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Bio-Signal Monitoring/ Processing/Modeling in Cardiac, Muscular and Nervous-Systems
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Bio-Signal Monitoring/ Processing/Modeling in Cardiac, Muscular and Nervous-Systems

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

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 4592

Special Issue Editors

Prof. Dr. Dinesh K Kumar

E-Mail Website
Guest Editor
Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Interests: biomedical signal processing; EMG; retina image analysis; thermal imaging; hyperspectral imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Sridhar P. Arjunan

E-Mail Website
Guest Editor
Department of Electronics and Instrumentation Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology (Formerly Known as SRM University), Kattankulathur, India
Interests: physiological signal processing; muscle activity analysis; human movement and gait analysis; EMG signal modelling; multi-spectral analysis; biomedical instrumentation; wearable sensors; machine learning

Special Issue Information

Dear Colleagues,

Biosignals have become a significant aspect of the monitoring of health parameters and can provide early diagnoses of various disorders. The processing of the biosignals, which is conducted through the cardiac, muscular and nervous systems, provides a better insight into the functionality of these systems.

It is important to understand the underlying process of the generation of these signals by modeling their characteristics to achieve better clinical diagnoses of disorders.

Advances in computing technologies are having a significant impact on wearable devices for the continuous monitoring of health parameters. This has led to the progress in data security and data mining techniques.

This Special Issue will provide insights into the latest research studies and outcomes on techniques of modeling and processing biosignals in the cardiac, muscular and nervous systems. These research articles will include the latest advancements in wearable devices for the health monitoring, data mining and data security of health parameters.

Prof. Dr. Dinesh K Kumar
Dr. Sridhar P. Arjunan
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. 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

  • physiological signal processing
  • ECG signal
  • human movement and gait analysis
  • EMG signal modelling
  • neural signal modelliing
  • biomedical instrumentation
  • wearable sensors
  • machine learning
  • data mining for health
  • data security for health

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Published Papers (2 papers)

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Research

20 pages, 2594 KiB  
Article
Different Ventricular Fibrillation Types in Low-Dimensional Latent Spaces
by Carlos Paúl Bernal Oñate, Francisco-Manuel Melgarejo Meseguer, Enrique V. Carrera, Juan José Sánchez Muñoz, Arcadi García Alberola and José Luis Rojo Álvarez
Sensors 2023, 23(5), 2527; https://doi.org/10.3390/s23052527 - 24 Feb 2023
Cited by 4 | Viewed by 1840
Abstract
The causes of ventricular fibrillation (VF) are not yet elucidated, and it has been proposed that different mechanisms might exist. Moreover, conventional analysis methods do not seem to provide time or frequency domain features that allow for recognition of different VF patterns in [...] Read more.
The causes of ventricular fibrillation (VF) are not yet elucidated, and it has been proposed that different mechanisms might exist. Moreover, conventional analysis methods do not seem to provide time or frequency domain features that allow for recognition of different VF patterns in electrode-recorded biopotentials. The present work aims to determine whether low-dimensional latent spaces could exhibit discriminative features for different mechanisms or conditions during VF episodes. For this purpose, manifold learning using autoencoder neural networks was analyzed based on surface ECG recordings. The recordings covered the onset of the VF episode as well as the next 6 min, and comprised an experimental database based on an animal model with five situations, including control, drug intervention (amiodarone, diltiazem, and flecainide), and autonomic nervous system blockade. The results show that latent spaces from unsupervised and supervised learning schemes yielded moderate though quite noticeable separability among the different types of VF according to their type or intervention. In particular, unsupervised schemes reached a multi-class classification accuracy of 66%, while supervised schemes improved the separability of the generated latent spaces, providing a classification accuracy of up to 74%. Thus, we conclude that manifold learning schemes can provide a valuable tool for studying different types of VF while working in low-dimensional latent spaces, as the machine-learning generated features exhibit separability among different VF types. This study confirms that latent variables are better VF descriptors than conventional time or domain features, making this technique useful in current VF research on elucidation of the underlying VF mechanisms. Full article
(This article belongs to the Special Issue Bio-Signal Monitoring/ Processing/Modeling in Cardiac, Muscular and Nervous-Systems)
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14 pages, 3566 KiB  
Article
Baroreflex Sensitivity Assessment Using the Sequence Method with Delayed Signals in End-Stage Renal Disease Patients
by Marisol Martinez-Alanis, Martín Calderón-Juárez, Paola Martínez-García, Gertrudis Hortensia González Gómez, Oscar Infante, Héctor Pérez-Grovas and Claudia Lerma
Sensors 2023, 23(1), 260; https://doi.org/10.3390/s23010260 - 27 Dec 2022
Viewed by 2188
Abstract
Impaired baroreflex sensitivity (BRS) is partially responsible for erratic blood pressure fluctuations in End-Stage Renal Disease (ESRD) patients on chronic hemodialysis (HD), which is related to autonomic nervous dysfunction. The sequence method with delayed signals allows for the measurement of BRS in a [...] Read more.
Impaired baroreflex sensitivity (BRS) is partially responsible for erratic blood pressure fluctuations in End-Stage Renal Disease (ESRD) patients on chronic hemodialysis (HD), which is related to autonomic nervous dysfunction. The sequence method with delayed signals allows for the measurement of BRS in a non-invasive fashion and the investigation of alterations in this physiological feedback system that maintains BP within healthy limits. Our objective was to evaluate the modified delayed signals in the sequence method for BRS assessment in ESRD patients without pharmacological antihypertensive treatment and compare them with those of healthy subjects. We recruited 22 healthy volunteers and 18 patients with ESRD. We recorded continuous BP to obtain a 15-min time series of systolic blood pressure and interbeat intervals during the supine position (SP) and active standing (AS) position. The time series with delays from 0 to 5 heartbeats were used to calculate the BRS, number of data points, number of sequences, and estimation error. The BRS from the ESRD patients was smaller than in healthy subjects (p < 0.05). The BRS estimation with the delayed sequences also increased the number of data points and sequences and decreased the estimation error compared to the original time series. The modified sequence method with delayed signals may be useful for the measurement of baroreflex sensitivity in ESRD patients with a shorter recording time and maintaining an estimation error below 0.01 in both the supine and active standing positions. With this framework, it was corroborated that baroreflex sensitivity in ESRD is decreased when compared with healthy subjects. Full article
(This article belongs to the Special Issue Bio-Signal Monitoring/ Processing/Modeling in Cardiac, Muscular and Nervous-Systems)
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