Breathing Signal Research Articles

Quantitative Assessment of Spontaneous Breathing in Children: Evaluation of a Depth Camera System

This article describes a new approach for quantitative evaluation of respiration in the pediatric intensive care unit (PICU). Video sequences of thorax movements are recorded by two depth cameras to cover the 3-D surface of the torso and its lateral sides. The breathing activity implies a frame-by-frame surface deformation, which can be described by the volume variation of reconstructed surfaces between the consecutive video frames. A quantitative evaluation of the breathing pattern is then performed through a subtraction technique, thereby detecting the volume variation between the subsequent frames. A high-fidelity simulation was performed in a realistic environment designed for critically ill children. The simulation was then followed by a real-world evaluation, involving two newborn babies (one female and one male) who required ventilator support. The breathing signal patterns resulting from our approach were compared to those measured by mechanical ventilation in terms of their waveforms, evaluating the most significant dynamic parameters: tidal volume, respiratory rate, and minute ventilation. Our experimental study showed a significant agreement between the proposed 3-D imaging system and the gold standard method in estimating respiratory waveforms and parameters. This article presents the following innovations. First, we suggest a 3-D imaging system specifically designed for PICUs based on a contactless design. Second, we propose an efficient positioning mechanism for the cameras, offering a very high spatial coverage of the thoracoabdominal zone and considering the PICU constraints. Finally, we propose an objective vision-based method to quantitatively measure respiration for spontaneous breathing patients in PICUs.

Experimental research of digital filtering in the separation of breathing signals and heart contractions to assess the control of the driver’s condition

The article is devoted to the research of the method of digital filtering of a mixture of signals at ultra-low frequencies. A mathematical model of the system has been developed that includes an adequate signal model that really brings the result. The type of filter and the window function that is included in it were selected, and an algorithm for digital filtering of the signal mixture was developed that allows the most accurate selection of the received signal parameters. The obtained research results confirmed the efficiency of the mathematical model and the filtering algorithm of the signal mixture, and also proved their practicality in use.

Low Frequency Forced Oscillation Lung Function Test Can Distinguish Dynamic Tissue Non-linearity in COPD Patients.

This paper introduces the use of low frequencies forced oscillation technique (FOT) in the presence of breathing signal. The hypothesis tested is to evaluate the sensitivity of FOT to various degrees of obstruction in COPD patients. The measurements were performed in the frequency range 0–2 Hz. The use of FOT to evaluate respiratory impedance has been broadly recognized and its complementary use next to standardized method as spirometry and body plethysmography has been well-documented. Typical use of FOT uses frequencies between 4–32 Hz and above. However, interesting information at frequencies below 4 Hz is related to viscoelastic properties of parenchyma. Structural changes in COPD affect viscoelastic properties and we propose to investigate the use of FOT at low frequencies with a fourth generation fan-based FOT device. The generator non-linearity introduced by the device is separated from the linear approximation of the impedance before evaluating the results on patients. Three groups of COPD obstruction, GOLD II, III, and IV are evaluated. We found significant differences in mechanical parameters (tissue damping, tissue elasticity, hysteresivity) and increased degrees of non-linear dynamic contributions in the impedance data with increasing degree of obstruction (p < 0.01). The results obtained suggest that the non-linear index correlates better with degrees of heterogeneity linked to COPD GOLD stages, than the currently used hysteresivity index. The protocol and method may prove useful to improve current diagnosis percentages for various COPD phenotypes.

DESIGN AND IMPLEMENTATION OF LOW COST DATA ACQUISITION SYSTEM FOR ECG SIGNAL AND EDR USING LABVIEW AND ARDUINO

usage of electrocardiogram (ECG) signals not only limited to the medical services, but also used for studying, learning, processing and analysing. However, the devices such as BIOPAC Systems that used for this purpose are extremely expensive and often not existed, particularly in the third world. The aim of this paper is to design and implement low cost data acquisition system to capture ECG signals and generating ECG-Derived Respiration (EDR) using the LabVIEW platform and Arduino with help of AD8232 chip. The system is designed to monitor the heart signals over the time and calculates the heart rate accurately. It also constructs the breathing signal indirectly by detecting R peaks in ECG signals and interpolating them. Furthermore, this system introduces a unique feature through giving ability to save both the heart and breathing signals as the Matlab file to be used for further analysis. The outcomes show that the ECG signals are in the standard range, as well as constructing breathing signal was successfully done, and the obtained low cost system can be useful for biomedical studies.

