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Sensors and Biomedical Signal Processing for Patient Monitoring

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

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 48990

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Special Issue Editors

Prof. Dr. Massimo Mischi

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Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
Interests: biomedical signal processing; sensors; image processing

Prof. Luca Mainardi

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Electronic, Information and Bioengineering Dept, Politecnico di Milano, Milan, Italy
Interests: analysis of biological systems; bioengineering; e-health

Prof. Dr. Edward Sazonov

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Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA
Interests: biomedical signal processing; pattern recognition; wearable sensors
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Prof. Dr. G. Mihaela Neagu

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Department of Applied Electronics and Information Engineering, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: brain computer interface; artificial intelligence for medical diagnosis; biomedical signal and image processing; fetal monitoring
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Prof. Dr. Wei Chen

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School of Information Science and Technology, Fudan University, Shanghai 200433, China
Interests: medical monitoring system; patient health monitoring; neonatal monitoring; brain activity monitoring; smart sleep; smart rehabilitation system; wireless body area networks Photo:
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Dr. Elisabetta Peri

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Electrical Engineering Dept, Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: biomedical signal processing; electrophysiological monitoring; non-invasive patient monitoring

Prof. Brian Telfer

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Human Health and Performance Systems Group, MIT Lincoln Laboratory, Boston, USA
Interests: wearable sensors and systems for human health and performance; physiological, cognitive, and medical monitoring; biosignal and image processing; machine learning

Prof. Dr. Bart Vanrumste

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E-MEDIA, STADIUS, Department of Electrical Engineering (ESAT), Campus Group T, KU Leuven, 3000 Leuven, Belgium
Interests: biomedical signal analysis; blind source separation; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Healthcare deployment will increasingly take advantage of unobtrusive sensing, supported by (ultra)low-power technology, wireless communication, signal processing, and machine learning to expand in the direction of extramural patient montoring. New sensing solutions are emerging that provide unprecedented possibilities for timely detection of diseases, leading to less invasive intervention and improved patient outcome, paralleled by reduced hospitalization and associated costs. Extramural and especially ambulatory monitoring are hampered by the presence of artifacts and interferences in the recorded signals, which are dominant in everyday-life scenarios. Robust monitoring requires joint development of sensing technology and signal processing algorithms to extract the relevant diagnostic information, also exploting multimodal and/or multichannel recording for accurate signal interpretation.

The present Special Issue will address technological and methodological advances in the field of patient monitoring. It welcomes original contributions that focus on novel sensor technologies and digital signal processing that can foster extramural and ambulatory patient monitoring. Reviews focused on the latest achievements of scientific research and emerging techniques are also welcome.

Prof. Dr. Massimo Mischi
Prof. Luca Mainardi
Prof. Dr. Edward Sazonov
Prof. G. Mihaela Neagu
Prof. Dr. Wei Chen
Dr. Elisabetta Peri
Prof. Brian Telfer
Prof. Dr. Bart Vanrumste
Guest Editors

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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

patient monitoring
biomedical signal processing
multimodal sensing
unobtrusive sensing
ambulatory monitoring
m-health
blockchain for medical applications
wearable sensors
artifact removal
denoising
multichannel signal processing
compressed sensing
blind source separation
machine learning

Published Papers (14 papers)

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Research

14 pages, 1451 KiB

Open AccessArticle

The Effect of Signal Duration on the Classification of Heart Sounds: A Deep Learning Approach

by Xinqi Bao, Yujia Xu and Ernest Nlandu Kamavuako

Sensors 2022, 22(6), 2261; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062261 - 15 Mar 2022

