenline-HT) are ongoing in the Internal Medicine Unit at ASL Roma 6 Castelli Hospital. The study endpoints are clinical outcomes (from a patient’s perspective). In this perspective paper, the main findings of these studies, from the operators’ point of view, are reported. Operator opinions were collected from structured and unstructured surveys conducted among the staff involved, and their main themes are reported in a narrative manner. Results: Telemonitoring appears to be linked to a reduction in side-events and side-effects, which represent some of most commons risk factors for re-hospitalization and for delayed discharge during hospitalization. The main perceived advantages are increased patient safety and the quick response in case of emergency. The main disadvantages are believed to be related to low patient compliance and an infrastructural lack of optimization. Conclusions: The evidence of wireless monitoring studies, combined with the analysis of activity data, suggests the need for a model of patient management that envisages an increase in the territory of structures capable of offering patients subacute care (the possibility of antibiotic treatments, blood transfusions, infusion support, and pain therapy) for the timely management of chronic patients in the terminal phase, for which treatment in acute wards must be guaranteed only for a limited time for the management of the acute phase of their diseases. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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