A Comparison of SVM and CNN-LSTM Based Approach for Detecting Smoke Inhalations from Respiratory signal.

Wearable sensors have successfully been used in recent studies to monitor cigarette smoking events and analyze people's smoking behavior. Respiratory inductive plethysmography (RIP) has been employed to track breathing and to identify characteristic breathing pattern specific to smoking. Pattern recognition algorithms such as Support Vector Machine (SVM), Hidden Markov Model, Decision tree, or ensemble approaches have been used to identify smoke inhalations. However, no deep learning approaches, which have been proved effective to many time series datasets, have ever been tested yet. Hence, a Convolutional Neural Network (CNN) and Long Term Short Memory (LSTM) based approach is presented in this paper to detect smoke inhalations in the breathing signal. To illustrate the effectiveness of this deep learning approach, a traditional machine learning (SVM) based approach was used for comparison. On the validation dataset of 120 smoking sessions performed in a laboratory setting by 30 moderate-to-heavy smokers, the CNN-LSTM approach achieved an F1-score of 72% in leave-one-subject-out (LOSO) cross-validation method whereas the classical SVM approach scored 63%. These results suggest that deep learning-based approaches might provide a better analytical method for detection of smoke inhalations than more conventional machine learning approaches.

Probability-based 3D k-space sorting for motion robust 4D-MRI.

Current 4D-MRI techniques are prone to breathing-variation-induced motion artifacts. This study developed a novel method for motion-robust multi-cycle 4D-MRI using probability-based multi-cycle sorting to overcome this deficiency. The main cycles were first extracted from the breathing signal. 3D k-space data were then sorted using a result-driven method for each main cycle. The new method was tested on a 4D-extended cardiac-torso (XCAT) phantom with a patient and an artificially generated breathing curve. For comparison, the k-space data were sorted using conventional phase sorting to generate single-cycle 4D-MRI images. Signal-to-noise ratio (SNR) of tumor and liver, tumor volume consistency, and average intensity projection (AIP) accuracy were compared between the two methods. The original phantom images were used as references for the evaluation. The new method showed improved tumor-to-liver SNR and tumor volume consistency as compared to 3D k-space phase sorting in both the simulated artificial and real patient breathing signals. For the artificial breathing cycles, the average tumor-to-liver SNR and standard deviation (SD) of tumor volume were 2.53 and 3.80% for cycle 1, 2.24 and 6.16% for cycle 2 of probability-based sorting as compared to 1.47 and 21.83% obtained using the phase sorting method; for the patient breathing curve, values of 1.99 and 2.71%, 1.97 and 3.29%, 1.88 and 4.16% were observed for cycle 1, cycle 2 and cycle 3 of probability-based sorting, versus 1.44 and 7.20% for phase sorting method. Furthermore, the AIP accuracy was improved in the probability-based sorting approach when compared to phase sorting, with the average intensity difference per voxel reduced from 0.39 to 0.15 for the artificial curve, and from 0.46 to 0.21 for the patient curve. We demonstrated the feasibility of probability-based 3D k-space sorting for motion-robust multi-cycle 4D-MRI reconstruction with breathing variation induced motion artifact reduction compared with conventional 2D image sorting and 3D phase sorting methods. This new technique can potentially improve the accuracy of radiation treatment guidance for mobile targets.

FBSE-EWT-Based Approach for the Determination of Respiratory Rate From PPG Signals

Respiratory rate (RR) is a vital parameter that shows signs of abnormal human breathing activity. There are various techniques for extracting RR. In addition to oxygen saturation (SpO2) and cardiac rate measurement, the photoplethysmography (PPG) signal can be used to obtain breathing information that prevents the additional measurement sensor from being used. An algorithm has been suggested for the extraction of respiratory data from PPG signals using Fourier-Bessel series expansion-based empirical wavelet transform (FBSE-EWT) in this article. We have taken 310 and 632 epochs of simultaneous recorded PPG and breathing signals from the MIMIC and Capnobase databases in order to investigate the efficiency of the suggested algorithm. RR extraction from PPG signals by FBSE-EWT shows that the root mean square errors (RMSEs) for both the MIMIC and Capnobase databases are 0.48549 breaths/min and 0.92545 breaths/min, respectively. These findings show that the suggested FBSE-EWT method is more accurate in estimating RR in comparison to other existing techniques.

