# Deep-Learning Model to Predict Coronary Artery Calcium Scores in Humans from Electrocardiogram Data
###### Abstract  
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%. electrocardiogram; coronary artery calcium score; deep-learning neural network model; coronary artery disease +
Footnote †: journal: Medical  
==WARNING: Truncated because of repetitions==
100 & 0.686 & 0.780 & 0.741 & 63.6 & 71.0 & 68.0 \\  & 150 & 0.689 & 0.788 & 0.747 & 64.1 & 71.7 & 68.6 \\  & 200 & 0.697 & 0.795 & 0.755 & 64.8 & 72.2 & 69.2 \\  & 400 & 0.735 & 0.838 & 0.796 & 67.5 & 76.1 & 72.6 \\ \hline \multirow{8}{*}{RNN + DNN} & 1 & 0.749 & 0.780 & 0.766 & 68.2 & 71.1 & 69.7 \\  & 25 & 0.768 & 0.799 & 0.785 & 69.7 & 72.4 & 71.2 \\ \cline{1-1}  & 50 & 0.782 & 0.813 & 0.799 & 70.8 & 73.5 & 72.3 \\ \cline{1-1}  & 100 & 0.796 & 0.827 & 0.814 & 71.8 & 74.8 & 73.5 \\ \cline{1-1}  & 150 & 0.802 & 0.833 & 0.819 & 72.3 & 75.3 & 74.0 \\ \cline{1-1}  & 200 & 0.809 & 0.838 & 0.825 & 72.8 & 76.0 & 74.5 \\ \cline{1-1}  & 400 & 0.848 & 0.874 & 0.863 & 76.3 & 79.0 & 77.8 \\ \hline \multirow{8}{*}{Our Method} & 1 & 0.796 & 0.804 & 0.801 & 72.5 & 73.4 & 72.9 \\ \cline{1-1}  & 25 & 0.814 & 0.822 & 0.818 & 74.0 & 74.6 & 74.3 \\ \cline{1-1}  & 50 & 0.828 & 0.835 & 0.832 & 75.1 & 75.7 & 75.4 \\ \cline{1-1}  & 100 & 0.841 & 0.847 & 0.845 & 76.1 & 76.8 & 76.4 \\ \cline{1-1}  & 150 & 0.847 & 0.853 & 0.850 & 76.5 & 77.1 & 76.8 \\ \cline{1-1}  & 200 & 0.854 & 0.861 & 0.857 & 77.2 & 77.9 & 77.5 \\ \cline{1-1}  & 400 & 0.887 & 0.893 & 0.890 & 80.0 & 81.3 & 80.6 \\ \hline \hline \end{tabular} \end{table}
Table 2: Test dataset performance for coronary artery disease risk. +++  
to differentiate the category (CACS = 1-10) from other categories [12]. In Reference [12], [PERSON] et al. mentioned that it was highly sensitive to small prediction mistakes. As described in the Introduction, the precision, recall, and F-1 score in the category were 60.0%, 15.0%, and 24.0%, respectively. The second reason is that our goal is the early detection of heart-disease possibility. This means that it is important to generate a precise clinical interpretation of the coronary artery disease in the lower category. In the first case (CACS \(\geq\) 1) and the second case (CACS \(\geq\) 25), the percentages of the positive ECG data in the test dataset are 22.1% and 12.2%, respectively. This means that the dataset is imbalanced. In the first category, the precision, recall rate, and F-1 score of our model are 43.5%, 73.1%, and 54.6%, respectively. In the second category, the precision, recall rate, and F-1 score are 28.7%, 74.4%, and 41.1%, respectively. The  
To further validate the robustness of our model, we included the experimental results according to the number of ECG leads. Table 2 presents the performance when eight-lead (I, II, and V0-5) ECG waves are used as the input to our model. In the first case, two-lead (I and II) ECG waves are input to our model. The two leads compare the electrodes of the two arms and the left leg.