Kanchan Kulkarni1, Navchetan Awasthi1, Jesse D Roberts1,2, Antonis A Armoundas1,3. 1. Cardiovascular Research Center, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA. 2. Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA. 3. Institute for Medical Engineering and Science, Massachusetts Institute of Technology Cambridge, Massachusetts, USA.
Abstract
Background: We investigated the ability of a novel stand-alone, smartphone-based system, the cvrPhone, in estimating the minute ventilation (MV) from body surface electrocardiographic (ECG) signals. Methods: Twelve lead ECG signals were collected from anesthetized and mechanically ventilated swine (n = 9) using standard surface electrodes and the cvrPhone. The tidal volume delivered to the animals was varied between 0, 250, 500, and 750 mL at respiration rates of 6 and 14 breaths/min. MV estimates were determined by the cvrPhone and were compared with the delivered ones. Results: The median relative estimation errors were 17%, -4%, 35%, -3%, -9%, and 1%, for true MVs of 1,500, 3,000, 3,500, 4,500, 7,000, and 10,500 breaths*mL/min, respectively. The MV estimates at each of the settings were significantly different from each other (p < 0.05). Conclusions: We have demonstrated that accurate MV estimations can be derived from standard body surface ECG signals, using a smartphone.
Background: We investigated the ability of a novel stand-alone, smartphone-based system, the cvrPhone, in estimating the minute ventilation (MV) from body surface electrocardiographic (ECG) signals. Methods: Twelve lead ECG signals were collected from anesthetized and mechanically ventilated swine (n = 9) using standard surface electrodes and the cvrPhone. The tidal volume delivered to the animals was varied between 0, 250, 500, and 750 mL at respiration rates of 6 and 14 breaths/min. MV estimates were determined by the cvrPhone and were compared with the delivered ones. Results: The median relative estimation errors were 17%, -4%, 35%, -3%, -9%, and 1%, for true MVs of 1,500, 3,000, 3,500, 4,500, 7,000, and 10,500 breaths*mL/min, respectively. The MV estimates at each of the settings were significantly different from each other (p < 0.05). Conclusions: We have demonstrated that accurate MV estimations can be derived from standard body surface ECG signals, using a smartphone.
Entities:
Keywords:
e-health; electrocardiographic signals; minute ventilation; smartphone-based system
Authors: Omid Sayadi; Faisal M Merchant; Dheeraj Puppala; Theofanie Mela; Jagmeet P Singh; E Kevin Heist; Chris Owen; Antonis A Armoundas Journal: Circ Arrhythm Electrophysiol Date: 2013-07-24
Authors: Kwanghyun Sohn; Faisal M Merchant; Omid Sayadi; Dheeraj Puppala; Rajiv Doddamani; Ashish Sahani; Jagmeet P Singh; E Kevin Heist; Eric M Isselbacher; Antonis A Armoundas Journal: Sci Rep Date: 2017-03-22 Impact factor: 4.379