Continuous Respiratory Monitoring Research Articles

Polysomnography findings and respiratory muscle function in infants with early onset spinal muscular atrophy after gene replacement as monotherapy: A prospective study

BackgroundGene replacement therapy (onasemnogene abeparvovec) is associated with an improvement of the prognosis of children with spinal muscular atrophy, but information on long-term respiratory outcome is scarce. The aim of this study was to report the polysomnography findings and respiratory muscle function of infants with treatment-naive spinal muscular atrophy type 1 and 2 up to 24 months after onasemnogene abeparvovec monotherapy. MethodsA clinical and motor evaluation, respiratory muscle function testing, and polysomnography were performed repeatedly. ResultsFifteen spinal muscular atrophy patients (1 presymptomatic, 7 type 1b, 6 type 1c, and 1 type 2) were included at a median age of 8.6 months (range 3.8–12.6) and followed for 24 months. The thoracic over head circumference ratio was close to normal at baseline (median 1.00 (range 0.90–1.05)) and increased significantly over time. All polysomnography and nocturnal gas exchange parameters were within normal ranges at baseline (median apnea-hypopnea index 2.5 events/hour (range 0.4–5.3)) and follow-up. The inspiratory muscle strength was normal at baseline but tended to slightly decrease over time and the expiratory muscle strength was low at any time especially for patients with recurrent respiratory infections (median (range) at baseline in cmH2O: crying esophageal pressure 54 (30–110), crying transdiaphragmatic pressure 65 (35–107), gastric pressure during maximal cough 26 (10–130), esophageal pressure during maximal cough 61 (38–150)). Only 3 patients required noninvasive ventilation. ConclusionA continuous respiratory monitoring of spinal muscular atrophy patients during the first years of life following onasemnogene abeparvovec monotherapy seems recommended despite the normality of polysomnography parameters.

Clinical Interventions Following Escalations from a Continuous Respiratory Monitoring Service in Patients With Chronic Obstructive Pulmonary Disease.

Continuous respiratory monitoring can support integrated care for chronic obstructive pulmonary disease (COPD) patients, by coupling them with remote clinical personnel who triage patients in coordination with their health care providers. When deploying such services, there remains uncertainty surrounding outcomes when at-risk patients are proactively identified and escalated for provider evaluation. This study presents findings from a service deployed in a real-world COPD cohort by analyzing the clinical interventions made during in-person and telehealth pulmonary outpatient visits following remote escalations. A single-center, retrospective, observational study of real-world COPD patients at a multi-site pulmonary practice was conducted. Patients who were enrolled in a continuous respiratory monitoring service for at least one year and were seen by a provider within seven days of an escalation by the service (N=168) were included. To evaluate the potential impact of these escalations on provider and patient burden, medical charts from outpatient visits were manually reviewed and grouped into six categories based on the clinical action(s) taken by the provider. A total of 245 outpatient visits occurred from 168 patients within seven days of escalation. Of the 245 visits, 206 (84.1%) resulted in clinical intervention and 163 (66.5%) resulted in treatment consistent with acute exacerbations of COPD (AECOPDs). 1.6% of the outpatient visits resulted in referral to the emergency room. Provider encounters occurring following the escalation of a patient from a continuous respiratory monitoring service consistently resulted in that provider administering a treatment to the escalated patient.

Screen-printed wearable sensors for continuous respiratory rate monitoring: fabrication, clinical evaluation, and point-of-care potential

A screen-printed wearable sensor accurately monitored respiratory rate in 15 participants with a 0.055 bpm error, showing promise for point-of-care use in conditions like COPD and COVID-19.

Early detection of deterioration in COVID-19 patients by continuous ward respiratory rate monitoring: a pilot prospective cohort study.

