Performance analysis of remote photoplethysmography deep filtering using long short-term memory neural network.

BACKGROUND: Remote photoplethysmography (rPPG) is a technique developed to estimate heart rate using standard video cameras and ambient light. Due to the multiple sources of noise that deteriorate the quality of the signal, conventional filters such as the bandpass and wavelet-based filters are commonly used. However, after using conventional filters, some alterations remain, but interestingly an experienced eye can easily identify them. RESULTS: We studied a long short-term memory (LSTM) network in the rPPG filtering task to identify these alterations using many-to-one and many-to-many approaches. We used three public databases in intra-dataset and cross-dataset scenarios, along with different protocols to analyze the performance of the method. We demonstrate how the network can be easily trained with a set of 90 signals totaling around 45 min. On the other hand, we show the stability of the LSTM performance with six state-of-the-art rPPG methods. CONCLUSIONS: This study demonstrates the superiority of the LSTM-based filter experimentally compared with conventional filters in an intra-dataset scenario. For example, we obtain on the VIPL database an MAE of 3.9 bpm, whereas conventional filtering improves performance on the same dataset from 10.3 bpm to 7.7 bpm. The cross-dataset approach presents a dependence in the network related to the average signal-to-noise ratio on the rPPG signals, where the closest signal-to-noise ratio values in the training and testing set the better. Moreover, it was demonstrated that a relatively small amount of data are sufficient to successfully train the network and outperform the results obtained by classical filters. More precisely, we have shown that about 45 min of rPPG signal could be sufficient to train an effective LSTM deep-filter.

Impact of ascending aorta replacement by graft on elastic properties of descending thoracic aorta evaluated by cardiac magnetic resonance imaging.

OBJECTIVE: The aim of our study was to evaluate the impact of aortic root replacement by graft on the elastic properties of the descending thoracic aorta using cardiac magnetic resonance imaging (MRI) and automatic post-processing. MATERIALS AND METHODS: Nineteen patients were operated for an aortic root aneurysm. Cardiac MRI was performed before and after surgery to measure aortic compliance. Images were acquired on a 1.5 T MRI with a conventional aortic MRI protocol plus one additional kinetic sequence orientated perpendicularly to the aorta at the level of pulmonary trunk. The contours of the ascending and descending aortas were detected automatically for each phase with homemade software. RESULTS: Mean time between surgical procedure and earliest post-operative MRI was 18.2 ± 7.1 months. There was no significant difference between the pre- and earliest post-operative mean descending aorta areas and no significant modification in descending aortic compliance after aortic root replacement (1.43 ± 0.84 vs 1.37 ± 0.58 mm2/mmHg, p = 0.47). Pre- and post-operative systolic and diastolic blood pressure were similar. There was a significant decrease in ascending aortic compliance after surgery (2.52 ± 1.24 vs 0.91 ± 0.52 mm2/mmHg; p < 0.0001). DISCUSSION: The aortic root replacement by graft was not associated with changes in elastic properties of the descending aorta at short term. CLINICAL REGISTRATION NUMBER: NCT03817008.