# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 4 Discussion
Furthermore, more patients in the emergency group had problems related to the visibility of the face of the patient, which negatively affected the agreement. However, the two groups of patients are too small to evaluate if the difference in performance was systemic or if their occurrence was by chance, especially since irregular heart rhythm and problems with face detection also occur in some postoperative patients. We have noted that irregular heart rhythms can be problematic for accurate heart rate monitoring. This can be partially attributed to the fact that a Fourier-spectrum-based heart rate extraction cannot always accurately represent the heart rate of irregular heart rhythms. Potentially, a time-domain-based analysis of the heart rhythm would be better suited if the heart rhythm is irregular. Implementation of a time domain analysis might also require changes to other steps in the processing chain and is therefore left for future work. In previous work [27], we have investigated the effect of atrial fibrillation and flutter on heart rate monitoring in a different dataset; a similar analysis in this dataset will be performed in future work. One more important factor that we have not yet considered is skin pigment. In prior work [5], it was shown that the amplitude of the remote PPG signal is related to the skin tone, especially in RGB-based measurements. In our study, unfortunately, we could not evaluate the influence of skin pigments on the performance of heart rate extraction, as all patients are classified as having skin type I/II on the Fitz-Patrick scale. Further evaluation of these effects are left for future work. We evaluated continuous camera-based respiration monitoring using an optical flow method that finds the breathing motion by restricting the optical flow in the chest ROI. We furthermore developed a metric which uses these restrictions and the residual error of the restricted optical flow model to determine whether a segment contains any non-breathing motion which could affect the accuracy of the respiration rate measurement. Using the breathing motion metric for both NIR- and RGB-based respiration rate extraction, we evaluated the overall performance of the combined respiration rate. Overall, a 3 RPM agreement of 91.1%, an MAE of 1.12 RPM, and a coverage of 59.1% were achieved. Postoperative patients achieved an agreement of 92.5% and a coverage of 64.8%, while the emergency patients achieved an agreement of 86.7% and coverage of 47%. The lower coverage for the emergency patient group can be partly attributed to the failure of the face ROI detection, which also affected the quality of the chest ROI. Furthermore, postoperative patients tended to be sedated at the start of the recording, while emergency patients were generally not, making it more likely that segments were excluded there based on the presence of non-breathing motion. Our camera-based respiration rate extraction methodology still has some limitations. First, the respiration rate calculation method used in this work based on Fourier spectrum peaks was not always successful. In particular, the morphology of the breathing motion and irregular breathing could cause a loss of agreement with the reference. Therefore, a respiration rate measurement based on breath-to-breath intervals would likely be more successful and should be evaluated in future work. Furthermore, the breathing motion metric tends to be a conservative metric: if any non-breathing motion is detected in a segment, it is immediately discarded. In future work, we aim to extend the metric to an indicator which can detect non-breathing motion on a per-breath basis such that only real breaths are used to determine the respiration rate, while the signal affected by non-breathing motion is ignored.