Tachypnea is among the earliest signs of pulmonary decompensation. Contactless continuous respiratory rate monitoring might be useful in isolated COVID-19 patients admitted in wards. We therefore aimed to determine whether continuous monitoring of respiratory patterns in hospitalized patients with COVID-19 predicts subsequent need for increased respiratory support. Single-center pilot prospective cohort study in COVID-19 patients who were cared for in routine wards. COVID-19 patients who had at least one escalation of pulmonary management were matched to three non-escalated patients. Contactless respiratory monitoring was instituted after patients enrolled, and continued for 15 days unless hospital discharge, initiation of invasive mechanical ventilation, or death occurred. Clinicians were blinded to respiratory rate data from the continuous monitor. The exposures were respiratory features over rolling periods of 30 min, 24 h, and 72 h before respiratory care escalation. The primary outcome was a subsequent escalation in ventilatory support beyond a Venturi mask. Among 125 included patients, 13 exhibited at least one escalation and were each matched to three non-escalated patients. A total of 28 escalation events were matched to 84 non-escalation episodes. The 30-min mean respiratory rate in escalated patients was 23 breaths per minute (bpm) ranging from 13 to 40 bpm, similar to the 22 bpm in non-escalated patients, although with less variability (range 14 to 31 bpm). However, higher respiratory rate variability, especially skewness over 1 day, was associated with higher incidence of escalation events. Our overall model, based on continuous data, had a moderate accuracy with an AUC 0.81 (95%CI: 0.73, 0.88) and a good specificity 0.93 (95%CI: 0.87, 0.99). Our pilot observational study suggests that respiratory rate variability as detected with continuous monitoring is associated with subsequent care escalation during the following 24 h. Continuous respiratory monitoring thus appears to be a valuable increment over intermittent monitoring. Our study was the initial evaluation of Circadia contactless respiratory monitoring in COVID-19 patients who are at special risk of pulmonary deterioration. The major limitation is that the analysis was largely post hoc and thus needs to be confirmed in an out-of-sample population.

Wearable Graphene-based smart face mask for Real-Time human respiration monitoring

After the pandemic of SARS-CoV-2, the use of face-masks is considered the most effective way to prevent the spread of virus-containing respiratory fluid. As the virus targets the lungs directly, causing shortness of breath, continuous respiratory monitoring is crucial for evaluating health status. Therefore, the need for a smart face mask (SFM) capable of wirelessly monitoring human respiration in real-time has gained enormous attention. However, some challenges in developing these devices should be solved to make practical use of them possible. One key issue is to design a wearable SFM that is biocompatible and has fast responsivity for non-invasive and real-time tracking of respiration signals. Herein, we present a cost-effective and straightforward solution to produce innovative SFMs by depositing graphene-based coatings over commercial surgical masks. In particular, graphene nanoplatelets (GNPs) are integrated into a polycaprolactone (PCL) polymeric matrix. The resulting SFMs are characterized morphologically, and their electrical, electromechanical, and sensing properties are fully assessed. The proposed SFM exhibits remarkable durability (greater than1000 cycles) and excellent fast response time (∼42 ms), providing simultaneously normal and abnormal breath signals with clear differentiation. Finally, a developed mobile application monitors the mask wearer's breathing pattern wirelessly and provides alerts without compromising user-friendliness and comfort.

Ultrasensitive Strain Sensor Utilizing a AgF-AgNW Hybrid Nanocomposite for Breath Monitoring and Pulmonary Function Analysis.

Breath monitoring and pulmonary function analysis have been the prime focus of wearable smart sensors owing to the COVID-19 outbreak. Currently used lung function meters in hospitals are prone to spread the virus and can result in the transmission of the disease. Herein, we have reported the first-ever wearable patch-type strain sensor for enabling real-time lung function measurements (such as forced volume capacity (FVC) and forced expiratory volume (FEV) along with breath monitoring), which can avoid the spread of the virus. The noninvasive and highly sensitive strain sensor utilizes the synergistic effect of two-dimensional (2D) silver flakes (AgFs) and one-dimensional (1D) silver nanowires (AgNWs), where AgFs create multiple electron transmission paths and AgNWs generate percolation networks in the nanocomposite. The nanocomposite-based strain sensor possesses a high optimized conductivity of 7721 Sm-1 (and a maximum conductivity of 83,836 Sm-1), excellent stretchability (>1000%), and ultrasensitivity (GFs of 35 and 87 when stretched 0-20 and 20-50%, respectively), thus enabling reliable detection of small strains produced by the body during breathing and other motions. The sensor patching site was optimized to accurately discriminate between normal breathing, quick breathing, and deep breathing and analyze numerous pulmonary functions, including the respiratory rate, peak flow, FVC, and FEV. Finally, the observed measurements for different pulmonary functions were compared with a commercial peak flow meter and a spirometer, and a high correlation was observed, which highlights the practical feasibility of continuous respiratory monitoring and pulmonary function analysis.

