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Signals in Health Care
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Signals in Health Care

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 6856

Special Issue Editors

Dr. Manuel Duarte Ortigueira

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Guest Editor
Centre of Technology and Systems-UNINOVA, NOVA School of Science and Technology, NOVA University of Lisbon, Quinta da Torre, 2829-516 Caparica, Portugal
Interests: signal processing; fractional signals and systems; EEG and ECG processing
Special Issues, Collections and Topics in MDPI journals
Dr. Carla M. A. Pinto

E-Mail Website
Guest Editor
Instituto Superior de Engenharia do Porto, Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto, Portugal
Interests: epidemiological and within-host models; dynamical systems; fractional order models
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Arnaldo Guimarães Batista

E-Mail Website
Guest Editor
Center of Technology and Systems-UNINOVA and DEE/Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa Campus da FCT, Quinta da Torre, 2829-516 Caparica, Portugal
Interests: biomedical signal processing, wavelets, uterine electromyography signal processing, time-frequency analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical signals enjoy a privileged importance in our daily life. Disease risk evaluation and diagnostics is often based on the analysis of biomedical signals. Frequently, these diagnostic tools are non-invasive and simple to perform in hospitals and health centres. The acquired signals are then submitted to an array of processing algorithms either online or offline. These algorithms, which are the main core of these systems, undergo a validation process before being merged in commercial systems for health care. Electrocardiography, electroencephalography, electromyography and computerized axial tomography are just examples of signals that are studied, modelled and used to make predictions and suggest treatments.

Therefore, contributions regarding these signals are welcome:

  • Electrocardiography
  • Electroencephalography
  • Evoqued Potentials
  • Sleep Spindles
  • Epilepsy
  • Biomedical Image Processing
  • Brain-Computer Interface
  • Electromyography
  • Neuroprosthetics
  • Sleep Classification
  • Electrogastrography
  • Electroenterography
  • Electrocystography
  • Electrohysterography
  • Anal Sphincter Electromyography
  • Electrocorticography

Prof. Dr. Manuel D. Ortigueira
Prof. Dr. Carla Pinto
Prof. Dr. Arnaldo Guimarães Batista
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. Signals is an international peer-reviewed open access quarterly 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 1000 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

  • Biomedical Signal Processing
  • Biomedical Image Processing
  • Fluid modelling
  • AIDS- and non-AIDS-related tumors modelling

Published Papers (2 papers)

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Research

12 pages, 1584 KiB  
Article
Time-Frequency Analysis of Daily Activities for Fall Detection
by Quoc T. Huynh and Binh Q. Tran
Signals 2021, 2(1), 1-12; https://0-doi-org.brum.beds.ac.uk/10.3390/signals2010001 - 08 Jan 2021
Cited by 2 | Viewed by 2705
Abstract
Fall events in elderly populations often result in serious injury and may lead to long-term disability and/or death. While many fall detection systems have been developed using wearable sensors to distinguish falls from other daily activities, detection sensitivity and specificity decreases when exposed [...] Read more.
Fall events in elderly populations often result in serious injury and may lead to long-term disability and/or death. While many fall detection systems have been developed using wearable sensors to distinguish falls from other daily activities, detection sensitivity and specificity decreases when exposed to more rigorous activities such as running and jumping. This research uses time-frequency analysis of accelerometer-only activity data to develop a strategy for improving fall detection accuracy. In this study, a wireless sensor system (WSS) consisting of a three-axis accelerometer, microprocessor and wireless communications module is used to collect daily activities performed following a script in the laboratory setting. Experiments were conducted on 36 healthy human subjects performing four types of falls (i.e., forward, backward, and left/right sideway falls) as well as normal movements such as standing, walking, stand-to-sit, sit-to-stand, stepping, running and jumping. In total, 1227 different activities were collected and analyzed. The developed algorithm computes the magnitude of three-axis accelerometer data to detect if a critical fall threshold is passed, then analyzes the power spectral density within a critical fall duration window (500 ms) to differentiate fall events from other rigorous activities. Fall events were observed with high energy in the 2–3.5 Hz range and distinct from other rigorous activities such as running (3.5–5.5 Hz) and jumping (1–2 Hz). Preliminary results indicate the power spectral density (PSD)-based algorithm can detect falls with high sensitivity (98.4%) and specificity (98.6%) using lab-based daily activity data. The proposed algorithm has the benefit of improved accuracy over existing time-domain only strategies and multisensor strategies. Full article
(This article belongs to the Special Issue Signals in Health Care)
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21 pages, 1296 KiB  
Article
Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
by Mert Sevil, Mudassir Rashid, Mohammad Reza Askari, Zacharie Maloney, Iman Hajizadeh and Ali Cinar
Signals 2020, 1(2), 188-208; https://0-doi-org.brum.beds.ac.uk/10.3390/signals1020011 - 04 Dec 2020
Cited by 17 | Viewed by 3143
Abstract
Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to [...] Read more.
Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS. Full article
(This article belongs to the Special Issue Signals in Health Care)
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