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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 6285

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Special Issue Editors

Prof. Dr. Yoshitaka Oku

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Guest Editor

Department of Physiology, Hyogo Medical University, Nishinomiya 663-8501, Japan
Interests: control of breathing, especially, rhythm generation, pattern formation, and coordination between swallowing and breathing

Prof. Dr. Andrea Aliverti

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Guest Editor

Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
Interests: bioengineering of the respiratory system; physiological measurements; biomedical instrumentation and sensors and functional lung imaging

Special Issue Information

Dear Colleagues,

We would like to cordially invite you to participate in a Special Issue on “Sensors for Breathing Monitoring”. Breathing monitoring is essential in clinical settings to detect apnea, hypopnea, and other respiratory abnormalities. Further, respiratory fluctuation contains valuable information that can be used in clinical practice for diagnosis, emotion recognition, and mental conditioning. The recent advancement of sensor technology in combination with machine learning and information theory-based techniques has enabled us to extract such hidden information from respiratory fluctuation and translate it into usable forms.

Various sensors for breathing monitoring have been developed during recent decades that can be classified as 1. airflow-based sensors (e.g., pneumotachograph, thermistor, capnometer, acoustic sensors, etc.), 2. chest wall motion-based sensors (e.g., magnetometer, inductive plethysmography, impedance pneumography, piezoelectric sensors, accelerometer, optical sensors, radio frequency-based methods, etc.), and 3. methods based on respiratory modulation on other physiological signals such as electrocardiograms, arterial pulse wave transit time, photoplethysmograms (PPG), and imaging PPG.

One of the key issues is to figure out an appropriate method for a specific purpose. To accomplish this, we must know the characteristics (accuracy, stability, and restrictions) of these sensors on the one hand and the requirements to meet the purpose on the other hand.

Contributions to this Special Issue may include, but are not limited to:

Novel sensing techniques for breathing monitoring;
Practical sensor implementations for diagnosis, emotion recognition, and mental conditioning;
New insights into breathing complexity which provide methods to extract useful information.

Prof. Dr. Yoshitaka Oku
Prof. Dr. Andrea Aliverti
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.

Published Papers (6 papers)

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Research

10 pages, 720 KiB

Open AccessArticle

In-Laboratory Polysomnography Worsens Obstructive Sleep Apnea by Changing Body Position Compared to Home Testing

by Raquel Chartuni Pereira Teixeira and Michel Burihan Cahali

Sensors 2024, 24(9), 2803; https://doi.org/10.3390/s24092803 (registering DOI) - 27 Apr 2024

Abstract

(1) Background: Home sleep apnea testing, known as polysomnography type 3 (PSG3), underestimates respiratory events in comparison with in-laboratory polysomnography type 1 (PSG1). Without head electrodes for scoring sleep and arousal, in a home environment, patients feel unfettered and move their bodies more naturally. Adopting a natural position may decrease obstructive sleep apnea (OSA) severity in PSG3, independently of missing hypopneas associated with arousals. (2) Methods: Patients with suspected OSA performed PSG1 and PSG3 in a randomized sequence. We performed an additional analysis, called reduced polysomnography, in which we blindly reassessed all PSG1 tests to remove electroencephalographic electrodes, electrooculogram, and surface electromyography data to estimate the impact of not scoring sleep and arousal-based hypopneas on the test results. A difference of 15 or more in the apnea–hypopnea index (AHI) between tests was deemed clinically relevant. We compared the group of patients with and without clinically relevant differences between lab and home tests (3) Results: As expected, by not scoring sleep, there was a decrease in OSA severity in the lab test, similar to the home test results. The group of patients with clinically relevant differences between lab and home tests presented more severe OSA in the lab compared to the other group (mean AHI, 42.5 vs. 20.2 events/h, p = 0.002), and this difference disappeared in the home test. There was no difference between groups in the shift of OSA severity by abolishing sleep scoring in the lab. However, by comparing lab and home tests, there were greater variations in supine AHI and time spent in the supine position in the group with a clinically relevant difference, either with or without scoring sleep, showing an impact of the site of the test on body position during sleep. These variations presented as a marked increase or decrease in supine outcomes according to the site of the test, with no particular trend. (4) Conclusions: In-lab polysomnography may artificially increase OSA severity in a subset of patients by inducing marked changes in body position compared to home tests. The location of the sleep test seems to interfere with the evaluation of patients with more severe OSA. Full article

(This article belongs to the Special Issue Sensors for Breathing Monitoring)

24 pages, 7987 KiB

Open AccessArticle

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

by Alberto Gudiño-Ochoa, Julio Alberto García-Rodríguez, Raquel Ochoa-Ornelas, Jorge Ivan Cuevas-Chávez and Daniel Alejandro Sánchez-Arias

Sensors 2024, 24(4), 1294; https://doi.org/10.3390/s24041294 - 17 Feb 2024

Viewed by 1459

Abstract

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm’s achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection. Full article

(This article belongs to the Special Issue Sensors for Breathing Monitoring)

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16 pages, 1832 KiB

Open AccessArticle

Innovative Predictive Approach towards a Personalized Oxygen Dosing System

by Heribert Pascual-Saldaña, Xavi Masip-Bruin, Adrián Asensio, Albert Alonso and Isabel Blanco