Cited by 10 | Viewed by 2885

Abstract

Deep learning techniques are the future trend for designing heart sound classification methods, making conventional heart sound segmentation dispensable. However, despite using fixed signal duration for training, no study has assessed its effect on the final performance in detail. Therefore, this study aims at analysing the duration effect on the commonly used deep learning methods to provide insight for future studies in data processing, classifier, and feature selection. The results of this study revealed that (1) very short heart sound signal duration (1 s) weakens the performance of Recurrent Neural Networks (RNNs), whereas no apparent decrease in the tested Convolutional Neural Network (CNN) model was found. (2) RNN outperformed CNN using Mel-frequency cepstrum coefficients (MFCCs) as features. There was no difference between RNN models (LSTM, BiLSTM, GRU, or BiGRU). (3) Adding dynamic information (∆ and ∆²MFCCs) of the heart sound as a feature did not improve the RNNs’ performance, and the improvement on CNN was also minimal (≤2.5% in MAcc). The findings provided a theoretical basis for further heart sound classification using deep learning techniques when selecting the input length. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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16 pages, 3392 KiB

Open AccessArticle

Microwave Hydration Monitoring: System Assessment Using Fasting Volunteers

by Brendon C. Besler and Elise C. Fear

Sensors 2021, 21(21), 6949; https://0-doi-org.brum.beds.ac.uk/10.3390/s21216949 - 20 Oct 2021

Cited by 6 | Viewed by 1768

Abstract

Hydration is an important aspect of human health, as water is a critical nutrient used in many physiological processes. However, there is currently no clinical gold standard for non-invasively assessing hydration status. Recent work has suggested that permittivity in the microwave frequency range provides a physiologically meaningful metric for hydration monitoring. Using a simple time of flight technique for estimating permittivity, this study investigates microwave-based hydration assessment using a population of volunteers fasting during Ramadan. Volunteers are measured throughout the day while fasting during Ramadan and while not fasting after Ramadan. Comparing the estimated changes in permittivity to changes in weight and the time s fails to establish a clear relationship between permittivity and hydration. Assessing the subtle changes in hydration found in a population of sedentary, healthy adults proves difficult and more work is required to determine approaches suitable for tracking subtle changes in hydration over time with microwave-based hydration assessment techniques. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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15 pages, 19782 KiB

Open AccessArticle

Automatic Measurement of the Carotid Blood Flow for Wearable Sensors: A Pilot Study

by Riccardo Matera and Stefano Ricci

Sensors 2021, 21(17), 5877; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175877 - 31 Aug 2021

Cited by 3 | Viewed by 2548

Abstract

The assessment of the velocity of blood flowing in the carotid, in modern clinical practice, represents an important exam performed both in emergency situations and as part of scheduled screenings. It is typically performed by an expert sonographer who operates a complex and costly clinical echograph. Unfortunately, in developing countries, in rural areas, and even in crowded modern cities, the access to this exam can be limited by the lack of suitable personnel and ultrasound equipment. The recent availability of low-cost, handheld devices has contributed to solving part of the problem, but a wide access to the exam is still hampered by the lack of expert sonographers. In this work, an automated procedure is presented with the hope that, in the near future, it can be integrated into a low-cost, handheld instrument that is also suitable for self-measurement, for example, as can be done today with the finger oximeter. The operator should only place the probe on the neck, transversally with respect to the common tract of the carotid. The system, in real-time, automatically locates the vessel lumen, places the sample volume, and performs an angle-corrected velocity measurement of the common carotid artery peak velocity. In this study, the method was implemented for testing on the ULA-OP 256 scanner. Experiments on flow phantoms and volunteers show a performance in sample volume placement similar to that achieved by expert operators, and an accuracy and repeatability of 3.2% and 4.5%, respectively. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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19 pages, 7532 KiB

Open AccessArticle

Modulation of Pulse Propagation and Blood Flow via Cuff Inflation—New Distal Insights

by Laura I. Bogatu, Simona Turco, Massimo Mischi, Lars Schmitt, Pierre Woerlee, Erik Bresch, Gerrit J. Noordergraaf, Igor Paulussen, Arthur Bouwman, Hendrikus H. M. Korsten and Jens Muehlsteff

Sensors 2021, 21(16), 5593; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165593 - 19 Aug 2021

