# Towards an Automatic Recognition of Artifacts and Features in Plethysmographic Traces
## 5 Feature Identification
### Results  
In the test sample, which was statistically uncorrelated with the training and validation sets, the model predictions were compared against the annotations described in Section 3.3, which were considered the reference to evaluate the model. The numbers of \(c\) and \(y\) peaks identified in the testing sample are reported in Table 3. Figure 6 represents an example of a  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Layer Type** & **Output Shape** & **Number of Parameters** \\ \hline Input & 15, 1 & - \\ Convolutional 1D (1) & 8, 32 & 288 \\ Convolutional 1D (2) & 5, 16 & 2064 \\ Convolutional 1D (3) & 4, 8 & 264 \\ Flatten & 32 & - \\ Dense & 256 & 8448 \\ Output & 45 & 11,565 \\ \hline Total parameters & & 22,629 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of the convolutional neural network architecture, showing the layer type, output shape, and number of parameters.  
plethysmographic trace where both the annotations and the model predictions are shown and compared to each other.  
The performance of the model was evaluated in terms of efficiency, also known as recall, and purity, sometimes referred to as precision. Efficiency is defined as the ratio of true positives, i.e., the number of times where a peak was correctly identified, to the total number of positives in the testing sample, reflecting the completeness of the prediction. Purity is the ratio of true predicted positives to the total predicted positives, indicating the accuracy of the predictions. These figures of merit were chosen since there was no cross-contamination between categories, e.g., no \(c\) peak was labeled as a \(y\) peak and vice versa.  
The model achieved an efficiency of \(0.94^{+0.06}_{-0.18}\) and \(0.85^{+0.15}_{-0.17}\) for the the \(c\) and \(y\) peaks, respectively. The purity was \(0.92^{+0.08}_{-0.17}\) for the \(c\) peaks and \(0.89^{+0.11}_{-0.16}\) for the \(y\) peaks. These results, along with their uncertainties, are summarized in Table 3. The uncertainties are purely statistical and reflect the limited number of peaks in the testing sample.  
The difference in the performance between the two peaks was primarily due to the characteristics of the JVP waveform. The \(y\) peaks were often closely spaced and may have overlapped with other features, making them more challenging to detect. In contrast, \(c\) peaks were typically more isolated, simplifying their identification. Additionally, the performance of the model was constrained by the limited size and variability of the training dataset. Expanding the dataset to include a larger number of patients in diverse positions  
Figure 6: The autonomous labeling of the main features of a JVP waveform. The red and green dots represent the \(c\) and \(y\) peaks, respectively, predicted by the model. The crosses represent the “true” reference peaks, recognized as such by a human operator.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & \(c\)**Peak** & \(y\)**Peak** \\ \hline True positives & 51 & 47 \\ True predicted positives & 59 & 54 \\ Total positives & 54 & 55 \\ Total predicted positives & 64 & 61 \\ \hline Efficiency & \(0.94^{+0.06}_{-0.18}\) & \(0.85^{+0.15}_{-0.17}\) \\ Purity & \(0.92^{+0.08}_{-0.17}\) & \(0.89^{+0.11}_{-0.16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of peaks in the testing patient, reported separately for each peak type (\(c\) or \(y\)) of the JVP waveform. The bottom rows report the efficiency and the purity with the corresponding statistical uncertainties.  
would likely reduce the disparity between the accuracy of \(c\) and \(y\) peak classifications, improve overall accuracy, and decrease statistical uncertainty. Future work will focus on addressing these limitations to enhance the robustness and generalizability of the model.