Sensors 2024, 24(3), 764; https://doi.org/10.3390/s24030764 - 24 Jan 2024

Viewed by 606

Abstract

Despite the large impact chronic obstructive pulmonary disease (COPD) that has on the population, the implementation of new technologies for diagnosis and treatment remains limited. Current practices in ambulatory oxygen therapy used in COPD rely on fixed doses overlooking the diverse activities which patients engage in. To address this challenge, we propose a software architecture aimed at delivering patient-personalized edge-based artificial intelligence (AI)-assisted models that are built upon data collected from patients’ previous experiences along with an evaluation function. The main objectives reside in proactively administering precise oxygen dosages in real time to the patient (the edge), leveraging individual patient data, previous experiences, and actual activity levels, thereby representing a substantial advancement over conventional oxygen dosing. Through a pilot test using vital sign data from a cohort of five patients, the limitations of a one-size-fits-all approach are demonstrated, thus highlighting the need for personalized treatment strategies. This study underscores the importance of adopting advanced technological approaches for ambulatory oxygen therapy. Full article

(This article belongs to the Special Issue Sensors for Breathing Monitoring)

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17 pages, 3296 KiB

Open AccessArticle

Efficacy of Marker-Based Motion Capture for Respiratory Cycle Measurement: A Comparison with Spirometry

by Natalia D. Shamantseva, Tatiana A. Klishkovskaia, Sergey S. Ananyev, Andrey Y. Aksenov and Tatiana R. Moshonkina

Sensors 2023, 23(24), 9736; https://doi.org/10.3390/s23249736 - 10 Dec 2023

Viewed by 757

Abstract

Respiratory rate monitoring is fundamental in clinical settings, and the accuracy of measurement methods is critical. This study aimed to develop and validate methods for assessing respiratory rate and the duration leof respiratory cycle phases in different body positions using optoelectronic plethysmography (OEP) based on a motion capture video system. Two analysis methods, the summation method and the triangle method were developed. The study focused on determining the optimal number of markers while achieving accuracy in respiratory parameter measurements. The results showed that most analysis methods showed a difference of ≤0.5 breaths per minute, with R² ≥ 0.94 (p < 0.001) compared to spirometry. The best OEP methods for respiratory rate were the abdominal triangles and the sum of abdominal markers in all body positions. The study explored inspiratory and expiratory durations. The research found that 5–9 markers were sufficient to accurately determine respiratory time components in all body positions, reducing the marker requirements compared to previous studies. This interchangeability of OEP methods with standard spirometry demonstrates the potential of non-invasive methods for the simultaneous assessment of body segment movements, center of pressure dynamics, and respiratory movements. Future research is required to improve the clinical applicability of these methods. Full article

(This article belongs to the Special Issue Sensors for Breathing Monitoring)

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29 pages, 4797 KiB

Open AccessArticle

Minimally Invasive Hypoglossal Nerve Stimulator Enabled by ECG Sensor and WPT to Manage Obstructive Sleep Apnea

by Fen Xia, Hanrui Li, Yixi Li, Xing Liu, Yankun Xu, Chaoming Fang, Qiming Hou, Siyu Lin, Zhao Zhang, Jie Yang and Mohamad Sawan

Sensors 2023, 23(21), 8882; https://doi.org/10.3390/s23218882 - 01 Nov 2023

Viewed by 1298

Abstract

A hypoglossal nerve stimulator (HGNS) is an invasive device that is used to treat obstructive sleep apnea (OSA) through electrical stimulation. The conventional implantable HGNS device consists of a stimuli generator, a breathing sensor, and electrodes connected to the hypoglossal nerve via leads. However, this implant is bulky and causes significant trauma. In this paper, we propose a minimally invasive HGNS based on an electrocardiogram (ECG) sensor and wireless power transfer (WPT), consisting of a wearable breathing monitor and an implantable stimulator. The breathing external monitor utilizes an ECG sensor to identify abnormal breathing patterns associated with OSA with 88.68% accuracy, achieved through the utilization of a convolutional neural network (CNN) algorithm. With a skin thickness of 5 mm and a receiving coil diameter of 9 mm, the power conversion efficiency was measured as 31.8%. The implantable device, on the other hand, is composed of a front-end CMOS power management module (PMM), a binary-phase-shift-keying (BPSK)-based data demodulator, and a bipolar biphasic current stimuli generator. The PMM, with a silicon area of 0.06 mm

^{2}

(excluding PADs), demonstrated a power conversion efficiency of 77.5% when operating at a receiving frequency of 2 MHz. Furthermore, it offers three-voltage options (1.2 V, 1.8 V, and 3.1 V). Within the data receiver component, a low-power BPSK demodulator was ingeniously incorporated, consuming only 42

μ

W when supplied with a voltage of 0.7 V. The performance was achieved through the implementation of the self-biased phase-locked-loop (PLL) technique. The stimuli generator delivers biphasic constant currents, providing a 5 bit programmable range spanning from 0 to 2.4 mA. The functionality of the proposed ECG- and WPT-based HGNS was validated, representing a highly promising solution for the effective management of OSA, all while minimizing the trauma and space requirements. Full article

(This article belongs to the Special Issue Sensors for Breathing Monitoring)

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18 pages, 4097 KiB

Open AccessArticle

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach

by Emad Arasteh, Esther S. Veldhoen, Xi Long, Maartje van Poppel, Marjolein van der Linden, Thomas Alderliesten, Joppe Nijman, Robbin de Goederen and Jeroen Dudink

Sensors 2023, 23(18), 7665; https://doi.org/10.3390/s23187665 - 05 Sep 2023

Cited by 1 | Viewed by 1214

Abstract

Unobtrusive monitoring of children’s heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children’s RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM < 6.5% of average HR) and 2.63 breaths per minute (BPM < 7% of average RR). We also achieved a significant Pearson’s correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s. Full article

(This article belongs to the Special Issue Sensors for Breathing Monitoring)

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