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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: closed (20 February 2025) | Viewed by 25317

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Prof. Dr. Yoshitaka Oku

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Department of Physiology, Hyogo Medical University, Nishinomiya 663-8501, Japan
Interests: control of breathing; especially; rhythm generation; pattern formation; coordination between swallowing and breathing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Andrea Aliverti

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Guest Editor

Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
Interests: bioengineering of the respiratory system; physiological measurements; biomedical instrumentation and sensors and functional lung imaging
Special Issues, Collections and Topics in MDPI journals

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

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Sensors for Breathing Monitoring—2nd Edition in Sensors

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Research

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16 pages, 5306 KiB

Open AccessArticle

Electromagnetic Imaging for Breathing Monitoring

by Ivan Vassilyev and Zhassulan Mendakulov

Sensors 2024, 24(23), 7722; https://doi.org/10.3390/s24237722 - 3 Dec 2024

Viewed by 1316

Abstract

The search for new non-invasive methods of investigating the functioning of human internal organs is an urgent task. One of these methods for assessing the functioning of the human respiratory system is electromagnetic sensing, which is based on a significant difference in the dielectric permittivity of muscle tissue and air. During breathing, when the lungs are filled with air, the dielectric permittivity of the lungs decreases, which leads to a change in the level of the electromagnetic signal passing through the body. The results of experiments on recording changes in the level of electromagnetic radiation passing through the human body performed on an experimental device consisting of eight transmitting and receiving antennas located on opposite sides of the chest have been presented in the article. The possibility of visualizing the measured “pulmonograms” in the form of dynamic two-dimensional images showing the process of filling various parts of the lungs with air has been demonstrated. Full article

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

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15 pages, 1495 KiB

Open AccessArticle

Classification of Breathing Phase and Path with In-Ear Microphones

by Malahat H. K. Mehrban, Jérémie Voix and Rachel E. Bouserhal

Sensors 2024, 24(20), 6679; https://doi.org/10.3390/s24206679 - 17 Oct 2024

Viewed by 1371

Abstract

In recent years, the use of smart in-ear devices (hearables) for health monitoring has gained popularity. Previous research on in-ear breath monitoring with hearables uses signal processing techniques based on peak detection. Such techniques are greatly affected by movement artifacts and other challenging real-world conditions. In this study, we use an existing database of various breathing types captured using an in-ear microphone to classify breathing path and phase. Having a small dataset, we use XGBoost, a simple and fast classifier, to address three different classification challenges. We achieve an accuracy of 86.8% for a binary path classifier, 74.1% for a binary phase classifier, and 67.2% for a four-class path and phase classifier. Our path classifier outperforms existing algorithms in recall and F1, highlighting the reliability of our approach. This work demonstrates the feasibility of the use of hearables in continuous breath monitoring tasks with machine learning. Full article

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

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10 pages, 1334 KiB

Open AccessArticle

Validation of a Textile-Based Wearable Measuring Electrocardiogram and Breathing Frequency for Sleep Apnea Monitoring

by Florent Baty, Dragan Cvetkovic, Maximilian Boesch, Frederik Bauer, Neusa R. Adão Martins, René M. Rossi, Otto D. Schoch, Simon Annaheim and Martin H. Brutsche

Sensors 2024, 24(19), 6229; https://doi.org/10.3390/s24196229 - 26 Sep 2024

Cited by 1 | Viewed by 1333

Abstract

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up. Full article

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

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10 pages, 985 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 - 27 Apr 2024

Cited by 4 | Viewed by 1920

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)

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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

Cited by 11 | Viewed by 6434

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

Cited by 2 | Viewed by 1555

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

Cited by 1 | Viewed by 1613

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 - 1 Nov 2023

Cited by 3 | Viewed by 3102

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 - 5 Sep 2023

Cited by 5 | Viewed by 2609

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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Review

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15 pages, 1128 KiB

Open AccessReview

Advances and Challenges Associated with Low-Cost Pulse Oximeters in Home Care Programs: A Review

by Anisbed Naranjo Rojas and Freiser Cruz Mosquera

Sensors 2024, 24(19), 6284; https://doi.org/10.3390/s24196284 - 28 Sep 2024

Cited by 2 | Viewed by 2369

Abstract

Oximeters have significantly evolved since their invention and are essential for monitoring chronic diseases in home care. However, commercial models can present an economic barrier. Therefore, we conducted a review of the use of low-cost pulse oximeters in the home care of patients with respiratory diseases. Our review included studies addressing oxygen saturation and heart rate monitoring in adults, focusing on the use of portable devices. Our search identified advances in vital signs monitoring that could provide accessible solutions for non-clinical settings. Although there are challenges related to clinical validation and accuracy, these oximeters may improve medical care, particularly in resource-limited areas. As a result, the accessibility of these devices opens up new possibilities for patients with chronic respiratory diseases in home care, enabling regular self-monitoring and increasing control over their health. Full article

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

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