The reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency domain, SNR, etc.). The results show that it is possible to identify regions of a face, for which reliable PPG signals can be extracted. The accuracy obtained for the classification task and the mean absolute error achieved for the regression task proved the usefulness of the DNN models.
Authors
- prof. dr hab. inż. Jacek Rumiński link open in new tab ,
- mgr inż Alicja Kwaśniewska,
- mgr inż. Maciej Szankin,
- dr inż. Tomasz Kocejko link open in new tab ,
- dr inż. Magdalena Mazur-Milecka link open in new tab
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1109/embc.2019.8857839
- Category
- Aktywność konferencyjna
- Type
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language
- angielski
- Publication year
- 2019
