TY - GEN
T1 - A Method to Detect Pauses for Ventilation during Cardiopulmonary Resuscitation Using the Thoracic Impedance
AU - Rueda, Enrique
AU - Aramendi, Elisabete
AU - Irusta, Unai
AU - Idris, Ahamed H.
N1 - Funding Information:
This work was supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades through grant RTI2018-101475-BI00, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), and by the Basque Government through grants IT1229-19 and PRE 2019 0262.
Publisher Copyright:
© 2020 Creative Commons; the authors hold their copyright.
PY - 2020/9/13
Y1 - 2020/9/13
N2 - Cardiac arrest is the main cause of death in developed countries. A good quality cardiopulmonary resuscitation (CPR) is key for the survival of the patient in out-of-hospital cardiac arrest (OHCA), including chest compressions (CCs) and ventilations. Ventilations have been proven to have an important impact in the outcome of the patient, and detecting the CC pauses where ventilations were provided is the aim of this study. An algorithm that automatically detects pauses between sequences of CCs using machine learning techniques is proposed. For this study a set of 102 defibrillator files from OHCA patients that include the thoracic impedance recorded through the defibrillation pads was used. The work has been split into 2 main blocks: a random forest (RF) classifier that classifies 1-s windows as CC/no-CC and an algorithm that sets the beginning and the end of each detected pause. The RF classifier was validated using 10 fold cross-validation method, obtaining a median sensitivity (Se), specificity (Sp) and positive predictive value (PPV) of 95.4/97. 9/94.4 % respectively, for window classification. The pause detector returned median Se/PPV values of 90.0/91.3 % with a median pause delimitation error of 0.04 s and a duration error of 0.04 s.
AB - Cardiac arrest is the main cause of death in developed countries. A good quality cardiopulmonary resuscitation (CPR) is key for the survival of the patient in out-of-hospital cardiac arrest (OHCA), including chest compressions (CCs) and ventilations. Ventilations have been proven to have an important impact in the outcome of the patient, and detecting the CC pauses where ventilations were provided is the aim of this study. An algorithm that automatically detects pauses between sequences of CCs using machine learning techniques is proposed. For this study a set of 102 defibrillator files from OHCA patients that include the thoracic impedance recorded through the defibrillation pads was used. The work has been split into 2 main blocks: a random forest (RF) classifier that classifies 1-s windows as CC/no-CC and an algorithm that sets the beginning and the end of each detected pause. The RF classifier was validated using 10 fold cross-validation method, obtaining a median sensitivity (Se), specificity (Sp) and positive predictive value (PPV) of 95.4/97. 9/94.4 % respectively, for window classification. The pause detector returned median Se/PPV values of 90.0/91.3 % with a median pause delimitation error of 0.04 s and a duration error of 0.04 s.
UR - http://www.scopus.com/inward/record.url?scp=85100915027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100915027&partnerID=8YFLogxK
U2 - 10.22489/CinC.2020.313
DO - 10.22489/CinC.2020.313
M3 - Conference contribution
AN - SCOPUS:85100915027
T3 - Computing in Cardiology
BT - 2020 Computing in Cardiology, CinC 2020
PB - IEEE Computer Society
T2 - 2020 Computing in Cardiology, CinC 2020
Y2 - 13 September 2020 through 16 September 2020
ER -