We introduce a deep-learning neural network model that uses electrocardiogram (ECG) data to predict coronary artery calcium scores, which can be useful for reliably detecting cardiovascular risk in patients. In our pre-processing method, each lead of the ECG is segmented into several waves with an interval, which is determined as the period from the starting point of a P-wave to the end point of a T-wave. The number of segmented waves of one lead represents the number of heartbeats of the subject per 10 s. The segmented waves of one cycle are transformed into normalized waves with an amplitude of 0–1. Owing to the use of eight-lead ECG waves, the input ECG dataset has two dimensions. We used a convolutional neural network with 16 layers and 5 fully connected layers, comprising a one-dimensional filter to examine the normalized wave of one lead, rather than a two-dimensional filter to examine the coherence among the unit waves of eight leads. The training and testing are repeated 10 times with a randomly assigned dataset (177,547 ECGs). Our network model achieves an average area under the receiver operating characteristic curve of 0.801–0.890, and the average accuracy is in the range of 72.9–80.6%.
van der Waals loops and the melting transition in two dimensions