Abstract 15880: Detection of an Acute Pulmonary Embolism in the Home Bed Using Adherence-Independent Home Monitoring

Acute pulmonary embolism classically presents with dyspnea, tachypnea, and hypoxia, but self-recognition of symptoms and clinical findings is often challenging. This is particularly true in elder patients or those with cognitive impairments. Digital health technologies offer opportunities to remotely detect pre-symptomatic illness. We present a prodrome of an acute pulmonary embolism as it emerges in the home during continuous respiratory monitoring using a non-contact adherence-independent home bed sensor. The patient is an 85-year-old woman who is morbidly obese, has limited mobility, and multiple comorbidities including a recent diagnosis of atrial fibrillation for which she was prescribed anticoagulation but has not yet initiated. As part of an observational study, nocturnal respiratory rates (NRR) were longitudinally monitored in her home bed using a non-contact, adherence-independent bed sensor. Eight days prior to admission, increasing NRR prompted a patient call to family who noted no acute symptomatic changes. During the call it was discovered she had not been started on apixaban. On subsequent days, persistently elevated NRR prompted a second phone call 4 days prior to admission (PTA), where again no acute symptoms were noted. Anticoagulation still had not been started at that time. NRR worsened further on days following the second call prompting a home visit by an RN who found the patient dyspneic, tachypneic in the high 20s, and hypoxic with oxygen saturations in the 80s, prompting a transfer to the emergency department for further evaluation. She was eventually diagnosed with an acute pulmonary embolism. After a week-long hospitalization, the patient was discharged on adequate anticoagulation therapy.This case suggests that adherence-independent home bed monitoring of nocturnal respiratory rate may enable early detection of chronic volume overload and acute pulmonary embolism, potentially facilitating early intervention.

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.

A Low-Cost Flexible Perforated Respiratory Sensor Based on Platinum for Continuous Respiratory Monitoring.

Monitoring sleep conditions is of importance for sleep quality evaluation and sleep disease diagnosis. Accurate respiration detection provides key information about sleep conditions. Here, we propose a perforated temperature sensor that can be worn below the nasal cavity to monitor breath. The sensing system consists of two perforated temperature sensors, signal conditioning circuits, a transmission module, and a supporting analysis algorithm. The perforated structure effectively enhances the sensitivity of the system and shortens the response time. The sensor’s response time is 0.07 s in air and sensitivity is 1.4‰°C−1. The device can achieve a monitoring respiratory temperature range between normal room temperature and 40 °C. The simple and standard micromachining process ensures low cost and high reproducibility. We achieved the monitoring of different breathing patterns, such as normal breathing, panting, and apnea, which can be applied to sleep breath monitoring and exercise information recording.

A Wearable Bioimpedance Chest Patch for Real-Time Ambulatory Respiratory Monitoring.

This paper aims to introduce a wearable solution and a low-complexity algorithm for real-time continuous ambulatory respiratory monitoring. A wearable chest patch is designed using a bioimpedance (BioZ) sensor to measure the changes in chest impedance caused by breathing. Besides, a medical-grade infrared temperature sensor is utilized to monitor body temperature. The computing algorithm implemented on the patch enables computation of breath-by-breath respiratory rate and chest temperature in real-time. Two wireless communication protocols are included in the system, namely Bluetooth and Long Range (LoRa), which enable both short-range and long-range data transmission. The breathing rate measured in static (i.e., standing, sitting, supine, and lateral lying) and dynamic (i.e., walking, running, and cycling) positions by our device yielded an accuracy of more than 97.8% and 98.5% relative to the ground truth, respectively. Additionally, the device's performance is evaluated in real-world scenarios both indoors and outdoors. The proposed system is capable of measuring breathing rate throughout a variety of daily activities. To the best of our knowledge, this is the first BioZ-based wearable patch capable of detecting breath-by-breath respiratory rate in real-time remotely under unrestricted ambulatory conditions. This study establishes a strategy for continuous respiratory monitoring that could aid in the early detection of cardiopulmonary disorders in everyday life.

Respiratory monitoring of nonintubated patients in nonoperating room settings: old and new technologies.

