Signals

Special Issue Editor

Special Issue Information

Dear Colleagues,

Research in biomedical signal processing in combination with powerful machine learning algorithms has enabled the development of methodologies for the prediction, diagnosis, and monitoring of diseases and patients which serve as supportive tools for the (semi-)automatic management of healthcare services. In this SI we invite research papers presenting novel methodologies in biomedical signal processing, including but not limited to EEG, ECG, EMG, other wearable sensors, medical images and their combinations, as well as their application in the prediction, diagnosis and/or monitoring of diseases.

Dr. Iosif Mporas
Guest Editor

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

EEG
ECG
EMG
prediction
diagnosis
healthcare monitoring

Published Papers (2 papers)

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Research

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11 pages, 4599 KiB

Open AccessArticle

Electromyogram in Cigarette Smoking Activity Recognition

by Volkan Senyurek, Masudul Imtiaz, Prajakta Belsare, Stephen Tiffany and Edward Sazonov

Signals 2021, 2(1), 87-97; https://doi.org/10.3390/signals2010008 - 9 Feb 2021

Cited by 1 | Viewed by 3205

Abstract

In this study, information from surface electromyogram (sEMG) signals was used to recognize cigarette smoking. The sEMG signals collected from lower arm were used in two different ways: (1) as an individual predictor of smoking activity and (2) as an additional sensor/modality along with the inertial measurement unit (IMU) to augment recognition performance. A convolutional and a recurrent neural network were utilized to recognize smoking-related hand gestures. The model was developed and evaluated with leave-one-subject-out (LOSO) cross-validation on a dataset from 16 subjects who performed ten activities of daily living including smoking. The results show that smoking detection using only sEMG signal achieved an F1-score of 75% in person-independent cross-validation. The combination of sEMG and IMU improved reached the F1-score of 84%, while IMU alone sensor modality was 81%. The study showed that using only sEMG signals would not provide superior cigarette smoking detection performance relative to IMU signals. However, sEMG improved smoking detection results when combined with IMU signals without using an additional device. Full article

(This article belongs to the Special Issue Biomedical Signal Processing for Healthcare Prediction, Diagnosis and Monitoring Tools)

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Review

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21 pages, 5284 KiB

Open AccessReview

Non-Invasive Fetal Electrocardiogram Monitoring Techniques: Potential and Future Research Opportunities in Smart Textiles

by Geetika Aggarwal and Yang Wei

Signals 2021, 2(3), 392-412; https://doi.org/10.3390/signals2030025 - 29 Jun 2021

Cited by 6 | Viewed by 8034

Abstract

During the pregnancy, fetal electrocardiogram (FECG) is deployed to analyze fetal heart rate (FHR) of the fetus to indicate the growth and health of the fetus to determine any abnormalities and prevent diseases. The fetal electrocardiogram monitoring can be carried out either invasively by placing the electrodes on the scalp of the fetus, involving the skin penetration and the risk of infection, or non-invasively by recording the fetal heart rate signal from the mother’s abdomen through a placement of electrodes deploying portable, wearable devices. Non-invasive fetal electrocardiogram (NIFECG) is an evolving technology in fetal surveillance because of the comfort to the pregnant women and being achieved remotely, specifically in the unprecedented circumstances such as pandemic or COVID-19. Textiles have been at the heart of human technological progress for thousands of years, with textile developments closely tied to key inventions that have shaped societies. The relatively recent invention of smart textiles is set to push boundaries again and has already opened the potential for garments relevant to medicine, and health monitoring. This paper aims to discuss the different technologies and methods used in non-invasive fetal electrocardiogram (NIFECG) monitoring as well as the potential and future research directions of NIFECG in the smart textiles area. Full article

(This article belongs to the Special Issue Biomedical Signal Processing for Healthcare Prediction, Diagnosis and Monitoring Tools)

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