Evolutionary Optimized Multiple Instance Concept Learning for Beat-to-Beat Heart Rate Estimation from Electrocardiograms

In this paper, we proposed an effective method to obtain the R wave concept to estimate heart rate from electrocardiogram signals produced by a wearable electrocardiogram(ECG) device. The multiple instance adaptive cosine/coherent estimator(MI-ACE) is a multiple instance learning method that can learn the target concept from imprecisely labeled data. However, the R wave concepts estimated by MI-ACE are dependent on initialization strategy of MI-ACE. Thus, the heart rate estimation results are undetermined with different initialization. Evolutionary algorithm is a global optimization method that simulates natural processes. To overcome this problem, we proposed the evolutionary optimized MI-ACE algorithm(MI-ACE-Evo) which combines MI-ACE with an evolutionary optimization to learn the R wave target concept, which will make heart rate estimation more effective and not affected by varies initialization of MI-ACE. The experimental results show that the R wave concept learned by MI-ACE-Evo is more discriminative and the heartrate estimation results are superior to that of the original MI-ACE method.