Respiratory Monitoring Device Research Articles

Development of a novel humidity sensor for breath pattern recognition using large dislocation hollow core fiber infused with nano-carbon quantum water gel microfiber

This study presents a humidity sensor based on a large-core-offset hollow core optical fiber (LD-HCF) combined with polyvinyl alcohol carbon quantum dots (PVA-CQDs) nanocomposite microfiber for monitoring human respiratory. PVA-CQDs was introduced into the hollow core structure of LD-HCF to develope the respiratory sensor. The sensor has been integrated into a respiratory monitoring mask, exposed to ultraviolet (UV) light and demonstrated with a humidity sensitivity of 0.1721 nm/%RH as well as response and recovery times of 0.232 s and 0.252 s, respectively. The mask provides the real-time monitoring of human respiratory rates during various activities, including walking, standing, coughing, running, apnea 1 time and apnea 3 times. The typical respiratory patterns have been classified with a remarkable accuracy of 97.62 % using a WOA-1D CNN-LSTM neural network. The respiratory monitoring device is proposed by optical fiber, offering a promising potential in the real-time monitoring for healthcare process.

RFID-Based Respiration Monitoring Under Daily Body Motion Disturbances

Traditional respiratory monitoring methods typically use wearable sensors. In recent years, Radio Frequency Identification (RFID) tags have emerged as potential respiratory monitoring devices. However, existing research only detects respiration under static or simple body movements such as forward and backward motions, which limits the availability of RFID. In this article, an RFID-based respiratory monitoring system under body motion disturbances is proposed. In order to eliminate the effect of body movement on the signal, reference tags are placed on the user’s shoulders. The phase between different tags is calibrated and aligned to remove the discreteness. Then, a sliding window peak-seeking algorithm is designed to calculate respiratory rate over time. The system measures respiratory rate during daily exercise and maintains an accuracy of no less than 86.12%, even when influenced by lying down and turning. Additionally, the system can analyze estimated respiratory rates and intervals to provide alert information.

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.

Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment. In a retrospective study, 737 patients underwent [18F]FDG PET/CT scans using the uMI Panorama PET/CT scanner. Ninety-nine patients, who also had respiration monitoring device (VSM), formed the validation set. The remaining data of the 638 patients were used to train neural networks used in the uRMC. The uRMC primarily consists of three key components: (1) data-driven respiratory signal extraction, (2) attenuation map generation, and (3) PET-CT alignment. SUV metrics were calculated within 906 lesions for three approaches, i.e., data-driven uRMC (proposed), VSM-based uRMC, and OSEM without motion correction (NMC). RM magnitude of major organs were estimated. uRMC enhanced diagnostic capabilities by revealing previously undetected lesions, sharpening lesion contours, increasing SUV values, and improving PET-CT alignment. Compared to NMC, uRMC showed increases of 10% and 17% in SUVmax and SUVmean across 906 lesions. Sub-group analysis showed significant SUV increases in small and medium-sized lesions with uRMC. Minor differences were found between VSM-based and data-driven uRMC methods, with the SUVmax was found statistically marginal significant or insignificant between the two methods. The study observed varied motion amplitudes in major organs, typically ranging from 10 to 20mm. A data-driven solution for respiratory motion in PET/CT has been developed, validated and evaluated. To the best of our knowledge, this is the first unified solution that compensates for the motion blur within PET, the attenuation mismatch artifacts caused by PET-CT misalignment, and the misalignment between PET and CT.

Beyond energy harvesting: a review on the critical role of MXene in triboelectric nanogenerator

In the field of advanced materials and energy harvesting, MXene has played a pivotal role in advancing the development of triboelectric nanogenerators (TENGs). This contribution is notable not only in terms of enhancing the performance of TENGs but also in expanding their application range. A comprehensive review of MXene materials is offered herein to delve into the significant impact of MXene on the growing efficiency of energy harvesting and widening application in areas ranging from energy harvesters to physiochemical sensors to self-powered intelligent systems. We begin with the fundamentals of MXene and TENGs, then highlight how MXene improves TENGs via its integration into the triboelectrification and electrode layers to increase the electronegativity, charge density, and introduce self-healing and stretchability. The discussion then extends to the modifications in MXene that boost the electrical output, stability, and collection efficiency of TENGs. Additionally, the review covers the diverse applications of MXene-based TENGs in extreme environments, respiratory monitoring, and multi-purpose devices, emphasizing its critical role in promoting TENGs to future self-powered intelligent systems.