Postoperative mortality in the 30 days after surgery remains disturbingly high. Inadequate, intermittent and incomplete monitoring of vital signs in the nonoperating room environment is common practice. The rise of nonoperating room anaesthesia and sedation outside the operating room has highlighted the need to develop new and robust methods of portable continuous respiratory monitoring. This review provides a summary of old and new technologies in this environment. Technical advances have made possible the utilization of established monitoring to extrapolate respiratory rate, the increased availability and user friendliness of side stream capnography and the advent of other innovative systems. The use of aggregate signals wherein different modalities compensate for individual shortcomings seem to provide a reliable and artefact-free system. Respiratory monitoring is required in several situations and patient categories outside the operating room. The chosen modality must be able to detect respiratory compromise in a timely and accurate manner. Combing several modalities in a nonobtrusive, nontethered system and having an integrated output seems to give a reliable and responsive signal.

Estimation of Tidal Volume Using Load Cells on a Hospital Bed.

Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifiable respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respiratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respiratory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low-frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings.

A Low-Cost Flexible Perforated Temperature Sensor Based on Platinum for Continuous Respiratory Monitoring

A Low-Cost Flexible Perforated Temperature Sensor Based on Platinum for Continuous Respiratory Monitoring

RF-RVM: Continuous Respiratory Volume Monitoring With COTS RFID Tags

Continuous and accurate respiratory volume monitoring is crucial in many healthcare-related applications. Traditional respiratory volume monitoring approaches involve obtrusive devices that are uncomfortable for long-term monitoring, while unobtrusive approaches mainly focus on sensing the respiratory rate, which is insufficient for many healthcare-related applications. In this article, we present radio-frequency respiratory volume monitoring (RF-RVM), an unobtrusive system to sense the respiratory volume based on commercial off-the-shelf (COTS) RFID devices. Specifically, RF-RVM continuously collects the temporal phase information from tags attached to the chest and abdomen to extract the chest displacement and abdomen displacement caused by respiration. Then, we assess the respiratory volume by training a backpropagation neural network model to correlate chest and abdomen displacements and respiratory volume. We use a reference tag attached under the user's neck to eliminate the noise caused by slight movements of the upper body during respiration. We implement and evaluate RF-RVM based on COTS RFID devices. The experimental results show that RF-RVM can continuously monitor user's respiratory volume with an average accuracy of 94.52% for leave-one-session-out cross-validation and 91.96% for leave-one-record-out cross-validation based on a data set sampled from 20 volunteers.

Application of a continuous respiratory sound monitoring system in thoracic surgery

Application of a continuous respiratory sound monitoring system in thoracic surgery

Respiratory Monitoring Based on Tracheal Sounds: Continuous Time-Frequency Processing of the Phonospirogram Combined with Phonocardiogram-Derived Respiration.

Patients with central respiratory paralysis can benefit from diaphragm pacing to restore respiratory function. However, it would be important to develop a continuous respiratory monitoring method to alert on apnea occurrence, in order to improve the efficiency and safety of the pacing system. In this study, we present a preliminary validation of an acoustic apnea detection method on healthy subjects data. Thirteen healthy participants performed one session of two 2-min recordings, including a voluntary respiratory pause. The recordings were post-processed by combining temporal and frequency detection domains, and a new method was proposed—Phonocardiogram-Derived Respiration (PDR). The detection results were compared to synchronized pneumotachograph, electrocardiogram (ECG), and abdominal strap (plethysmograph) signals. The proposed method reached an apnea detection rate of 92.3%, with 99.36% specificity, 85.27% sensitivity, and 91.49% accuracy. PDR method showed a good correlation of 0.77 with ECG-Derived Respiration (EDR). The comparison of R-R intervals and S-S intervals also indicated a good correlation of 0.89. The performance of this respiratory detection algorithm meets the minimal requirements to make it usable in a real situation. Noises from the participant by speaking or from the environment had little influence on the detection result, as well as body position. The high correlation between PDR and EDR indicates the feasibility of monitoring respiration with PDR.

Postoperative respiratory state assessment using the Integrated Pulmonary Index (IPI) and resultant nurse interventions in the post-anesthesia care unit: a randomized controlled trial

