Utility of a Smartphone Based System (cvrphone) to Accurately Determine Apneic Events from Electrocardiographic Signals

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2019 Jun 18

PMID 31206522

Citations 5

Authors

Kwanghyun Sohn

Faisal M Merchant

Shady Abohashem

Kanchan Kulkarni

Jagmeet P Singh

E Kevin Heist

Chris Owen

Jesse D Roberts Jr

Eric M Isselbacher

Furrukh Sana

Antonis A Armoundas

Affiliations

Soon will be listed here.

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.

Citing Articles

Ambulatory monitoring promises equitable personalized healthcare delivery in underrepresented patients.

Kulkarni K, Sevakula R, Kassab M, Nichols J, Roberts Jr J, Isselbacher E Eur Heart J Digit Health. 2021; 2(3):494-510.

PMID: 34604759 PMC: 8482046. DOI: 10.1093/ehjdh/ztab047.

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

Kulkarni K, Walton R, Armoundas A, Tolkacheva E J Am Heart Assoc. 2021; 10(11):e020750.

PMID: 34027678 PMC: 8483541. DOI: 10.1161/JAHA.121.020750.

Utility of a Smartphone-Based System (cvrPhone) in Estimating Minute Ventilation from Electrocardiographic Signals.

Kulkarni K, Awasthi N, Roberts Jr J, Armoundas A Telemed J E Health. 2021; 27(12):1433-1439.

PMID: 33729001 PMC: 8742262. DOI: 10.1089/tmj.2020.0507.

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

Zhang Q, Arney D, Goldman J, Isselbacher E, Armoundas A Sensors (Basel). 2020; 20(22).

PMID: 33217945 PMC: 7698638. DOI: 10.3390/s20226581.

Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review.

Sana F, Isselbacher E, Singh J, Heist E, Pathik B, Armoundas A J Am Coll Cardiol. 2020; 75(13):1582-1592.

PMID: 32241375 PMC: 7316129. DOI: 10.1016/j.jacc.2020.01.046.

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