CT respiratory motion synthesis using joint supervised and adversarial learning

Objective. Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery. Approach. In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude. Main results. Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim). Significance. This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found at https://github.com/cyiheng/Dynagan.

Wearable strain sensor utilizing the synergistic effect of Ti3C2Tx MXene/AgNW nanohybrid for point-of-care respiratory monitoring

Respiratory signals are significant indicators for detecting changes in physiological conditions and early diagnosis of numerous respiratory illnesses. Herein, we have integrated a high-performance strain sensor and portable circuit board (PCB) with personalized interface to develop point-of-care respiratory monitoring device. The ultrasensitive strain sensor utilizes a conductive polymeric nanocomposite that harnesses the synergistic effect of Ti3C2Tx (MXene)/AgNW nanohybrid to develop a wearable device. The wearable device is capable of analysing pulmonary volumes, such as forced volume capacity (FVC) and forced expiratory volume (FEV1), while accurately recognizing various breathing patterns in a resting state (such as normal, forced, and obstructive), as well as during different physical activities. It shows excellent correlation (>93%) with commercial spirometer for measurement of pulmonary parameters. In addition, we present a wireless device for lab rat respiratory monitoring in anaesthetic state. The device is implemented for real-time respiratory monitoring under mild, normal and high anaesthesia doses. The levels of anaesthesia doses including a critical limit can be significantly discriminated by means of breathing frequency and amplitude, which ultimately results in saving their lives. These results demonstrate the practical feasibility of the strain sensing device as wearable electronics for point-of-care respiratory monitoring in humans and other species.

Standard technical specifications for methacholine chloride (Methacholine) bronchial challenge test (2023)

Standard technical specifications for methacholine chloride (Methacholine) bronchial challenge test (2023)

New Data-Driven Gated (DDG) PET/CT for Radiation Treatment Planning of NSCLC

PET/CT for RT simulation of NSCLC is typically conducted in two separate imaging sessions of gated 4D-CT in radiation oncology and fusion with non-gated PET/CT in diagnostic imaging, making treatment response assessment (TRA) with PET/CT and radiation treatment planning (RTP) challenging. To remedy this short-coming, we designed a new data-driven gated (DDG) PET/CT, based on the self-gated PET and self-gated CT data to derive the gated PET/CT data free of tumor blurring and misregistration artifacts without any respiratory monitoring device (ease of use and cost reduction). The new DDG PET/CT also provides the important 4D-CT information of end-inspiratory (EI), end-expiratory (EE), maximum-intensity-projection (MIP) and average-intensity-projection (AIP) CT's. The total imaging time is < 15 min, which makes DDG PET/CT simulation in a single session without any respiratory monitoring device possible. Acquisition is standard PET/CT imaging protocol followed by a cine CT imaging of the tumor area. We utilized the commercial DDG PET from GE Discovery PET/CT and designed a new DDG CT for attenuation correction of the DDG PET for reduction of tumor motion and mis-registration. The AIP and MIP images were from the average and maximum pixel values of the cine CT images, respectively. The cine CT images with the largest and smallest average CT number in the lung region were identified as the EE and EI CT images, respectively. For the image slices without any lung region present, the largest and smallest expansions of the body outline contour were selected to be the EE and EI CT phases, respectively. The AIP and EE CT images were used for attenuation correction of the PET and DDG PET data, respectively. Both DDG PET/CT and 4D-CT of (EI, EE, AIP and MIP) are for RT simulation of NSCLC. 38 4D-CT patient data sets were compared at the EE and EI phases between our DDG CT and 4D-CT to demonstrate the applicability of the newly designed DDG CT. A prototype software on a Dell PC of i5-6500 CPU has been successfully developed to enable DDG PET/CT and 4D-CT on seven GE Discovery PET/CT scanners. The operating system was Ubuntu and the computer language was Python. In the EE phase, the images selected by DDG CT and 4D CT were identical 62.5±21.6% of the time. In the EI phase, the images selected by DDG CT and 4D CT were identical 68.2±18.9% of the time. Inspection of the EE and EI phases of DDG CT and 50% and 0% of 4D-CT demonstrated both data sets of DDG CT and 4D CT were almost identical. A new DDG PET/CT and 4D-CT in a single imaging session without any hardware gating has been developed for TRA and RTP of NSCLC. The utility is available on the network and it enables all PET/CT scanners on the network for TRA and RTP. The new DDG CT provides the benefits of 4D-CT without any requirement of external hardware gating and make possible DDG PET/CT free of tumor motion and misregistration artifacts.

