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Sensors and Methods for the Measurement of Cardiovascular and Respiratory Systems

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 21826

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Special Issue Editors

Dr. Antti Vehkaoja

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Guest Editor

Tampere University of Technology, Tampere, Finland
Interests: unobtrusive measurement methods; wearable sensors; cardiovascular monitoring

Dr. Jari Viik

E-Mail Website
Guest Editor

Tampere University of Technology, Tampere, Finland
Interests: physiological measurements; electrocardiography; ballistocardiography; bioimpedance measurements; impedance pneumography; non-invasive sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cardiovascular and respiratory systems are the most important life-supporting systems of a body. Sensors and methods for measuring their operation are constantly under active research. Measurements of cardiovascular and respiratory systems may also be used to provide information on the status of the autonomic nervous system.

We invite manuscripts presenting and evaluating sensors as well as measurement or sensor signal analysis methods for assessing the condition of the aforementioned systems or monitoring their operation. In addition, methods using their signals for indirectly monitoring other physiological systems are within the scope of this Special Issue. Both original research papers and review articles relating to the sensors and methods in the areas of this Special Issue are welcomed.

Prof. Antti Vehkaoja
Prof. Jari Viik
Guest Editors

Manuscript Submission Information

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Keywords

cardiovascular measurements
respiratory measurements
wearable or ambulatory sensors
unobtrusive measurement systems
analysis methods
sensor signal processing
diagnostic methods
autonomic nervous system

Published Papers (6 papers)

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Research

14 pages, 2064 KiB

Open AccessArticle

Respiratory Activity during Exercise: A Feasibility Study on Transition Point Estimation Using Impedance Pneumography

by Marcel Młyńczak and Hubert Krysztofiak

Sensors 2021, 21(18), 6233; https://doi.org/10.3390/s21186233 - 17 Sep 2021

Viewed by 2328

Abstract

The current diagnostic procedures for assessing physiological response to exercise comprise blood lactates measurements, ergospirometry, and electrocardiography. The first is not continuous, the second requires specialized equipment distorting natural breathing, and the last is indirect. Therefore, we decided to perform the feasibility study with impedance pneumography as an alternative technique. We attempted to determine points in respiratory-related signals, acquired during stress test conditions, that suggest a transition similar to the gas exchange threshold. In addition, we analyzed whether or not respiratory activity reaches steady states during graded exercise. Forty-four students (35 females), practicing sports on different levels, performed a graded exercise test until exhaustion on cycloergometer. Eventually, the results from 34 of them were used. The data were acquired with Pneumonitor 2. The signals demonstrated that the steady state phenomenon is not as evident as for heart rate. The results indicated respiratory rate approaches show the transition point at the earliest (more than 6 min before the end of the exercise test on average), and the tidal volume ones at the latest (less than 5 min). A combination gave intermediate findings. The results showed the impedance pneumography appears reasonable for the transition point estimation, but this should be further studied with the reference. Full article

(This article belongs to the Special Issue Sensors and Methods for the Measurement of Cardiovascular and Respiratory Systems)

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37 pages, 4855 KiB

Open AccessArticle

Removal of ECG Artifacts Affects Respiratory Muscle Fatigue Detection—A Simulation Study

by Lorenz Kahl and Ulrich G. Hofmann

Sensors 2021, 21(16), 5663; https://doi.org/10.3390/s21165663 - 23 Aug 2021

Cited by 6 | Viewed by 2677

Abstract

This work investigates elimination methods for cardiogenic artifacts in respiratory surface electromyographic (sEMG) signals and compares their performance with respect to subsequent fatigue detection with different fatigue algorithms. The analysis is based on artificially constructed test signals featuring a clearly defined expected fatigue level. Test signals are additively constructed with different proportions from sEMG and electrocardiographic (ECG) signals. Cardiogenic artifacts are eliminated by high-pass filtering (HP), template subtraction (TS), a newly introduced two-step approach (TSWD) consisting of template subtraction and a wavelet-based damping step and a pure wavelet-based damping (DSO). Each method is additionally combined with the exclusion of QRS segments (gating). Fatigue is subsequently quantified with mean frequency (MNF), spectral moments ratio of order five (SMR5) and fuzzy approximate entropy (fApEn). Different combinations of artifact elimination methods and fatigue detection algorithms are tested with respect to their ability to deliver invariant results despite increasing ECG contamination. Both DSO and TSWD artifact elimination methods displayed promising results regarding the intermediate, “cleaned” EMG signal. However, only the TSWD method enabled superior results in the subsequent fatigue detection across different levels of artifact contamination and evaluation criteria. SMR5 could be determined as the best fatigue detection algorithm. This study proposes a signal processing chain to determine neuromuscular fatigue despite the presence of cardiogenic artifacts. The results furthermore underline the importance of selecting a combination of algorithms that play well together to remove cardiogenic artifacts and to detect fatigue. This investigation provides guidance for clinical studies to select optimal signal processing to detect fatigue from respiratory sEMG signals. Full article

(This article belongs to the Special Issue Sensors and Methods for the Measurement of Cardiovascular and Respiratory Systems)

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18 pages, 3172 KiB

Open AccessArticle

A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients

by Simon Lyra, Leon Mayer, Liyang Ou, David Chen, Paddy Timms, Andrew Tay, Peter Y. Chan, Bergita Ganse, Steffen Leonhardt and Christoph Hoog Antink

