A shockable rhythm detection algorithm for automatic external defibrillators by combining a slope variability analyzer with a band-pass digital filter

To make automatic external defibrillators (AEDs) easy to use by the public who is not familiar with emergency treatment and electrocardiogram (ECG) analysis, it is critical to have an accurate shockable rhythm recognition algorithm. This paper presents a novel compositive algorithm by combining a slope variability analyzer with a band-pass digital filter so as to accurately distinguish shockable rhythms from non-shockable rhythms for automatic external defibrillators (AEDs). A total of 35 ECG records from the widely recognized Creighton University Ventricular Tachyarrhythmia Database (CUDB) were used to test the performance of the proposed algorithm. The obtained sensitivity of 94.2% and the specificity of 96.6% both satisfy requirements by the AHA rules on the arrhythmias detection for AEDs, and show a higher performance comparing with the previous HILB algorithm and the slope variability method only. As a conclusion, the proposed compositive algorithm would potentially provide a useful tool for AED systems with a higher accuracy and lower computation requirements.