Mobilmed’s implementation of AI algorithm is a novel fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. We have implemented an adaptive implementation of 1D Convolutional Neural Networks (CNNs) which is utilized to merge feature extraction and classification into a single learning body. With a deep but low-complexity implementation, each 1D CNN for each patient not only voids the necessity to extract hand-crafted manual features, or any kind of pre- and post-processing, also makes it a primary choice for a real-time implementation for heart health monitoring and anomaly detection. Besides the elegant speed and computational efficiency achieved, our method currently holds the state-of-the-art solution for the patient-specific ECG beat classification problem. Recently it became one of the top-10 most popular articles for the year 2016 in the IEEE Transaction on Biomedical Engineering.