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Machine Learning for Biomedical Sensing and Healthcare

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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 (20 October 2023) | Viewed by 27588

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Special Issue Editors

Dr. Georgios D. Barmparis

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Guest Editor

Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, P.O. Box 2208, 71003 Heraklion, Greece
Interests: machine learning in complex systems and medicine; quantum biology; dynamical systems and chaos; nanotechnology; materials science

Dr. Maria E. Marketou

E-Mail Website
Guest Editor

Cardiology Department, Heraklion University Hospital, P.O. Box 1352, 71110 Heraklion, Crete, Greece
Interests: cardiovascular diseases; electrocardiogram; echocardiography; holter monitoring; precision medicine

Prof. Dr. Giorgos P. Tsironis

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Guest Editor

Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, P.O. Box 2208, 71003 Heraklion, Greece
Interests: artificial intelligence and machine learning in physics, engineering, biology, and medicine; statistical mechanics; nonlinear physics; quantum metamaterials

Special Issue Information

Dear Colleagues,

Recent advances in wearable devices that continuously monitor the health state of individuals as well as the utilization of Internet of Things (IoT) capabilities create new possibilities in real time diagnosis and health care. In remote, personalized health care, each patient will be treated individually and receive specific, targeted therapy. The great challenge in the new field of personalized health care is the efficient handling of huge amounts of health data collected by sensors that monitor the health profile of patients on a round-the-clock basis. The analysis of this data can only be done through artificial intelligence (AI) and machine learning techniques.

This Special Issue aims at publishing a collection of articles bringing together state-of-art machine learning techniques that enable handling of big data in the health sector. This data is produced by smart sensors and through proper analysis can empower clinicians’ decision and facilitate personalized diagnosis and prognostication.

Dr. Georgios D. Barmparis
Dr. Maria E. Marketou
Prof. Dr. Giorgos P. Tsironis
Guest Editors

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Keywords

machine learning
healthcare
wearable technology
remote sensing
smart sensors
personal health advisor
medical decision making
early diagnosis
therapy

Published Papers (7 papers)

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Research

18 pages, 3381 KiB

Open AccessArticle

Effect of Tryptic Digestion on Sensitivity and Specificity in MALDI-TOF-Based Molecular Diagnostics through Machine Learning

by Sumon Sarkar, Abigail Squire, Hanin Diab, Md. Kaisar Rahman, Angela Perdomo, Babafela Awosile, Alexandra Calle and Jonathan Thompson

Sensors 2023, 23(19), 8042; https://doi.org/10.3390/s23198042 - 23 Sep 2023

Viewed by 2345

Abstract

The digestion of protein into peptide fragments reduces the size and complexity of protein molecules. Peptide fragments can be analyzed with higher sensitivity (often > 10² fold) and resolution using MALDI-TOF mass spectrometers, leading to improved pattern recognition by common machine learning algorithms. In turn, enhanced sensitivity and specificity for bacterial sorting and/or disease diagnosis may be obtained. To test this hypothesis, four exemplar case studies have been pursued in which samples are sorted into dichotomous groups by machine learning (ML) software based on MALDI-TOF spectra. Samples were analyzed in ‘intact’ mode in which the proteins present in the sample were not digested with protease prior to MALDI-TOF analysis and separately after the standard overnight tryptic digestion of the same samples. For each case, sensitivity (sens), specificity (spc), and the Youdin index (J) were used to assess the ML model performance. The proteolytic digestion of samples prior to MALDI-TOF analysis substantially enhanced the sensitivity and specificity of dichotomous sorting. Two exceptions were when substantial differences in chemical composition between the samples were present and, in such cases, both ‘intact’ and ‘digested’ protocols performed similarly. The results suggest proteolytic digestion prior to analysis can improve sorting in MALDI/ML-based workflows and may enable improved biomarker discovery. However, when samples are easily distinguishable protein digestion is not necessary to obtain useful diagnostic results. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Sensing and Healthcare)

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20 pages, 3729 KiB

Open AccessArticle

Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19

by Imran Shafi, Muhammad Sajad, Anum Fatima, Daniel Gavilanes Aray, Vivían Lipari, Isabel de la Torre Diez and Imran Ashraf

Sensors 2023, 23(15), 6837; https://doi.org/10.3390/s23156837 - 31 Jul 2023

Cited by 3 | Viewed by 1504

Abstract

With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Sensing and Healthcare)

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17 pages, 967 KiB

Open AccessArticle

Machine Learning Models for Weight-Bearing Activity Type Recognition Based on Accelerometry in Postmenopausal Women

by Cameron J. Huggins, Rebecca Clarke, Daniel Abasolo, Erreka Gil-Rey, Jonathan H. Tobias, Kevin Deere and Sarah J. Allison