All-printed wearable humidity sensor with hydrophilic polyvinylpyrrolidone film for mobile respiration monitoring

In this study, a capacitive-type wearable humidity sensor comprising a polyvinylpyrrolidone (PVP) film, as active sensing dielectric, and carbon nanotube (CNT) random networks, as electrodes, all-printed on flexible substrates in ambient air, is demonstrated for real-time respiration monitoring. The PVP-based humidity sensor achieved a high sensing response of 3.0 ± 0.15 nF/RH%∙cm2 (where RH stands for relative humidity) and a sensitivity of 6.4 ± 0.1 pF/%RH in the RH range of 40–90%. A significantly small hysteresis window, fast response/recovery (∼ 30 ms), and high reliability were noted. The sensing mechanism of the capacitive response toward humidity was investigated, and it is attributed to continuous proton hopping through water molecule layers. The operational stability of the proposed humidity sensor under temperature variations and mechanical deformation, was investigated to address existing issues related to wearable electronics. Finally, the feasibility of a mobile respiration monitoring device is demonstrated by developing an armband-type wireless-communication monitoring system equipped with the wearable humidity sensor. Real-time biophysical information can be analyzed by monitoring the changes in the respiration intensity and frequency, which were wirelessly transmitted to the user’s mobile interface. We note that very few studies have focused on all-printed wearable humidity sensors consisting of pristine PVP and immobilized CNT random networks for real-time mobile respiration monitoring of human breath regardless of bent and relaxed state.

Lever-inspired triboelectric respiration sensor for respiratory behavioral assessment and exhaled hydrogen sulfide detection

Enhancing the flow sensitivity of the triboelectric respiration sensors (TRSs) is crucial in detecting shallow oral/nasal breath signal. The modulation of contact-separate distances between dielectrics is a simple and convenient tactic to enhance the output sensitivity of TRSs compared with the complex composite materials and microstructure design of the triboelectric dielectrics. Herein, a lever-inspired TRS with adjustable contact-separate distances between arms was designed for simultaneous respiratory behavioral assessment and exhaled hydrogen sulfide (H2S) detection. Results show that the TRS with the optimal length ratio of power arm to resistance arm (1:2) possesses excellent respiration sensing behaviors in measuring flow rate (0.5–8 L/min) and frequency (0.25–1 Hz), enabling the flow sensitivity under oral/nasal respiration. Meanwhile, an in-situ self-assembled Fe2+ doped polypyrrole (FPPy) sensing film was rationally selected as one of the back electrodes of triboelectric layers owing to the selective reaction between ferrous ions (Fe2+) and H2S. Under the optimal synthesis condition of FPPy film, the prepared TRS exhibits good H2S sensing performances, including a high response of 25.21% (10 ppm), good sensing repeatability (±0.46%), and low detection limit (1 ppm). Finally, a sensing mechanism on account of the impedance variations is proposed and verified based on the composition changes of the sensing materials unveiled by multiple analytical methods. This work proposes an effective way for building a flow-sensitive respiration monitoring device, and provides new insights into the impedance-induced sensing mechanism via establishing a linkage between interfacial gas chemisorption and output signals.