Cited by 3 | Viewed by 2019

Abstract

In standard critical care practice, cuff sphygmomanometry is widely used for intermittent blood pressure (BP) measurements. However, cuff devices offer ample possibility of modulating blood flow and pulse propagation along the artery. We explore underutilized arrangements of sensors involving cuff devices which could be of use in critical care to reveal additional information on compensatory mechanisms. In our previous work, we analyzed the response of the vasculature to occlusion perturbations by means of observations obtained non-invasively. In this study, our aim is to (1) acquire additional insights by means of invasive measurements and (2) based on these insights, further develop cuff-based measurement strategies. Invasive BP experimental data is collected downstream from the cuff in two patients monitored in the OR. It is found that highly dynamic processes occur in the distal arm during cuff inflation. Mean arterial pressure increases in the distal artery by 20 mmHg, leading to a decrease in pulse transit time by 20 ms. Previous characterizations neglected such distal vasculature effects. A model is developed to reproduce the observed behaviors and to provide a possible explanation of the factors that influence the distal arm mechanisms. We apply the new findings to further develop measurement strategies aimed at acquiring information on pulse arrival time vs. BP calibration, artery compliance, peripheral resistance, artery-vein interaction. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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23 pages, 2776 KiB

Open AccessArticle

Dedicated Algorithm for Unobtrusive Fetal Heart Rate Monitoring Using Multiple Dry Electrodes

by Alessandra Galli, Elisabetta Peri, Yijing Zhang, Rik Vullings, Myrthe van der Ven, Giada Giorgi, Sotir Ouzounov, Pieter J. A. Harpe and Massimo Mischi

Sensors 2021, 21(13), 4298; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134298 - 23 Jun 2021

Cited by 8 | Viewed by 2772

Abstract

Multi-channel measurements from the maternal abdomen acquired by means of dry electrodes can be employed to promote long-term monitoring of fetal heart rate (fHR). The signals acquired with this type of electrode have a lower signal-to-noise ratio and different artifacts compared to signals acquired with conventional wet electrodes. Therefore, starting from the benchmark algorithm with the best performance for fHR estimation proposed by Varanini et al., we propose a new method specifically designed to remove artifacts typical of dry-electrode recordings. To test the algorithm, experimental textile electrodes were employed that produce artifacts typical of dry and capacitive electrodes. The proposed solution is based on a hybrid (hardware and software) pre-processing step designed specifically to remove the disturbing component typical of signals acquired with these electrodes (triboelectricity artifacts and amplitude modulations). The following main processing steps consist of the removal of the maternal ECG by blind source separation, the enhancement of the fetal ECG and identification of the fetal QRS complexes. Main processing is designed to be robust to the high-amplitude motion artifacts that corrupt the acquisition. The obtained denoising system was compared with the benchmark algorithm both on semi-simulated and on real data. The performance, quantified by means of sensitivity, F1-score and root-mean-square error metrics, outperforms the performance obtained with the original method available in the literature. This result proves that the design of a dedicated processing system based on the signal characteristics is necessary for reliable and accurate estimation of the fHR using dry, textile electrodes. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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14 pages, 2362 KiB

Open AccessArticle

Recognizing Manual Activities Using Wearable Inertial Measurement Units: Clinical Application for Outcome Measurement

by Ghady El Khoury, Massimo Penta, Olivier Barbier, Xavier Libouton, Jean-Louis Thonnard and Philippe Lefèvre

Sensors 2021, 21(9), 3245; https://0-doi-org.brum.beds.ac.uk/10.3390/s21093245 - 07 May 2021

Cited by 1 | Viewed by 1811

Abstract

The ability to monitor activities of daily living in the natural environments of patients could become a valuable tool for various clinical applications. In this paper, we show that a simple algorithm is capable of classifying manual activities of daily living (ADL) into categories using data from wrist- and finger-worn sensors. Six participants without pathology of the upper limb performed 14 ADL. Gyroscope signals were used to analyze the angular velocity pattern for each activity. The elaboration of the algorithm was based on the examination of the activity at the different levels (hand, fingers and wrist) and the relationship between them for the duration of the activity. A leave-one-out cross-validation was used to validate our algorithm. The algorithm allowed the classification of manual activities into five different categories through three consecutive steps, based on hands ratio (i.e., activity of one or both hands) and fingers-to-wrist ratio (i.e., finger movement independently of the wrist). On average, the algorithm made the correct classification in 87.4% of cases. The proposed algorithm has a high overall accuracy, yet its computational complexity is very low as it involves only averages and ratios. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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24 pages, 836 KiB

