# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
## 6 Results and Discussions
### Baseline 1 DCNN Performance  
In this section, we evaluate the performance of the proposed 1 DCNN specified using the following metrics. * Top-1 Accuracy: This is the conventional accuracy and it measures the proportion of test examples for which the predicted label (i.e., respiratory state) matches the expected label. To formulate top-1 accuracy, we introduce the following definitions. * True Positives (TP): For binary classification problems, i.e., ones with a yes/no outcome (such as the case of respiratory classification), this is the total number of test examples for which the value of the actual class is yes and the value of predicted class is also yes. * True Negatives (TN): This is the total number of test examples for which the value of the actual class is no and the value of the predicted class is also no. * False Positives (FP): This is the total number of test examples for which the value of the actual class is no but the value of the predicted class is yes. * False Negatives (FN): This is the total number of test examples for which the value of the actual class is yes but the value of the predicted class is no. \[\text{Top-1 Accuracy}=\frac{TP+TN}{TP+FP+FN+TN}\] (16)
* F1 Score: To formulate the F1 score, we introduce the following definitions. Precision: This is the ratio of correctly predicted positive observations to the total predicted positive observations, i.e., \[\text{Precision}=\frac{TP}{TP+FP}\] (17) * Recall: This is the ratio of correctly predicted positive observations to the all observations in actual class, i.e., \[\text{Recall}=\frac{TP}{TP+FN}\] (18) The F1 score conveys the balance between the precision and the recall. It is calculated as the weighted average of precision and recall, i.e., \[\text{F1 Score}=\frac{2*(\text{Recall}*\text{Precision})}{(\text{Recall}+ \text{Precision})}\] (19)
* AUC: In machine learning, a receiver operating characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as its discrimination threshold is varied. The area under curve (AUC) measures the two-dimensional area underneath the ROC curve. AUC tells how much the model is capable of distinguishing between classes. The higher the AUC, the better the model is at predicting yes classes as yes and no classes as no. * Sensitivity: This is the true positive rate, i.e., how often the model correctly generates a yes out of all the examples for which the value of actual class is yes. Sensitivity is formulated as \[\text{Sensitivity}=\frac{TP}{TP+FN}\] (20)
* Specificity: This is the true negative rate, i.e., how often the model correctly generates a no out of all the examples for which the value of actual class is no. Specificity is formulated as \[\text{Specificity}=\frac{TN}{TN+FP}\] (21)  
Table 3 compares the classification performance using the proposed 1 DCNN against three state-of-the-art approaches: (1) Support Vector Machine (SVM) classifier of [18], (2) Logistic Regression (LR) classifier of [19], and (3) Random Forest classifier of [19]. We make the following four key observations. First, the proposed 1 DCNN has the highest top-1 accuracy of all the evaluated techniques (higher top-1 accuracy is better). The top-1 accuracy of 1 DCNN is better than SVM by 5.2%, LR by 6.0%, and RF by 4.0%. Second, the proposed 1 DCNN has the highest F1 score of all the evaluated techniques (higher F1 score is better). The F1 score of 1 DCNN is higher than SVM by 7.7%, LR by 7.7%, and RF by 6.5%. Third, the proposed 1 DCNN has the highest AUC of all the evaluated techniques (higher AUC score is better). The AUC score of 1 DCNN is higher than SVM by 6.2%, LR by 8.5%, and RF by 8.5%.