An efficient privacy-preserving control mechanism based on blockchain for E-health applications

The development of the Internet of Things (IoT) has opened up new horizons in the field of remote health data analysis to obtain smart healthcare. However, protecting patients’ data privacy seems challenging because medical files are so sensitive. There are significant risks to data confidentiality associated with storing patient health information on third-party servers. The covid-19 epidemic also enhanced the need for a temperature sensor-based respiratory monitoring device. Sharing electronic health records can aid with diagnostic accuracy when privacy and security protection are important system challenges. Due to the benefits of immutability, blockchain has been suggested as a possible option to enable personal health data exchange with privacy and security protection. This work suggests a safe and privacy-preserving diagnostic enhancement strategy for e-Health platforms based on blockchain technology, which addresses the inadequacy of previous work in these regards. The proposed work proposes an effective access control system that would let data owners specify their preferred access controls over their privacy-sensitive medical data. Users could utilize their user transactions for key generation to efficiently cancel or add authorized doctors. Experimental data and security analyses demonstrate the proposed Health-chain's suitability for use in smart healthcare systems. The thorough experimental investigation demonstrates the blockchain's effectiveness of computing and time consumption as well as its resistance to numerous security assaults.

Wearable pulmonary monitoring system with integrated functional lung imaging and chest sound recording: a clinical investigation in healthy subjects

Objective. Current wearable respiratory monitoring devices provide a basic assessment of the breathing pattern of the examined subjects. More complex monitoring is needed for healthcare applications in patients with lung diseases. A multi-sensor vest allowing continuous lung imaging by electrical impedance tomography (EIT) and auscultation at six chest locations was developed for such advanced application. The aims of our study were to determine the vest’s capacity to record the intended bio-signals, its safety and the comfort of wearing in a first clinical investigation in healthy adult subjects. Approach. Twenty subjects (age range: 23–65 years) were studied while wearing the vests during a 14-step study protocol comprising phases of quiet and deep breathing, slow and forced full expiration manoeuvres, coughing, breath-holding in seated and three horizontal postures. EIT, chest sound and accelerometer signals were streamed to a tablet using a dedicated application and uploaded to a back-end server. The subjects filled in a questionnaire on the vest properties using a Likert scale. Main results. All subjects completed the full protocol. Good to excellent EIT waveforms and functional EIT images were obtained in 89% of the subjects. Breathing pattern and posture dependent changes in ventilation distribution were properly detected by EIT. Chest sounds were recorded in all subjects. Detection of audible heart sounds was feasible in 44%–67% of the subjects, depending on the sensor location. Accelerometry correctly identified the posture in all subjects. The vests were safe and their properties positively rated, thermal and tactile properties achieved the highest scores. Significance. The functionality and safety of the studied wearable multi-sensor vest and the high level of its acceptance by the study participants were confirmed. Availability of personalized vests might further advance its performance by improving the sensor-skin contact.

MXene effectively enhances the electron-withdrawing (EW) ability and dielectric properties of PVDF-TrFE nanofibers for triboelectric nanogenerators

Functional fillers can improve the triboelectric characteristics of polyvinylidene difluoride (PVDF)-based material, a typical triboelectric nanogenerator (TENG) negative dielectric layer material. MXene fillers can significantly enhance the triboelectric characteristics of PVDF-based negative dielectric layers by dielectric modulation. However, the effect of MXene as a highly electronegative filler on the electron-withdrawing (EW) ability of PVDF/MXene composite films is frequently ignored. In this work, polyvinylidene fluoride-trifluoro-ethylene (PVDF-TrFE)/MXene (Ti3C2Tx) composite nanofibers films (PMNFs) are prepared by electrospinning. When the MXene content is 0.8 wt%, PMNFs-based TENG achieves a maximum output power density of 250 mW/m2 at a matching external load resistance of 4 MΩ and a maximum open-circuit voltage (VOC) of 85 V, 2.66 times higher than that of the PVDF-TrFE-based TENG. Enhancing the EW ability and the dielectric properties by adding MXene are considered and proven by examining the work function (WF) and dielectric constant (and loss), respectively. Finally, we design a tremor-based TENG by PMNFs and develope a novel respiratory monitor to achieve real-time respiratory monitoring, which should offer fresh perspectives on creating respiratory monitoring devices.

