Validity analysis of respiratory events of polysomnography using a plethysmography chest and abdominal belt.

PURPOSE: Respiratory inductive plethysmography (RIP) is recommended as an alternative respiratory sensor for the identification of each apnea and hypopnea event in polysomnography. Using this sensor, the cumulative RIP results from the chest and abdomen (RIP sum) and time-derived results of the RIP sum (RIP flow) are calculated to track respiratory flow. However, the effectiveness of this sensor and the calculated respiratory results is still unclear, and validation studies for the scoring of respiratory events in polysomnography are rare. METHODS: Two hundred subjects were selected according to the severity of obstructive sleep apnea. A sleep specialist re-evaluated the respiratory events based on RIP flow data in a single-blind study. Statistical analysis was conducted with paired respiratory events scored in each of the RIP flow and polysomnography datasets. RESULTS: All respiratory events scored from the RIP flow were strongly correlated with those identified with standard sensors of polysomnography, regardless of disease severity. Most of the respiratory parameters from RIP flow trended toward underestimation. The RIP flow obtained from the alternative RIP sensor was appropriate for the diagnosis of obstructive sleep apnea based on a receiver operating characteristic curve. CONCLUSIONS: Scored respiratory events from RIP flow data effectively reflected the respiratory flow and statistically correlated with the results from standard polysomnography sensors. Therefore, analyzing RIP flow utilizing an RIP sensor is considered a reliable method for respiratory event scoring.

Development of Embroidery-Type Sensor Capable of Detecting Respiration Using the Capacitive Method.

This study presents a respiration sensor that is dependent on a parallel capacitor, including connection lines and electrodes embroidered on textiles. First, characterizations of the respiration capacitor using a silver thread, including a combination of porous Eco-flex simulating air in the lungs due to respiration, were evaluated using an LCR meter. Second, the effects of air gaps on the detection of respiration motions according to the change in electrode distance under pressure were presented. The data values were measured from 1 to 300 kHz using an LCR meter and dielectric test fixture. Third, actual breathing was examined in four patterns: normal breathing, deep breathing, hyperventilation, and apnea. The test was performed after fabricating a clothing-type breathing sensor. Finally, the change in capacitance for actual respiration was determined by wearing a clothing-type respiration sensor based on the data collected. The effectiveness of the respiration sensor was demonstrated by measuring it to discern all waveforms, cycles, and ranges associated with the breathing pattern.

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.

Novel findings of respiratory rate increases using the multisensor HeartLogic heart failure monitoring algorithm in COVID-19-positive patients: a case series.

BACKGROUND: With the ongoing coronavirus disease 2019 (COVID-19) epidemic, remote monitoring of patients with implanted cardiac devices has become more important than ever, as physical distancing measures have placed limits on in-clinic device monitoring. Remote monitoring alerts, particularly those associated with heart failure trends, have proved useful in guiding care in regard to monitoring fluid status and adjusting heart failure medications. CASE SUMMARY: This report describes use of Boston Scientific's HeartLogic algorithm, which is a multisensor device algorithm in implantable cardioverter-defibrillator devices that is proven to be an early predictor of heart failure decompensation by measuring several variables, including respiratory rate, nighttime heart rate, and heart sounds. We present three cases of patients who were actively surveilled by the various HeartLogic device algorithm sensors and were identified to have increasing respiratory rates high enough to trigger a HeartLogic alert prior to a positive COVID-19 diagnosis. DISCUSSION: We propose that the HeartLogic algorithm and its accompanying individual physiologic sensors demonstrate potential for use in identifying non-heart failure-related decompensation, such as COVID-19-positive diagnoses.

Respiratory Motion Sensor Measuring Capacitance Constructed across Skin in Daily Activities.

In this work, a respiratory sensor is studied, measuring the capacitance constructed by attached electrodes on the abdomen. Based on previous findings, that skin thickness changes caused by respiration provides the signal, the fitting condition of the electrode on the skin is stabilized using a 7-µm-thick dressing film. This film can be comfortably worn for a long time, while maintaining the electrode's position on the skin. This stabilized setup enables the detection of, not only respiration, as the cyclic capacitance change, but also of minute body volume changes over the daytime, as a change in the base line indicates the quality of the sensor signal. For this demonstration, the respiration signal is measured during the daily activity of exercise and 6-min walks.