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| Result:
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| 1. Quantification of accuracies for single channels with 20-fold cross-validation, resulting highest individual accuracies: v5, v6, vx, vz, and ii
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| 2. Quantification of accuracies for pairs of top 5 highest individual channels with 20-fold cross-validation, resulting highest pairs accuracies to fed into a the neural network: lead v6 and lead vz
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| 3. Use 100-fold cross validation on v6 and vz pair of channels, then compare outliers based on top 20, top 50 and total 100 performing models, finding that standard deviation is non-trivial and there are few models performed very poorly.
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| 4. Discussing 2 factors effecting model performance evaluation:
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| 1. Random train-val-test split might have effects of the performance of the model, but it can be improved by access with a larger data set and further discussion
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| 2. random initialization of the weights of neural network shows little effects on the performance of the model performance evaluation, because of showing a high average results with a fixed train-val-test split
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| 5. Comparing with other models, the model in this article has the highest accuracy, specificity, and precision
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| 6. Further using 290 fold patient-wise split, resulting the same highest accuracy of the pair v6 and vz as record-wise split
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| 1. Discuss patient-wise split might result lower accuracy evaluation, however, it still maintain an average of 97.83%
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