Abstract
BackgroundSleep disordered breathing manifested as sleep apnea (SA) is prevalent in the general population, and while it is associated with increased morbidity and mortality risk in some patient populations, it remains under-diagnosed. The objective of this study was to assess the accuracy of respiration-rate (RR) and tidal-volume (TV) estimation algorithms, from body-surface ECG signals, using a smartphone based ambulatory respiration monitoring system (cvrPhone).MethodsTwelve lead ECG signals were collected using the cvrPhone from anesthetized and mechanically ventilated swine (n = 9). During ECG data acquisition, the mechanical ventilator tidal-volume (TV) was varied from 250 to 0 to 750 to 0 to 500 to 0 to 750 ml at respiratory rates (RR) of 6 and 14 breaths/min, respectively, and the RR and TV values were estimated from the ECG signals using custom algorithms.ResultsTV estimations from any two different TV settings showed statistically significant difference (p < 0.01) regardless of the RR. RRs were estimated to be 6.1±1.1 and 14.0±0.2 breaths/min at 6 and 14 breaths/min, respectively (when 250, 500 and 750 ml TV settings were combined). During apnea, the estimated TV and RR values were 11.7±54.9 ml and 0.0±3.5 breaths/min, which were significantly different (p<0.05) than TV and RR values during non-apnea breathing. In addition, the time delay from the apnea onset to the first apnea detection was 8.6±6.7 and 7.0±3.2 seconds for TV and RR respectively.ConclusionsWe have demonstrated that apnea can reliably be detected using ECG-derived RR and TV algorithms. These results support the concept that our algorithms can be utilized to detect SA in conjunction with ECG monitoring.
Highlights
We have demonstrated that apnea can reliably be detected using ECG-derived RR and TV algorithms
These results support the concept that our algorithms can be utilized to detect sleep apnea (SA) in conjunction with ECG monitoring
Respiration rate (RR) and tidal volume (TV) monitoring are an essential component of patient care in emergency rooms, intensive care units and they are employed during mechanical ventilation of patients with acute lung injury, acute respiratory distress syndrome, etc. [1], [2]
Summary
Respiration rate (RR) and tidal volume (TV) monitoring are an essential component of patient care in emergency rooms, intensive care units and they are employed during mechanical ventilation of patients with acute lung injury, acute respiratory distress syndrome, etc. [1], [2]. Recent advances in hardware technology and software processing algorithms have enabled the development of much smaller, light-weight, and reliable systems for ambulatory cardiorespiratory monitoring [8]. These systems can include standalone wearable and implantable sensor devices [9] that can be integrated with an application (app) [10], [11] for acquisition, processing, and monitoring of the data. Non-invasive techniques targeting ambulatory monitoring of RR and TV have been developed that have resulted in portable devices incorporated into garments [13], [14], [15] These systems incorporate algorithms that are optimized to record and interpret ECG signals, it is desirable to develop advanced signal processing methods that can utilize these cardiac signals to measure and analyze other physiological parameters. The objective of this study was to assess the accuracy of respiration-rate (RR) and tidal-volume (TV) estimation algorithms, from body-surface ECG signals, using a smartphone based ambulatory respiration monitoring system (cvrPhone)
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