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Biomedical Sensors: New Technologies, Integration and Signal Processing
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Biomedical Sensors: New Technologies, Integration and Signal Processing

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 3081

Special Issue Editor

Dr. Antonio Fratini

E-Mail Website
Guest Editor
Engineering for Health, College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK
Interests: from wearable medical devices to rehabilitation treatments; from biometric recognition to driver safety

Special Issue Information

Dear Colleagues,

The increased availability of sensors that are capable of monitoring a variety of biomedical variables opens up new avenues in healthcare. Classic biomedical monitoring devices that are integrated into personal devices, in conjunction with new sensor types such as wearables, can provide valuable new insights into person health and lifestyle.

The fusion of information from different technologies can improve diagnostic ability, continuously assess therapies or the effectiveness of rehabilitation, as well as simply contribute to the pursuit of a healthy lifestyle.

This Special Issue is aimed at embracing the wider spectrum of technologies to monitor physiological parameters; it will cover both classical and unconventional techniques, the advanced processing of biomedical signals, as well as data integration for the continuous and pervasive monitoring of personal health.

For this Special Issue, we invite research papers that present novel research on topics including, but not limited to, the following:

  • Advanced sensors for biomedical signals;
  • Wearable or minimally invasive sensing;
  • Smartphone-based sensing applications;
  • Monitoring systems for sport and wellness;
  • Data pre-processing and noise filtering in biosignals;
  • Advanced processing of biomedical signals;
  • Machine learning and deep learning applied to biomedical signals;
  • Multimodal sensing systems for patient monitoring;
  • Sensing in cardiac, respiratory, and physical activity applications;
  • IoT in medical applications;
  • Telemedicine and semi-automatic diagnosis support systems;
  • Patient monitoring during treatment and/or rehabilitation;
  • Techniques and algorithms for advanced personalized medical assessment;
  • Other emerging applications of biomedical signal processing.

The goal of this Special Issue is to highlight the latest developments in the field of biomedical sensors and to assess their potential impacts on healthcare. By bringing together experts in the field, we hope to foster collaboration and advance the research on biomedical sensor technology.

Dr. Antonio Fratini
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. 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

  • biomedical sensors
  • biomedical signal processing
  • health monitoring
  • wearable sensors
  • artificial intelligence
  • machine learning and deep learning applied to biomedical signals
  • multimodal biomedical signal integration
  • IoT for medical applications
  • sport and wellness monitoring

Published Papers (3 papers)

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Research

20 pages, 4794 KiB  
Article
Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals
by Jessica Centracchio, Salvatore Parlato, Daniele Esposito and Emilio Andreozzi
Sensors 2024, 24(5), 1525; https://doi.org/10.3390/s24051525 - 27 Feb 2024
Viewed by 556
Abstract
Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, [...] Read more.
Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, which is not a trivial task. This study proposes a method for an accurate recognition and localization of heart sounds in Forcecardiography (FCG) recordings. FCG is a novel technique able to measure subsonic vibrations and sounds via small force sensors placed onto a subject’s thorax, allowing continuous cardio-respiratory monitoring. In this study, a template-matching technique based on normalized cross-correlation was used to automatically recognize heart sounds in FCG signals recorded from six healthy subjects at rest. Distinct templates were manually selected from each FCG recording and used to separately localize S1 and S2 sounds, as well as S1–S2 pairs. A simultaneously recorded electrocardiography (ECG) trace was used for performance evaluation. The results show that the template matching approach proved capable of separately classifying S1 and S2 sounds in more than 96% of all heartbeats. Linear regression, correlation, and Bland–Altman analyses showed that inter-beat intervals were estimated with high accuracy. Indeed, the estimation error was confined within 10 ms, with negligible impact on heart rate estimation. Heart rate variability (HRV) indices were also computed and turned out to be almost comparable with those obtained from ECG. The preliminary yet encouraging results of this study suggest that the template matching approach based on normalized cross-correlation allows very accurate heart sounds localization and inter-beat intervals estimation. Full article
(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)
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14 pages, 2376 KiB  
Article
Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device
by Maria Gragnaniello, Alessandro Borghese, Vincenzo Romano Marrazzo, Luca Maresca, Giovanni Breglio, Andrea Irace and Michele Riccio
Sensors 2024, 24(3), 828; https://doi.org/10.3390/s24030828 - 26 Jan 2024
Viewed by 868
Abstract
Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for [...] Read more.
Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for continuous monitoring using wearable devices. This paper proposes a single-microcontroller-based system designed for the automatic detection of MI based on the Edge Computing paradigm. Two solutions for MI detection are evaluated, based on Machine Learning (ML) and Deep Learning (DL) techniques. The developed algorithms are based on two different approaches currently available in the literature, and they are optimized for deployment on low-resource hardware. A feasibility assessment of their implementation on a single 32-bit microcontroller with an ARM Cortex-M4 core was examined, and a comparison in terms of accuracy, inference time, and memory usage was detailed. For ML techniques, significant data processing for feature extraction, coupled with a simpler Neural Network (NN) is involved. On the other hand, the second method, based on DL, employs a Spectrogram Analysis for feature extraction and a Convolutional Neural Network (CNN) with a longer inference time and higher memory utilization. Both methods employ the same low power hardware reaching an accuracy of 89.40% and 94.76%, respectively. The final prototype is an energy-efficient system capable of real-time detection of MI without the need to connect to remote servers or the cloud. All processing is performed at the edge, enabling NN inference on the same microcontroller. Full article
(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)
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18 pages, 5074 KiB  
Article
Sensorized T-Shirt with Intarsia-Knitted Conductive Textile Integrated Interconnections: Performance Assessment of Cardiac Measurements during Daily Living Activities
by Abdelakram Hafid, Emanuel Gunnarsson, Alberto Ramos, Kristian Rödby, Farhad Abtahi, Panagiotis D. Bamidis, Antonis Billis, Panagiotis Papachristou and Fernando Seoane
Sensors 2023, 23(22), 9208; https://doi.org/10.3390/s23229208 - 16 Nov 2023
Viewed by 1079
Abstract
The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We [...] Read more.
The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications. Full article
(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)
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