Neural network based algorithm for a spectrogram classification of wrist-type PPG using high-order harmonics processing.

The importance of accurate and continuous heart rate monitoring during workout cannot be overestimated. Supporting heart rate in the desired range, you strengthen the heart muscle and the overall fitness level. Therefore, the precise heart rate measurements are important at each stage: before workout, during exercises and after completion. One of the most important problems for precise heart rate measurement during high intensive exercises with the help of wearable devices is the movement artifacts. Even the smallest movement of the muscles, the turn of the wrist, the movement of the fingers or even the displacement of the fitness tracker per millimeter - all this very much distorts the useful signal. To solve the problem of motion artifacts, both digital signal processing approaches are used, as well as deep learning methods. In this paper, we offer a new method of processing the signal of wearable devices during workout, in particular to solve the motion artifacts problem, using deep neural networks. A distinctive feature of the model is the use of higher signal harmonics to determine the shape and type of signal. In particular, a method is proposed for classifying the signal spectrogram to noise, movement and useful component. During cross-validation on an available datasets, we compared the effectiveness of the proposed approach for spectrogram classification and received an improvement of averaged ROC AUC (area under the receiver operating characteristic curve) and F1 Score by 5% by using higher harmonics. Clinical relevance- This work aims to provide an approach for a wearable devices PPG signal spectrogram classification using neural networks and high-order harmonics processing.