A Wearable Self‐Powered Multi‐Parameter Respiration Sensor

AbstractRespiratory disease can be early warned by many respiratory parameters such as intensity, rhythm, temperature, and breath molecules like acetone. It requires a complex process to measure these respiratory parameters simultaneously in clinical practice. Therefore, it is a hot topic to design portable devices especially wearable electronics to monitor respiratory conditions continually in daily life. Here, a flexible self‐powered multi‐parameter respiration sensor based on poly(vinylidene fluoride) (PVDF) thin film and flexible interdigital electrodes that is bonding with sodium‐doped zinc oxide nanoflowers is demonstrated , which can detect airflow, temperature, and acetone during breathing. The respiration sensor may provide some reference ways for the fabrication of miniaturized respiratory disease monitoring devices.

Interfractional Liver Positional Motion Under Exhaled Breath Holding Based on Cone Beam Computed Tomography.

We measured interfractional liver positional motion in liver stereotactic body radiotherapy (SBRT) with exhaled breath holding (BH) based on kilovoltage (kV) cone-beam computed tomography (CBCT) images. We collected 528 pre-treatment kV-CBCT images from 132 patients who underwent liver SBRT under exhaled BH using the Abches system, a non-electronic contact-based respiratory monitoring device, and analyzed them to investigate interfractional liver positional motion. Planning computed tomography (CT) scans were obtained using the Abches system when the patients were under exhaled BH. Translational 3-degree-of-freedom (DOF) soft-tissue-based image registration was performed using the kV-CBCT images under exhaled BH after 6-DOF vertebral bone image registration. Interfractional liver positional motions in the left-right (LR), anteroposterior (AP), and craniocaudal (CC) directions were defined based on the differences in the position of the liver relative to the vertebral bones. For all fractions, the absolute mean±standard deviation for the interfractional liver positional motion in the LR, AP, and CC directions was 0.7±1.0 mm, 1.0±1.5 mm, and 2.8±3.1 mm, respectively. The liver interfractional systematic/random positional motions in the LR, AP, and CC directions were 0.9/1.2 mm, 1.4/1.8 mm, and 2.9/3.9 mm, respectively. For all fractions, 100.0%, 98.3%, and 86.9% of the interfractional liver positional motions in the LR, AP, and CC directions, respectively, were less than 5 mm. CBCT-guided online correction should be used to correct interfractional liver positions errors present in liver SBRT with exhaled BH.

Low-Cost IoT Based Wearable Respiratory Sensor for Covid-19 Patients

The three main Covid-19 symptoms are shortness of breath, coughing and fever. Currently, most of the patients who tested positive for COVID-19 are selfquarantined at home. Unfortunately, some home quarantine Covid-19 patients are brought in death to hospital. Therefore, e-health remote patient monitoring systems are needed. Although many e-health monitoring systems are proposed by the researcher, not many dedicated systems are suitable for COVID-19 specifically. Mostly do not have a respiratory rate monitoring function. Furthermore, many e-health devices in the market only feature local data storage and do not include Internet of Things (IoT) integration. In this work, we proposed a low-cost IoT based respiratory sensor for home quarantine Covid-19 patients to monitor the respiratory rate. The measured respiratory rate will be transmitted to Google Clould via WiFi connection and the user can read it through their computer or smartphone. Alert message will be generated if the respiratory rate reaches an unsafe threshold. The proposed device was tested with five samples and gave a 100% accuracy on respiratory rate measurement. The proposed prototype cost is much lower than the other respiratory monitoring devices in the market. The proposed device could reduce the mortality of home quarantine Covid19 patients