Although postoperative adverse respiratory events, defined by a decrease in respiratory rate (RR) and/or a drop in oxygen saturation (SpO2), occur frequently, many of such events are missed. The purpose of the current study was to assess whether continuous monitoring of the integrated pulmonary index (IPI), a composite index of SpO2, RR, end-tidal PCO2 and heart rate, alters our ability to identify and prevent adverse respiratory events in postoperative patients. Eighty postoperative patients were subjected to continuous respiratory monitoring during the first postoperative night using RR and pulse oximetry and the IPI monitor. Patients were randomized to receive intervention based on standard care (observational) or based on the IPI monitor (interventional). Nurses were asked to respond to adverse respiratory events with an intervention to improve the patient’s respiratory condition. There was no difference in the number of patients that experienced at least one adverse respiratory event: 21 and 16 in observational and interventional group, respectively (p = 0.218). Compared to the observational group, the use of the IPI monitor led to an increase in the number of interventions performed by nurses to improve the respiratory status of the patient (average 13 versus 39 interventions, p < 0.001). This difference was associated with a significant reduction of the median number of events per patient (2.5 versus 6, p < 0.05) and a shorter median duration of events (62 s versus 75 s, p < 0.001). The use of the IPI monitor in postoperative patients did not result in a reduction of the number of patients experiencing adverse respiratory events, compared to standard clinical care. However, it did lead to an increased number of nurse interventions and a decreased number and duration of respiratory events in patients that experienced postoperative adverse respiratory events.

A Home Telemedicine System for Continuous Respiratory Monitoring.

This article presents a continuous home telemonitoring system for chronic respiratory patients using 5G connectivity developed in partnership with Vodafone as a part of the 5G Trial in Milan established by the Italian Ministry of Economic Development. The system features a wearable respiratory and activity monitor, an environmental sensor and a pulse oximeter sending the data through a 5G router to a Multi-Edge Computing server, incorporated in the Vodafone 5G infrastructure, where they are stored and accessible for visualization. In particular, activity, respiratory and environmental data are continuously streamed and collected. The solution has been tested on 18 healthy volunteers during non-supervised recordings lasting at least 48 hours. The combination of recognized activities and associated respiratory parameters provided statistically significant variations in breathing patterns between one activity and the other, thus giving more complete information to the clinicians than previously studied telemedicine systems based on spot-checks. In particular, statistically significant differences are found in tidal volume and minute ventilation between horizontal and vertical postures (p < 0.001) and between vertical postures and dynamic activities (p < 0.001); the respiratory rate shows statistically significant differences between horizontal and vertical postures (p < 0.001). Some environmental parameters have different mean values between day and night, such as carbon dioxide (p < 0.001). Trials on patients are needed to further study this telemedicine solution and make it commercially available in the future. The main further technical development suggested is the use of commercial 5G smartphones as routers, in order to make the system usable outside of home settings.

A report of three cancer patients on opioid analgesia receiving spinal anesthesia: abrupt pain elimination without respiratory depression

BackgroundComplete removal of pain with regional anesthesia has been reported to cause fatal respiratory depression in opioid-dependent patients, which leads us to choose general anesthesia. We hereby report three cases of chronically opioid-treated cancer patients operated under spinal anesthesia without respiratory event.Case presentationCase 1: a 32-year-old female treated with high-dose morphine for her cancer pain was planned for cesarean section. Case 2: a 65-year-old female on moderate dose of oxycodone was planned for surgery of her femoral bone fracture. Case 3: a 65-year-old male on low-dose oxycodone was planned for intramedullary nailing for metastatic femoral bone tumor. In all three cases, spinal anesthesia was chosen. Continuous respiratory monitoring revealed no apnea or bradypnea.ConclusionSpinal anesthesia was safely performed without respiratory depression in chronic opioid users for cancer pain.

Skin‐Like Hybrid Integrated Circuits Conformal to Face for Continuous Respiratory Monitoring

AbstractTo perform wearable and continuous health monitoring, epidermal electronics for all kinds of sensing have been developed, yet neither organic nor inorganic materials‐based epidermal devices are able to perform the functions like signal collection, processing, transmission, or power supply. Here, a strategy is reported to build skin‐like hybrid integrated circuits (SHICs) with stretchable sensors and commercial chips for long‐term respiratory monitoring. It can independently perform signal acquisition, processing, converting, and wireless transmission with carrying power supply. Thermal theory model is established to characterize the breathing air's interaction with the sensor, which helps in signal analysis. Respiratory monitoring on human body is tested in diverse daily activities including exercising, dining, drinking, and sleeping with a custom‐developed phone application to record and display the respiratory signal. Results show that the SHIC is of high efficiency and accuracy to track the breath patterns continuously in both static and moving scenarios. Furthermore, the SHIC is a feasible and promising platform that can be extended with multiple sensors or actuators in individualized medical applications and physiological study.