Recognition of Sleep/Wake States analyzing Heart Rate, Breathing and Movement Signals.

This document presents an algorithm for a non-obtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen's kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement.

Evaluating Accuracy of Respiratory Rate Estimation from Super Resolved Thermal Imagery.

Non-contact estimation of Respiratory Rate (RR) has revolutionized the process of establishing the measurement by surpassing some issues related to attaching sensors to a body, e.g. epidermal stripping, skin disruption and pain. In this study, we perform further experiments with image processing-based RR estimation by using various image enhancement algorithms. Specifically, we employ Super Resolution (SR) Deep Learning (DL) network to generate hallucinated thermal image sequences that are then analyzed to extract breathing signals. DL-based SR networks have been proved to increase image quality in terms of Peak Signal-to-Noise ratio. However, it hasn't been evaluated yet whether it leads to better RR estimation accuracy, what we address in this study. Our research confirms that for estimator based on the dominated peak in the frequency spectrum Root Mean Squared Error improves by 0.15bpm for 8-bit and by 0.84bpm for 16-bit data comparing to original sequences if hallucinated frames are used. Mean Absolute Error is reduced by 0.63bpm for average aggregator and by 2.06bpm for skewness. This finding can enable various remote monitoring solutions that may suffer from poorer accuracy due to low spatial resolution of utilized thermal cameras.

Noncontact RF Vital Sign Sensor for Continuous Monitoring of Driver Status.

In this paper, a radio frequency vital sign sensor based on double voltage-controlled oscillators (VCOs) combined with a switchable phase-locked loop (PLL) is proposed for a noncontact remote vital sign sensing system. Our sensing system primarily detects the periodic movements of the human lungs and the hearts via the impedance variation of the resonator. With a change in impedance, both the VCO oscillation frequency and the PLL feedback voltage also change. Thus, by tracking the feedback voltage of the PLL, breath and heart rate signals can be acquired simultaneously. However, as the distance between the body and the sensor varies, there are certain points with minimal sensitivity, making it is quite difficult to detect vital signs. These points, called impedance null points, periodically occur at distances proportional to the wavelength. To overcome the impedance null point problem, two resonators operating at different frequencies, 2.40 and 2.76GHz, are employed as receiving components. In an experiment to investigate the sensing performance as a function of distance, the measurement distance was accurately controlled by a linear actuator. Furthermore, to evaluate the sensing performance in a real environment, experiments were carried out with a male and a female subject in a static vehicle. To demonstrate the real-time vital sign monitoring capability, spectrograms were utilized, and the accuracy was assessed relative to reference sensors. Based on the results, it is demonstrated that the proposed remote sensor can reliably detect vital signs in a real vehicle environment.

Tempo de amamentação e sinais de respiração oral em crianças com mau desempenho escolar

Introdução: A amamentação é fator de proteção para doenças respiratórias, além de contribuir para o desenvolvimento intelectual das crianças. Objetivo: Analisar a relação do tempo de amamentação com sinais de respiração oral em crianças com mau desempenho escolar. Métodos: Estudo transversal, realizado com 82 crianças de 7 a 12 anos de idade, com mau desempenho escolar, recrutadas nas escolas públicas de uma cidade do interior de Minas Gerais. Os pais responderam a um questionário contendo os seguintes temas: tempo de amamentação em meses, sexo da criança, escolaridade materna e sinais de respiração oral. Para a análise estatística foi empregado o teste Qui-quadrado de Pearson e Teste Exato de Fisher, considerando o nível de significância de 5%. Resultados: Do total das crianças estudadas, 61 (74%) receberam amamentação por mais de seis meses, 60 (73%) eram do sexo masculino, 47 (56%) das mães eram analfabetas ou tinham o ensino fundamental incompleto. Os sinais de respiração oral estavam presentes em 57 (70%) dos escolares. Houve associação entre o tempo de amamentação e o relato de queixas de obstrução nasal esporadicamente, dificuldade ou demora ao engolir o alimento e dormir de boca aberta. Conclusão: As crianças que foram amamentadas por tempo inferior a seis meses apresentaram maior número combinado de sinais de respiração oral. Houve associação estatisticamente significante entre os sinais de respiração oral e o tempo de amamentação. O rastreamento de crianças que precisam ser encaminhadas para avaliação multiprofissional do modo respiratório pode ser realizado por meio das informações sobre tempo de amamentação e sinais de respiração oral combinados.