Sensors 2022, 22(23), 9176; https://doi.org/10.3390/s22239176 - 25 Nov 2022

Cited by 2 | Viewed by 1516

Abstract

Hip-worn triaxial accelerometers are widely used to assess physical activity in terms of energy expenditure. Methods for classification in terms of different types of activity of relevance to the skeleton in populations at risk of osteoporosis are not currently available. This publication aims to assess the accuracy of four machine learning models on binary (standing and walking) and tertiary (standing, walking, and jogging) classification tasks in postmenopausal women. Eighty women performed a shuttle test on an indoor track, of which thirty performed the same test on an indoor treadmill. The raw accelerometer data were pre-processed, converted into eighteen different features and then combined into nine unique feature sets. The four machine learning models were evaluated using three different validation methods. Using the leave-one-out validation method, the highest average accuracy for the binary classification model, 99.61%, was produced by a k-NN Manhattan classifier using a basic statistical feature set. For the tertiary classification model, the highest average accuracy, 94.04%, was produced by a k-NN Manhattan classifier using a feature set that included all 18 features. The methods and classifiers within this study can be applied to accelerometer data to more accurately characterize weight-bearing activity which are important to skeletal health. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Sensing and Healthcare)

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17 pages, 827 KiB

Open AccessArticle

Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

by Elias Dritsas and Maria Trigka

Sensors 2022, 22(14), 5365; https://doi.org/10.3390/s22145365 - 18 Jul 2022

Cited by 16 | Viewed by 2640

Abstract

Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Sensing and Healthcare)

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18 pages, 828 KiB

Open AccessArticle

Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

by Elias Dritsas and Maria Trigka

Sensors 2022, 22(14), 5304; https://doi.org/10.3390/s22145304 - 15 Jul 2022

Cited by 38 | Viewed by 4148

Abstract

Diabetes mellitus is a chronic condition characterized by a disturbance in the metabolism of carbohydrates, fats and proteins. The most characteristic disorder in all forms of diabetes is hyperglycemia, i.e., elevated blood sugar levels. The modern way of life has significantly increased the incidence of diabetes. Therefore, early diagnosis of the disease is a necessity. Machine Learning (ML) has gained great popularity among healthcare providers and physicians due to its high potential in developing efficient tools for risk prediction, prognosis, treatment and the management of various conditions. In this study, a supervised learning methodology is described that aims to create risk prediction tools with high efficiency for type 2 diabetes occurrence. A features analysis is conducted to evaluate their importance and explore their association with diabetes. These features are the most common symptoms that often develop slowly with diabetes, and they are utilized to train and test several ML models. Various ML models are evaluated in terms of the Precision, Recall, F-Measure, Accuracy and AUC metrics and compared under 10-fold cross-validation and data splitting. Both validation methods highlighted Random Forest and K-NN as the best performing models in comparison to the other models. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Sensing and Healthcare)

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13 pages, 624 KiB

Open AccessArticle

Stroke Risk Prediction with Machine Learning Techniques

by Elias Dritsas and Maria Trigka

Sensors 2022, 22(13), 4670; https://doi.org/10.3390/s22134670 - 21 Jun 2022

Cited by 82 | Viewed by 11364

Abstract

A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Sensing and Healthcare)

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24 pages, 13735 KiB

Open AccessArticle

Preeminently Robust Neural PPG Denoiser

by Ju Hyeok Kwon, So Eui Kim, Na Hye Kim, Eui Chul Lee and Jee Hang Lee

Sensors 2022, 22(6), 2082; https://doi.org/10.3390/s22062082 - 8 Mar 2022

Cited by 7 | Viewed by 2810

Abstract

Photoplethysmography (PPG) is a simple and cost-efficient technique that effectively measures cardiovascular response by detecting blood volume changes in a noninvasive manner. A practical challenge in the use of PPGs in real-world applications is noise reduction. PPG signals are likely to be compromised by various types of noise, such as scattering or motion artifacts, and removing such compounding noises using a monotonous method is not easy. To this end, this paper proposes a neural PPG denoiser that can robustly remove multiple types of noise from a PPG signal. By casting the noise reduction problem into a signal restoration approach, we aim to achieve a solid performance in the reduction of different noise types using a single neural denoiser built upon transformer-based deep generative models. Using this proposed method, we conducted the experiments on the noise reduction of a PPG signal synthetically contaminated with five types of noise. Following this, we performed a comparative study using six different noise reduction algorithms, each of which is known to be the best model for each noise. Evaluation results of the peak signal-to-noise ratio (PSNR) show that the neural PPG denoiser is superior in three out of five noise types to the performance of conventional noise reduction algorithms. The salt-and-pepper noise type showed the best performance, with the PSNR of the neural PPG denoiser being 36.6080, and the PSNRs of the other methods were 19.8160 and 32.8234. The Poisson noise type performed the worst, showing a PSNR of 33.0090; the PSNRs of other methods were 35.1822 and 33.4795, respectively. Thereafter, an experiment to recover a signal synthesized with two or more of the five noise types was conducted. When the number of mixed noises was two, three, four, and five, the PSNRs were 29.2759, 27.8759, 26.5608, and 25.9402, respectively. Finally, an experiment to recover motion artifacts was also conducted. The synthesized motion artifact signal was created by synthesizing only a certain ratio of the total signal length. As a result of the motion artifact signal restoration, the PSNRs were 25.2872, 22.8240, 21.2901, and 19.9577 at 30%, 50%, 70%, and 90% motion artifact ratios, respectively. In the three experiments conducted, the neural PPG denoiser showed that various types of noise were effectively removed. This proposal contributes to the universal denoising of continuous PPG signals and can be further expanded to denoise continuous signals in the general domain. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Sensing and Healthcare)

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