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

Abstract

Background 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). Methods 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. Results 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. Conclusions 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.

Design Implementation and Evaluation of a Mobile Continuous Blood Oxygen Saturation Monitoring System

“…In this paper, we introduce a wireless, smartphone-based, continuous SpO2 monitoring system that aims to supplement cvrPhone, a medical-grade, smartphone-based cardiac and respiratory monitoring platform [ 6 , 7 , 8 ]. To avoid an invasive measurement directly from a blood sample, we have researched and developed a PPG-based estimation approach.…”

“…SpO2 monitoring is an important vital sign in evaluating and monitoring the cardio-pulmonary function of both in-hospital and out-of-hospital patients [2][3][4]. As the average age of the US population increases and chronic conditions are becoming more prevalent, there is a need to improve the effectiveness of disease prevention, to enhance access to healthcare, and to sustain healthy independent living [5][6][7][8]. Mobile health technologies are expected to function Figure 1.…”

“…SpO2 monitoring is an important vital sign in evaluating and monitoring the cardio-pulmonary function of both in-hospital and out-of-hospital patients [ 2 , 3 , 4 ]. As the average age of the US population increases and chronic conditions are becoming more prevalent, there is a need to improve the effectiveness of disease prevention, to enhance access to healthcare, and to sustain healthy independent living [ 5 , 6 , 7 , 8 ]. Mobile health technologies are expected to function not only as monitoring devices, but also as essential components in the delivery of healthcare to those with chronic diseases [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], especially in under-served populations.…”

Ambulatory monitoring promises equitable personalized healthcare delivery in underrepresented patients

“…We tested the utility of our smartphone-based system, cvrPhone, in performing pulmonary assessment by estimating the RR, TV, MV, and onset of apnoeic events in real-time ( n = 9 swine). 21 Figure 4 depicts a representative plot of RR, TV, and MV estimation as the true RR was varied from 6 to 0 to 6 to 0 to 6 to 0 to 6 breaths/min, TV was varied from 250 to 0 to 750 to 0 to 500 to 0 to 750 mL, and MV was varied from 1500 to 0 to 4500 to 0 to 3000 to 0 to 4500 breaths*mL/min ( n = 9 swine). The smartphone-based system accurately estimated TV at different settings (0, 250, 500, and 750 mL) with statistically significant difference ( P < 0.01) between any two different settings regardless of the RR (6 or 14 b.p.m.).…”

Clinical Potential of Beat‐to‐Beat Diastolic Interval Control in Preventing Cardiac Arrhythmias

“…To further probe the translational potential of constant DI pacing, we recently developed and evaluated a novel closed‐loop method to dynamically modulate TWA in an in vivo myocardial infarction swine model. 26 , 40 , 75 , 76 The real‐time control system is capable of recording and analyzing TWA from multiple intracardiac leads 11 and delivering stimuli at a fixed interval on a beat‐by‐beat basis on detection of the T wave. 77 In addition, the algorithm has the ability to identify and exclude/replace abnormal beats (ie, premature ventricular complexes and extrasystoles) based on a template matching technique.…”