“Il flauto magico” still works: Mozart’s secret of ventilation

Background: Synchronisation/coupling between respiratory patterns and musical structure.&#x0D; Methods: Healthy professional musicians and members of the audience were studied during a performance of W.A. Mozart’s Piano Concerto KV 449. Electrocardiogram (ECG)/Heart Rate Variability (HRV) data recording (Schiller: MedilogWAR12, ECG-channels: 3, sampling rate: 4096 Hz, 16 Bit) was carried out and a simultaneous synchronized high definition video/audio recording was made. The breathing-specific data were subsequently extracted using Electrocardiogram-derived respiration (EDR; Software: Schiller medilogWDARWIN) from the HRV data and overlaid at the same time onto the musical score using FINALE 2011 notation software and the GIMP 2.0 graphics programme. The musical score was graphically modified graphically so that the time code of the breathing signals coincided exactly with the notated musical elements. Thus a direct relationship could be produced between the musicians’ breathing activity and the musical texture. In parallel with the medical/technical analysis, a music analysis of the score was conducted with regard to the style and formal shaping of the composition.&#x0D; Results: It was found that there are two archetypes of ideally typical breathing behaviour in professional musicians that either drive the musical creation, performance and experience or are driven by the musical structure itself. These archetypes also give rise to various states of synchronisation and regulation between performers, audience and the musical structure.&#x0D; Conclusions: There are two archetypes of musically-induced breathing which not only represent the identity of music and human physiology but also offer new approaches for multidisciplinary respiratory medicine.

Assessment of Diaphragmatic Function by Ultrasonography: A Device to Record Breathing Signals on an Ultrasound Machine

In recent years, several works have supported the significance of ultrasonography in the assessment of diaphragmatic function [1,2].

Design and development of portable prototype for human breath analysis: a comparative study between haemodialysis patients and healthy subjects

Certain diseases and clinical conditions alter the exhaled breath gas components. Ammonia was identified as a biomarker for chronic kidney disease (CKD) from the laboratory analytical techniques. Although these techniques were accurate, lack of portability, time consumption and affordablity for the masses were the primary concern for the medical fraternity. To overcome these issues, a portable prototype was developed for the analysis of breath samples from patients undergoing hemodialysis. The aim and objectives of the study was as follows: (i) to detect ammonia in human breath sample using the commercial gas sensor (MQ135); (ii) to acquire breath patterns using NI-myDAQ for preprocessing and extraction of ten transient and steady state features; (iii) To classify the normal and abnormal breath signal using different neural network classifiers (Support Vector Machines, Naïve Bayes, and Multi-layer perceptron). Twenty-one subjects undergoing hemodialysis and 17 healthy subjects were included in the study. The obtained breath patterns revelaed that voltage flag runs between 0.6–0.9 V for a healthy individual, and 1.8–2.65 V for CKD patients. The variations in magnitude voltage were high for CKD subjects compared to healthy subjects. Among the obtained transient and steady-state features, normalized voltage change (ΔVm) showed a greater % difference between healthy and CKD patients undergoing haemodialysis. The multilayer perceptron achieved a better classification accuracy of 76.31% compared to SVM (60.52%) and naive bayes classifier (55.2%). Hence the developed prototype was simple in operation and cost effective, provides an affordable and act as complimentary diagnostic tool for CKD patients.

Detection of Breathing and Heart Rates in UWB Radar Sensor Data Using FVPIEF-Based Two-Layer EEMD

Ultra-wideband (UWB) radar is an important remote sensing tool of life detection or a non-contact monitor of the vital signals. By processing the received UWB pulse echoes reflected from the body, different signals corresponding to heart activity and breathing, corrupted by body motion and the environment noise, are wanted to be separated clearly. However, the heartbeat signal is so tiny that it is covered by breathing harmonics and clutters. At the same time, since the frequencies of the vital signals are very close, usually around 1 Hz, it is difficult to apply an ordinary frequency filter to separate them apart. This problem induces that the vital signal detection method, usually, only detects the large breath signal, not the heartbeat signal. To solve this problem, a novel method is provided, in this paper, to extract the heartbeat and the breath information simultaneously. The method uses the feature time index with the first valley peak of the energy function of intrinsic mode functions (FVPIEF) calculated by pseudo bi-dimension ensemble empirical mode decomposition method and extracts the vital signals by the ensemble empirical mode decomposition (EEMD). Both simulation and experiment results evidently show that the proposed FVPIEF based two-layer EEMD method is effective for separating the small heartbeat signal from the large breath signal and significantly improves the evaluation of heart and breathing rates in both hold-breathing and breathing conditions.

