Abstract：Automatic external defibrillator (AED) is an important device in saving patients with cardiac arrest (SCA). Shockable advice algorithm (SAA) is the key technology of AED. In this work, our model was trained with a data set including 2 024 segments of shockable rhythms (SHR) and 7 884 segments of non-shockable rhythms (NSHR). We proposed a SAA based on machine learning. Combining 6 effective features selected from 32 features such as time domain, frequency domain and complexity, support vector machine was employed to classify SHR and NSHR. After 500 experiments, the mean value ±standard deviation of sensitivity of the algorithm was 97.62±0.18%, the specificity was 99.15±0.04%, and the accuracy was 98.79±0.08%. The results showed that the SAA proposed in this paper met the requirements of American Heart Association for SAA performance in AED, and it can be used as an AED algorithm module for automatic discrimination of SHR.
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