This paper presents a heartwave based authentication method that utilizes an ensemble of deep belief networks (DBNs) under different parameters to increase the reliability of feature extraction. The multiview outputs are further embedded into a single view before inputting into a stacked DBN for classification. The result of the proposed novel architecture achieved a classification rate of 98.3% with 30% training data. Importantly, it is able to perform user classification using heartwave signals acquired under intense physical exercise where heart rate ranges from 50 bpm to as high as 180 bpm. Under extreme physical duress, the heartwave from an individual experiences extreme morphological variations that render conventional classification approaches nonapplicable.