Assessment of ECG frequency and morphology parameters for automatic classification of life-threatening cardiac arrhythmias.
The reliable recognition and adequate electrical shock therapy of life-threatening cardiac states depend on the electrocardiogram (ECG) descriptors which are used by the defibrillator-embedded automatic arrhythmia analysis algorithms. We propose a method for real-time ECG processing and parameter set extraction using band-pass digital filtration and ECG peak detection. Twelve parameters were derived: (i) seven parameters from the band-pass filter output-six threshold parameters and one peak counter; (ii) five parameters from the ECG peak detection branch, which assess the heart rate, the periodicity and the amplitude/slope symmetry of the ECG peaks. The statistical assessment for more than 36 h of cardiac arrhythmia episodes collected from the public AHA and MIT databases showed that some of the parameters achieved high specificity and sensitivity, but there was no parameter providing 100% separation between non-shockable and shockable rhythms. In order to estimate the influence of the wide variety of cardiac arrhythmias and the different artifacts in real recording conditions, we performed a more detailed study for eight non-shockable and four shockable rhythm categories. The combination of the six top-ranked parameters provided specificity: (i) more than 99% for rhythms with narrow supraventricular complexes, premature ventricular contractions, paced beats and bradycardias; (ii) almost 95% for supraventricular tachycardias; (iii) 91.5% for bundle branch blocks; (iv) 92.2% for slow ventricular tachycardias. The attained sensitivity was above 98% for coarse and fine ventricular fibrillations and 94% for the rapid ventricular tachycardias. The accuracy for the noise contaminated non-shockable and shockable signals exceeded 93%. The proposed parameter set guarantees an accuracy that meets the AHA performance goal for each rhythm category and could be a reliable facility for AED shock-advisory algorithms.
Krasteva
Vessela
V
Centre of Biomedical Engineering Prof. Ivan Daskalov, Bulgarian Academy of Science, Sofia. vessika@clbme.bas.bg
Jekova
Irena
I
D001145:Arrhythmias, Cardiac / Q000473:pathology / Q000503:physiopathology*; D004562:Electrocardiography; D006801:Humans