Research Article: Utility of a smartphone based system (cvrphone) to accurately determine apneic events from electrocardiographic signals

Date Published: June 17, 2019

Publisher: Public Library of Science

Author(s): Kwanghyun Sohn, Faisal M. Merchant, Shady Abohashem, Kanchan Kulkarni, Jagmeet P. Singh, E. Kevin Heist, Chris Owen, Jesse D. Roberts, Eric M. Isselbacher, Furrukh Sana, Antonis A. Armoundas, Moshe Swissa.

http://doi.org/10.1371/journal.pone.0217217

Abstract

Sleep 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).

Twelve 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.

TV 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. 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 SA in conjunction with ECG monitoring.

Partial Text

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]. RR and/or TV can be measured using a number of different methods, such as spirometer [3], Pitot tube [4], respiratory inductance plethysmography [5], impedance plethysmography [6], and computed tomography [7]. In the clinical setting, specialized hardware employing these standard techniques provide efficient measurement of RR and TV, in ambulatory setting their bulkiness often makes patient monitoring of these parameters, impractical.

The RR and TV estimation depends on the HR (the HR corresponds to the sampling rates for the TV and RR estimation algorithms). In this study, the HR was 114±11 beats per minute.

The presence of SA has a significant negative impact on prognosis across many disease states but despite the availability of effective treatment, SA remains substantially underdiagnosed, and as a result, undertreated. To decrease the barrier to SA evaluation, we have developed a smartphone-based cardio-respiratory monitoring system, namely cvrPhone, that monitors RR and TV [17]. In this study, we tested the performance of the RR/TV estimation algorithms of the cvrPhone in diagnosing apnea. The results support that our algorithms can first, estimate the RR with an accuracy of 1 breath/min using only 2 ECG leads, ~91% of the time; second, estimate the TV with an accuracy of less than 105 ml using all 12 ECG leads, ~75% of the time; and third, detect apnea within ~7–8 seconds.

 

Source:

http://doi.org/10.1371/journal.pone.0217217

 

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