DocumentCode :
1010971
Title :
Automatic Identification of Return of Spontaneous Circulation During Cardiopulmonary Resuscitation
Author :
Risdal, Martin ; Aase, Sven Ole ; Kramer-Johansen, Jo ; Eftestol, T.
Author_Institution :
PDMS, Stavanger
Volume :
55
Issue :
1
fYear :
2008
Firstpage :
60
Lastpage :
68
Abstract :
The main problem during pulse check in out-of-hospital cardiac arrest is the discrimination between normal pulse-generating rhythm (PR) and pulseless electrical activity (PEA). It has been suggested that circulatory information can be acquired by measuring the thoracic impedance via the defibrillator pads. To investigate this, we performed an experimental study where we retrospectively analyzed 127 PEA segments and 91 PR segments out of 219 and 113 segments. A PEA versus PR classification framework was developed, that uses short segments (< 10 s) of ECG and impedance measurements to discriminate between the two rhythms. Using realistic data analyzed over a duration of 3 s, our system correctly identifies 90.0% of the segments with rhythm being pulseless electrical activity, and 91.5% of the normal pulse rhythm segments. Automatic identification of pulse could avoid unnecessary pulse checks and thereby reduce no-flow time and potentially increase the chance of survival.
Keywords :
biomedical measurement; cardiovascular system; electric impedance measurement; electrocardiography; medical signal processing; neural nets; pattern recognition; signal classification; ECG; automatic identification; cardiac arrest; cardiopulmonary resuscitation; circulatory information; classification framework; defibrillator pads; electrocardiography; neural networks; normal pulse-generating rhythm; pattern recognition; pulse check; pulseless electrical activity; realistic data analysis; spontaneous circulation; thoracic impedance measurement; time 3 s; Blood pressure; Cardiac arrest; Cardiology; Data analysis; Electric shock; Electrocardiography; Impedance measurement; Pattern recognition; Performance analysis; Rhythm; Electrocardiography; impedance; neural networks; pattern recognition; Artificial Intelligence; Blood Circulation; Blood Flow Velocity; Cardiography, Impedance; Cardiopulmonary Resuscitation; Diagnosis, Computer-Assisted; Electrocardiography; Humans; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Therapy, Computer-Assisted;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2007.910644
Filename :
4404098
Link To Document :
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