3D Printable One‐Part Carbon Nanotube‐Elastomer Ink for Health Monitoring Applications

Abstract3D printing of conductive elastomers is a promising route to personalized health monitoring applications due to its flexibility and biocompatibility. Here, a one‐part, highly conductive, flexible, stretchable, 3D printable carbon nanotube (CNT)‐silicone composite is developed and thoroughly characterized. The one‐part nature of the inks: i) enables printing without prior mixing and cures under ambient conditions; ii) allows direct dispensing at ≈100 µm resolution printability on nonpolar and polar substrates; iii) forms both self‐supporting and high‐aspect‐ratio structures, key aspects in additive biomanufacturing that eliminate the need for sacrificial layers; and iv) lends efficient, reproducible, and highly sensitive responses to various tensile and compressive stimuli. The high electrical and thermal conductivity of the CNT‐silicone composite is further extended to facilitate use as a flexible and stretchable heating element, with applications in body temperature regulation, water distillation, and dual temperature sensing and Joule heating. Overall, the facile fabrication of this composite points to excellent synergy with direct ink writing and can be used to prepare patient‐specific wearable electronics for motion detection and cardiac and respiratory monitoring devices and toward advanced personal health tracking and bionic skin applications.

Feasibility Analysis and Implementation of Head-Mounted Electrical Impedance Respiratory Monitoring.

The respiratory rate is one of the crucial indicators for monitoring human physiological health. The purpose of this paper was to introduce a head-mounted respiratory monitoring solution based on electrical impedance sensing. Firstly, we constructed a finite element model to analyze the feasibility of using head impedance for respiratory sensing based on the physiological changes in the pharynx. After that, we developed a circuit module that could be integrated into a head-mounted respiratory monitoring device using a bioelectrical impedance sensor. Furthermore, we combined adaptive filtering and respiratory tracking algorithms to develop an app for a mobile phone. Finally, we conducted controlled experiments to verify the effectiveness of this electrical impedance sensing system for extracting respiratory rate. We found that the respiration rates measured by the head-mounted electrical impedance respiratory monitoring system were not significantly different from those of commercial respiratory monitoring devices by a paired t-test (p > 0.05). The results showed that the respiratory rates of all subjects were within the 95% confidence interval. Therefore, the head-mounted respiratory monitoring scheme proposed in this paper was able to accurately measure respiratory rate, indicating the feasibility of this solution. In addition, this respiratory monitoring scheme helps to achieve real-time continuous respiratory monitoring, which can provide new insights for personalized health monitoring.

TREADMILL STRESS TEST VENTILATORY PATTERN USING A WEARABLE DEVICE AS AN ADDITIONAL MARKER FOR CV DISEASE

TREADMILL STRESS TEST VENTILATORY PATTERN USING A WEARABLE DEVICE AS AN ADDITIONAL MARKER FOR CV DISEASE

Paper-Based Humidity Sensor for Respiratory Monitoring.

Flexible respiratory monitoring devices have become available for outside-hospital application scenarios attributable to their improved system wearability. However, the complex fabrication process of such flexible devices results in high prices, limiting their applications in real-life scenarios. This study proposes a flexible, low-cost, and easy-processing paper-based humidity sensor for sleep respiratory monitoring. A paper humidity sensing model was established and sensors under different design parameters were processed and tested, achieving high sensitivity of 5.45 kΩ/%RH and good repeatability with a matching rate of over 85.7%. Furthermore, the sensor patch with a dual-channel 3D structure was designed to distinguish between oral and nasal breathing from origin signals proved in the simulated breathing signal monitoring test. The sensor patch was applied in the sleep respiratory monitoring of a healthy volunteer and an obstruct sleep apnea patient, demonstrating its ability to distinguish between different respiratory patterns as well as various breathing modes.