A method that remotely measures blood oxygen saturation through two cameras under regular lighting is proposed and experimentally demonstrated. Two narrow-band filters with their visible wavelength of 660nm and 520nm are mounted to two cameras respectively, which are then used to capture two photoplethysmographic (PPG) from the subject simultaneously. The data gathered from this system, including both blood oxygen saturation and heart rate, is compared to the output of a traditional figure blood volume pulse (BVP) senor that was employed on the subject at the same time. Result of the comparison showed that the data from the new, non-contact system is consistent and comparable with the BVP senor. Compared to other camera-based measuring method, which requires additional close-up lighting, this new method is achievable under regular lighting condition, therefore more stable and easier to implement. This is the first demonstration of an accurate video-based method for non-contact oxygen saturation measurements by using ambient light with their respective visible wavelength of 660nm and 520nm which is free from interference of the light in other bands.

We proposed a one-dimensional convolutional neural network (CNN) model, which divides heart sound signals into normal and abnormal directly independent of ECG. The deep features of heart sounds were extracted by the denoising autoencoder (DAE) algorithm as the input feature of 1D CNN. The experimental results showed that the model using deep features has stronger anti-interference ability than using mel-frequency cepstral coefficients, and the proposed 1D CNN model has higher classification accuracy precision, higher F-score, and better classification ability than backpropagation neural network (BP) model. In addition, the improved 1D CNN has a classification accuracy rate of 99.01%.

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