Sensors 2021, 21(4), 1495; https://doi.org/10.3390/s21041495 - 21 Feb 2021

Cited by 37 | Viewed by 6384

Abstract

Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this study, we investigated both skin temperature trend measurement and the extraction of respiration-related chest movements to determine the respiratory rate using low-cost hardware in combination with advanced algorithms. In addition, the frequency of medical examinations or visits to the patients was extracted. We implemented a deep learning-based algorithm for real-time vital sign extraction from thermography images. A clinical trial was conducted to record data from patients on an intensive care unit. The YOLOv4-Tiny object detector was applied to extract image regions containing vital signs (head and chest). The infrared frames were manually labeled for evaluation. Validation was performed on a hold-out test dataset of 6 patients and revealed good detector performance (0.75 intersection over union, 0.94 mean average precision). An optical flow algorithm was used to extract the respiratory rate from the chest region. The results show a mean absolute error of 2.69 bpm. We observed a computational performance of 47 fps on an NVIDIA Jetson Xavier NX module for YOLOv4-Tiny, which proves real-time capability on an embedded GPU system. In conclusion, the proposed method can perform real-time vital sign extraction on a low-cost system-on-module and may thus be a useful method for future contactless vital sign measurements. Full article

(This article belongs to the Special Issue Sensors and Methods for the Measurement of Cardiovascular and Respiratory Systems)

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14 pages, 533 KiB

Open AccessArticle

SDNN24 Estimation from Semi-Continuous HR Measures

by Davide Morelli, Alessio Rossi, Leonardo Bartoloni, Massimo Cairo and David A. Clifton

Sensors 2021, 21(4), 1463; https://doi.org/10.3390/s21041463 - 20 Feb 2021

Cited by 3 | Viewed by 3125

Abstract

The standard deviation of the interval between QRS complexes recorded over 24 h (SDNN24) is an important metric of cardiovascular health. Wrist-worn fitness wearable devices record heart beats 24/7 having a complete overview of users’ heart status. Due to motion artefacts affecting QRS complexes recording, and the different nature of the heart rate sensor used on wearable devices compared to ECG, traditionally used to compute SDNN24, the estimation of this important Heart Rate Variability (HRV) metric has never been performed from wearable data. We propose an innovative approach to estimate SDNN24 only exploiting the Heart Rate (HR) that is normally available on wearable fitness trackers and less affected by data noise. The standard deviation of inter-beats intervals (SDNN24) and the standard deviation of the Average inter-beats intervals (ANN) derived from the HR (obtained in a time window with defined duration, i.e., 1, 5, 10, 30 and 60 min), i.e.,

A N N = \frac{60}{H R}

(SDANN

_{H R}

24), were calculated over 24 h. Power spectrum analysis using the Lomb-Scargle Peridogram was performed to assess frequency domain HRV parameters (Ultra Low Frequency, Very Low Frequency, Low Frequency, and High Frequency). Due to the fact that SDNN24 reflects the total power of the power of the HRV spectrum, the values estimated from HR measures (SDANN

_{H R}

24) underestimate the real values because of the high frequencies that are missing. Subjects with low and high cardiovascular risk show different power spectra. In particular, differences are detected in Ultra Low and Very Low frequencies, while similar results are shown in Low and High frequencies. For this reason, we found that HR measures contain enough information to discriminate cardiovascular risk. Semi-continuous measures of HR throughout 24 h, as measured by most wrist-worn fitness wearable devices, should be sufficient to estimate SDNN24 and cardiovascular risk. Full article

(This article belongs to the Special Issue Sensors and Methods for the Measurement of Cardiovascular and Respiratory Systems)

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16 pages, 8712 KiB

Open AccessArticle

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

by Uduak Z. George, Kee S. Moon and Sung Q. Lee

Sensors 2021, 21(4), 1393; https://doi.org/10.3390/s21041393 - 17 Feb 2021

Cited by 12 | Viewed by 3433

Abstract

Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm. Full article

(This article belongs to the Special Issue Sensors and Methods for the Measurement of Cardiovascular and Respiratory Systems)

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25 pages, 2052 KiB

Open AccessArticle

Towards Continuous and Ambulatory Blood Pressure Monitoring: Methods for Efficient Data Acquisition for Pulse Transit Time Estimation

by Oludotun Ode, Lara Orlandic and Omer T. Inan

Sensors 2020, 20(24), 7106; https://doi.org/10.3390/s20247106 - 11 Dec 2020

Viewed by 2920

Abstract

We developed a prototype for measuring physiological data for pulse transit time (PTT) estimation that will be used for ambulatory blood pressure (BP) monitoring. The device is comprised of an embedded system with multimodal sensors that streams high-throughput data to a custom Android application. The primary focus of this paper is on the hardware–software codesign that we developed to address the challenges associated with reliably recording data over Bluetooth on a resource-constrained platform. In particular, we developed a lossless compression algorithm that is based on optimally selective Huffman coding and Huffman prefixed coding, which yields virtually identical compression ratios to the standard algorithm, but with a 67–99% reduction in the size of the compression tables. In addition, we developed a hybrid software–hardware flow control method to eliminate microcontroller (MCU) interrupt-latency related data loss when multi-byte packets are sent from the phone to the embedded system via a Bluetooth module at baud rates exceeding 115,200 bit/s. The empirical error rate obtained with the proposed method with the baud rate set to 460,800 bit/s was identically equal to

0 %

. Our robust and computationally efficient physiological data acquisition system will enable field experiments that will drive the development of novel algorithms for PTT-based continuous BP monitoring. Full article

(This article belongs to the Special Issue Sensors and Methods for the Measurement of Cardiovascular and Respiratory Systems)

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