Limbic and paralimbic structures driving ictal central apnea.

To precisely identify cortical regions that modulate breathing, and delineate a network of cortical structures that underpin ictal central apnea (ICA) during epileptic seizures. We electrically stimulated multiple cortical structures in patients undergoing stereotactic EEG (SEEG) evaluation before epilepsy surgery. Structures investigated were orbitofrontal cortex, anterior and posterior cingulate and subcallosal gyri, insula, hippocampus, parahippocampal gyrus, amygdala, temporo-polar cortex, antero-mesial fusiform gyrus, and lateral and basal temporal cortices. Chest/abdominal excursions using thoracic/abdominal belts, peripheral capillary oxygen saturation, end tidal and transcutaneous carbon dioxide, and airflow were continuously monitored. Nineteen consecutive adult patients (10 female) aged 18-69 years were investigated. Transient central apnea was elicited in 13/19 patients with amygdala, hippocampus head and body, anterior parahippocampal gyrus, and antero-mesial fusiform gyrus. Insula, cingulate, subcallosal, orbitofrontal, lateral, and basal temporal cortices stimulation did not induce apnea. Apnea duration was associated with stimulus duration (p < 0.001) and current intensity (p = 0.004). These findings suggest a limbic/paralimbic mesial temporal breathing modulation network that includes amygdala, hippocampus, anterior parahippocampal, and antero-mesial fusiform gyri. These structures likely represent anatomical and functional substrates for ICA, a putative sudden unexpected death in epilepsy (SUDEP) breathing biomarker. Damage to such areas is known to occur in high SUDEP risk patients and SUDEP victims, and may underpin the prolonged ICA that is thought to be particularly dangerous. Furthermore, inclusive targeting of apnea-producing structures in SEEG implantations, peri-ictal breathing signal recordings, and stringent analysis of apneic sequences in seizure semiology may enhance accurate identification of symptomatogenic and seizure onset zones for epilepsy surgery.

65. Study of respiratory signals of patients undergoing 4DCT for VMAT lung cancer treatments

Purpose To account for respiratory motion in radiotherapy treatment of lung lesions, 4DCT is often employed. For a correct reconstruction of respiratory phases, periodic and regular breathing is the underlying hypothesis when phase-binning algorithms are used. Aim of this work was to study the respiratory signals acquired during 4DCT and to evaluate whether a correlation exists between deviations from regularity of breathing signal and quality of reconstructed images. Methods and materials 4DCT of 40 patients acquired with a Philips Brilliance Big Bore CT were analyzed in this study. Respiratory signals were exported and an in-house written Matlab routine was used for extracting and processing the waveforms. The duration of each respiratory cycle (CD) was calculated as the distance between two consecutive maxima, while the fraction of inspiration (ID) was calculated as the ratio between the distance minimum–maximum and the duration of the whole cycle. Mean value (M), standard deviation ( σ ) and relative percentage error Er = σ M × 100 were extracted for each patient, the latter being a measurement of breathing regularity. Thereafter, 4DCT image quality was blindly scored from 1 to 4 (1: presence of visible artifacts in the reconstructed phases, 4: absence of visible artifacts). Patients were classified according to the score and mean relative percentage errors and standard deviations were calculated within each scoring class. Results In Fig. 1 mean relative percentage errors versus assigned score are reported for both CD and ID. A correlation between Er and image quality is clearly visible. Threshold values for Er of about 18% for CD and about 15% for ID can be identified as limits between good and bad quality reconstructed images. Conclusion Variations in respiratory cycle and inspiration phase duration clearly correlate with the quality of 4DCT reconstruction and can be used as predictors of image quality. Download : Download high-res image (153KB) Download : Download full-size image

A Novel Method for Breath Detection via Stepped-Frequency Continuous Wave Ultra-Wideband (SFCW UWB) Radars Based on Operational Bandwidth Segmentation.