Open AccessArticle

A Blockchain-Enabled Framework for mHealth Systems

by Dragos Daniel Taralunga and Bogdan Cristian Florea

Sensors 2021, 21(8), 2828; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082828 - 16 Apr 2021

Cited by 23 | Viewed by 5495

Abstract

Presently modern technology makes a significant contribution to the transition from traditional healthcare to smart healthcare systems. Mobile health (mHealth) uses advances in wearable sensors, telecommunications and the Internet of Things (IoT) to propose a new healthcare concept centered on the patient. Patients’ real-time remote continuous health monitoring, remote diagnosis, treatment, and therapy is possible in an mHealth system. However, major limitations include the transparency, security, and privacy of health data. One possible solution to this is the use of blockchain technologies, which have found numerous applications in the healthcare domain mainly due to theirs features such as decentralization (no central authority is needed), immutability, traceability, and transparency. We propose an mHealth system that uses a private blockchain based on the Ethereum platform, where wearable sensors can communicate with a smart device (a smartphone or smart tablet) that uses a peer-to-peer hypermedia protocol, the InterPlanetary File System (IPFS), for the distributed storage of health-related data. Smart contracts are used to create data queries, to access patient data by healthcare providers, to record diagnostic, treatment, and therapy, and to send alerts to patients and medical professionals. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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18 pages, 579 KiB

Open AccessArticle

Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank

by James R. Williamson, Brian Telfer, Riley Mullany and Karl E. Friedl

Sensors 2021, 21(6), 2047; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062047 - 14 Mar 2021

Cited by 20 | Viewed by 4127

Abstract

Parkinson’s disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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17 pages, 2159 KiB

Open AccessArticle

Objective Quantification of In-Hospital Patient Mobilization after Cardiac Surgery Using Accelerometers: Selection, Use, and Analysis

by Frank R. Halfwerk, Jeroen H. L. van Haaren, Randy Klaassen, Robby W. van Delden, Peter H. Veltink and Jan G. Grandjean

Sensors 2021, 21(6), 1979; https://0-doi-org.brum.beds.ac.uk/10.3390/s21061979 - 11 Mar 2021

Cited by 7 | Viewed by 3500

Abstract

Cardiac surgery patients infrequently mobilize during their hospital stay. It is unclear for patients why mobilization is important, and exact progress of mobilization activities is not available. The aim of this study was to select and evaluate accelerometers for objective qualification of in-hospital mobilization after cardiac surgery. Six static and dynamic patient activities were defined to measure patient mobilization during the postoperative hospital stay. Device requirements were formulated, and the available devices reviewed. A triaxial accelerometer (AX3, Axivity) was selected for a clinical pilot in a heart surgery ward and placed on both the upper arm and upper leg. An artificial neural network algorithm was applied to classify lying in bed, sitting in a chair, standing, walking, cycling on an exercise bike, and walking the stairs. The primary endpoint was the daily amount of each activity performed between 7 a.m. and 11 p.m. The secondary endpoints were length of intensive care unit stay and surgical ward stay. A subgroup analysis for male and female patients was planned. In total, 29 patients were classified after cardiac surgery with an intensive care unit stay of 1 (1 to 2) night and surgical ward stay of 5 (3 to 6) nights. Patients spent 41 (20 to 62) min less time in bed for each consecutive hospital day, as determined by a mixed-model analysis (p < 0.001). Standing, walking, and walking the stairs increased during the hospital stay. No differences between men (n = 22) and women (n = 7) were observed for all endpoints in this study. The approach presented in this study is applicable for measuring all six activities and for monitoring postoperative recovery of cardiac surgery patients. A next step is to provide feedback to patients and healthcare professionals, to speed up recovery. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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22 pages, 2155 KiB

Open AccessArticle

Optimization and Validation of a Classification Algorithm for Assessment of Physical Activity in Hospitalized Patients

by Hanneke C. van Dijk-Huisman, Wouter Bijnens, Rachel Senden, Johannes M. N. Essers, Kenneth Meijer, Jos Aarts and Antoine F. Lenssen

