A pilot study of the nocturnal respiration rates in COPD patients in the home environment using a non-contact biomotion sensor

Abstract

Nocturnal respiration rate parameters were collected from 20 COPD subjects over an 8 week period, to determine if changes in respiration rate were associated with exacerbations of COPD. These subjects were primarily GOLD Class 2 to 4, and had been recently discharged from hospital following a recent exacerbation. The respiration rates were collected using a non-contact radio-frequency biomotion sensor which senses respiratory effort and body movement using a short-range radio-frequency sensor. An adaptive notch filter was applied to the measured signal to determine respiratory rate over rolling 15 s segments. The accuracy of the algorithm was initially verified using ten manually-scored 15 min segments of respiration extracted from overnight polysomnograms. The calculated respiration rates were within 1 breath min(-1) for >98% of the estimates. For the 20 subjects monitored, 11 experienced one or more subsequent exacerbation of COPD (ECOPD) events during the 8 week monitoring period (19 events total). Analysis of the data revealed a significant increase in nocturnal respiration rate (e.g. >2 breath min(-1)) prior to many ECOPD events. Using a simple classifier of a change of 1 breath min(-1) in the mode of the nocturnal respiration rate, a predictive rule showed a sensitivity of 63% and specificity of 85% for predicting an exacerbation within a 5 d window. We conclude that it is possible to collect respiration rates reliably in the home environment, and that the respiration rate may be a potential indicator of change in clinical status.

Performance of Contactless Respiratory Rate Monitoring by Albus HomeTM, an Automated System for Nocturnal Monitoring at Home: A Validation Study

“…In monitoring device validation, it is desired to assess both intersubject and intrasubject variation, ideally encompassing repeatability and reliability across multiple nights. A limitation of this study (but one shared by precedent validation studies involving polygraphy [ 8 , 15 ]) is that each participant undertook one night of monitoring with both the Albus system and reference. This was in large part due to the practical limitations of the gold-standard polygraphy, which is burdensome and poorly suitable for multiple nights, especially for children, but also a study design choice where given the same resource, it was hypothesised that performance was more likely to vary between different participants, rather than the accuracy for a given participant’s night significantly changing if it was repeated for the same person.…”

“…Accuracy was analysed and reported for periods starting from when each participant was in bed and there were ten minutes of respiratory data without gross motion from both the Albus system and polygraphy. As per precedent validation methodology [ 15 ], a 15-minute period was counted and compared for each participant to assess the inter- and intrasubject performance. A RR reading from the Albus system was defined as accurate when within ±2 breaths per minute of the reference count as per criteria used in previous work [ 8 , 16 ].…”

Machine-learning based feature selection for a non-invasive breathing change detection

“…Some methods, based on self-reported symptoms or manually entered data [6][7][8][9][10] are limited, since they depend on subjective assessment and on patient compliance. Others have been focusing in remote monitoring devices, enabling automatic follow-up of physiological data and reducing the need for intervention for data acquisition by patients or the health team [11][12][13][14][15][16]. Some of the methods described employ an online learning process, that can be considered as a novelty detection approach.…”

Protein-Based Flexible Moisture-Induced Energy-Harvesting Devices As Self-Biased Electronic Sensors

“…The devices are demonstrated to monitor the variation of humidity content on exhaled and inhaled air during breathing without requiring any external power. Therefore, these sensors can be used for continuous monitoring of breathing pattern to detect many critical diseases, such as sleep apnea, asthma, pneumonia, and chronic obstructive pulmonary diseases (COPDs). − Monitoring moisture level at the wound site can also help in deciding a suitable type of dressing and tracking the healing process continuously . These sensors were used to map the lateral variation of moisture level for monitoring wound healing.…”