TY - GEN
T1 - An Impedance-based Algorithm to Detect Ventilations during Cardiopulmonary Resuscitation
AU - Jaureguibeitia, X.
AU - Irusta, U.
AU - Aramendi, E.
AU - Wang, H. E.
AU - Idris, A. 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-2019-1-0209.
Publisher Copyright:
© 2020 Creative Commons; the authors hold their copyright.
PY - 2020/9/13
Y1 - 2020/9/13
N2 - Cardiopulmonary resuscitation (CPR) is a core therapy to treat out-of-hospital cardiac arrest (OHCA). Thoracic impedance (TI) can be used to assess ventilations during CPR, but the signal is also affected by chest compression (CC) artifacts. This study presents a method for TI-based ventilation detection during concurrent manual CCs. Data from 152 OHCA patients were analyzed. A total of 423 TI segments of at least 60 s during ongoing CCs were extracted. True ventilations were annotated using the capnogram. The final dataset comprised 1210 min of TI recordings and 9665 ground truth ventilations. A three-stage detection algorithm was developed. First, the TI signal was filtered to obtain ventilation waveforms, including a least mean squares filter to remove artifacts due to CCs. Potential ventilations were then identified with a heuristic detector and characterized by a set of 16 features. These were finally fed to a random forest classifier to discriminate between true ventilations and false positives. Patients were split into 100 distinct training (70%) and test (30%) partitions. The median (interquartile range) sensitivity, PPV and F-score were 83.9 (70.2-91.2) %, 86.1 (75.0-93.3) % and 84.3 (72.1-91.4) %. Our method would allow feedback on ventilation rates as well as surrogate measures of insufflated air volume during CPR.
AB - Cardiopulmonary resuscitation (CPR) is a core therapy to treat out-of-hospital cardiac arrest (OHCA). Thoracic impedance (TI) can be used to assess ventilations during CPR, but the signal is also affected by chest compression (CC) artifacts. This study presents a method for TI-based ventilation detection during concurrent manual CCs. Data from 152 OHCA patients were analyzed. A total of 423 TI segments of at least 60 s during ongoing CCs were extracted. True ventilations were annotated using the capnogram. The final dataset comprised 1210 min of TI recordings and 9665 ground truth ventilations. A three-stage detection algorithm was developed. First, the TI signal was filtered to obtain ventilation waveforms, including a least mean squares filter to remove artifacts due to CCs. Potential ventilations were then identified with a heuristic detector and characterized by a set of 16 features. These were finally fed to a random forest classifier to discriminate between true ventilations and false positives. Patients were split into 100 distinct training (70%) and test (30%) partitions. The median (interquartile range) sensitivity, PPV and F-score were 83.9 (70.2-91.2) %, 86.1 (75.0-93.3) % and 84.3 (72.1-91.4) %. Our method would allow feedback on ventilation rates as well as surrogate measures of insufflated air volume during CPR.
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U2 - 10.22489/CinC.2020.325
DO - 10.22489/CinC.2020.325
M3 - Conference contribution
AN - SCOPUS:85100927246
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 -