Human being detection via ultra-wideband (UWB) radars has shown great prospects in many areas, such as biomedicine, military operation, public security, emergency rescue, and so on. When a person stays stationary, the main feature that separates him/her from surroundings is the movement of chest wall due to breath. There have been many algorithms developed for breath detection while using UWB radars. However, those algorithms were almost based on a basic scheme that focused on processing in the time dimension of UWB data. They did not utilize the benefits from the wide operational bandwidth of UWB radars to show potential superiority over those narrowband systems such as a continuous wave (CW) Doppler radar. In this paper, a breath detection method was proposed based on operational bandwidth segmentation. A basic theoretical model was firstly introduced, indicating that characteristics of breath signals contained in UWB echoes were consistent among the operational frequencies, while those of clutters were not. So, the method divided a set of UWB echo data into a number of subsets, each of which corresponded to a sub-band within the operational bandwidth of the UWB radar. Thus information about the operational frequency is provided for subsequent processing. With the aid of the information, a breath enhancement algorithm was developed mainly by averaging the segmented UWB data along the operational frequency. The algorithm’s performance was verified by data measured by a stepped-frequency CW (SFCW) UWB radar. The experimental results showed that the algorithm performed better than that without the segmentation. They also showed its feasibility for fast detection of breath based on a short duration of data. Moreover, the method’s potential for target identification and impulse-radio (IR) UWB radar was investigated. In summary, the method provides a new processing scheme for UWB radars when they are used for breath detection. With this scheme, the UWB radars have a benefit of greater flexibility in data processing over those narrowband radars, and thus will perform more effectively and efficiently in practical applications.

Optimized breath detection algorithm in electrical impedance tomography

Objective: This paper defines a method for optimizing the breath delineation algorithms used in electrical impedance tomography (EIT). In lung EIT the identification of the breath phases is central for generating tidal impedance variation images, subsequent data analysis and clinical evaluation. The optimisation of these algorithms is particularly important in neonatal care since the existing breath detectors developed for adults may give insufficient reliability in neonates due to their very irregular breathing pattern. Approach: Our approach is generic in the sense that it relies on the definition of a gold standard and the associated definition of detector sensitivity and specificity, an optimisation criterion and a set of detector parameters to be investigated. The gold standard has been defined by 11 clinicians with previous experience with EIT and the performance of our approach is described and validated using a neonatal EIT dataset acquired within the EU-funded CRADL project. Main results: Three different algorithms are proposed that improve the breath detector performance by adding conditions on (1) maximum tidal breath rate obtained from zero-crossings of the EIT breathing signal, (2) minimum tidal impedance amplitude and (3) minimum tidal breath rate obtained from time-frequency analysis. As a baseline a zero-crossing algorithm has been used with some default parameters based on the Swisstom EIT device. Significance: Based on the gold standard, the most crucial parameters of the proposed algorithms are optimised by using a simple exhaustive search and a weighted metric defined in connection with the receiver operating characterics. This provides a practical way to achieve any desirable trade-off between the sensitivity and the specificity of the detectors.

Analysis of measurement conditions for detecting change in vital signs with catheter flow sensor

We previously developed a catheter type flow sensor for measuring both breathing and heartbeat signals by analyzing the frequency of airflow signals in the airway during breathing (Kawaoka et al 2015 18th Int. Conf. on Solid-State Sensors, Actuators and Microsystems, Kawaoka et al 2015 IEEE Sensors, Kawaoka et al 2016 IEEE 29th Int. Conf. Micro Electro Mechanical Systems). In this study, we verified the optimized conditions for measuring heartbeat in the airway airflow and also confirmed during an animal experiment that the developed flow sensor can take measurements while following the change in the vital signs of rats. To obtain the necessary conditions for measuring the heartbeat, frequency analysis was performed using the airflow signals in the airway and measured with varying frequency resolutions. We experimentally confirmed that the heartbeat can be measured in the airflow when the frequency resolution is 1/10 or less of the basic breathing frequency. We also confirmed that the developed sensor could measure the changes in vital signs by administrating adrenaline and propranolol hydrochloride in experiments with rats.