Sensors 2021, 21(5), 1652; https://0-doi-org.brum.beds.ac.uk/10.3390/s21051652 - 27 Feb 2021

Cited by 7 | Viewed by 3255

Abstract

Low amounts of physical activity (PA) and prolonged periods of sedentary activity are common in hospitalized patients. Objective PA monitoring is needed to prevent the negative effects of inactivity, but a suitable algorithm is lacking. The aim of this study is to optimize and validate a classification algorithm that discriminates between sedentary, standing, and dynamic activities, and records postural transitions in hospitalized patients under free-living conditions. Optimization and validation in comparison to video analysis were performed in orthopedic and acutely hospitalized elderly patients with an accelerometer worn on the upper leg. Data segmentation window size (WS), amount of PA threshold (PA Th) and sensor orientation threshold (SO Th) were optimized in 25 patients, validation was performed in another 25. Sensitivity, specificity, accuracy, and (absolute) percentage error were used to assess the algorithm’s performance. Optimization resulted in the best performance with parameter settings: WS 4 s, PA Th 4.3 counts per second, SO Th 0.8 g. Validation showed that all activities were classified within acceptable limits (>80% sensitivity, specificity and accuracy, ±10% error), except for the classification of standing activity. As patients need to increase their PA and interrupt sedentary behavior, the algorithm is suitable for classifying PA in hospitalized patients. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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15 pages, 1287 KiB

Open AccessArticle

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram

by Elisabetta Peri, Lin Xu, Christian Ciccarelli, Nele L. Vandenbussche, Hongji Xu, Xi Long, Sebastiaan Overeem, Johannes P. van Dijk and Massimo Mischi

Sensors 2021, 21(2), 573; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020573 - 15 Jan 2021

Cited by 9 | Viewed by 3762

Abstract

A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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27 pages, 6756 KiB

Open AccessArticle

Bioimpedance Sensor and Methodology for Acute Pain Monitoring

by Mihaela Ghita, Martine Neckebroek, Jasper Juchem, Dana Copot, Cristina I. Muresan and Clara M. Ionescu

Sensors 2020, 20(23), 6765; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236765 - 26 Nov 2020

Cited by 26 | Viewed by 3678

Abstract

The paper aims to revive the interest in bioimpedance analysis for pain studies in communicating and non-communicating (anesthetized) individuals for monitoring purpose. The plea for exploitation of full potential offered by the complex (bio)impedance measurement is emphasized through theoretical and experimental analysis. A non-invasive, low-cost reliable sensor to measure skin impedance is designed with off-the-shelf components. This is a second generation prototype for pain detection, quantification, and modeling, with the objective to be used in fully anesthetized patients undergoing surgery. The 2D and 3D time–frequency, multi-frequency evaluation of impedance data is based on broadly available signal processing tools. Furthermore, fractional-order impedance models are implied to provide an indication of change in tissue dynamics correlated with absence/presence of nociceptor stimulation. The unique features of the proposed sensor enhancements are described and illustrated here based on mechanical and thermal tests and further reinforced with previous studies from our first generation prototype. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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21 pages, 1727 KiB

Open AccessArticle

Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology

by Ahmed Youssef Ali Amer, Femke Wouters, Julie Vranken, Dianne de Korte-de Boer, Valérie Smit-Fun, Patrick Duflot, Marie-Hélène Beaupain, Pieter Vandervoort, Stijn Luca, Jean-Marie Aerts and Bart Vanrumste

Sensors 2020, 20(22), 6593; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226593 - 18 Nov 2020

Cited by 15 | Viewed by 5374

Abstract

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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16 pages, 1571 KiB

Open AccessArticle

Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography

by Lin Xu, Elisabetta Peri, Rik Vullings, Chiara Rabotti, Johannes P. Van Dijk and Massimo Mischi

Sensors 2020, 20(17), 4890; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174890 - 29 Aug 2020

Cited by 11 | Viewed by 3856

Abstract

Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e.,

{KR}_{2}

and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method. Full article

(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)

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