Deep learning for predicting respiratory rate from biosignals

In the past decade, deep learning models have been applied to bio-sensors used in a body sensor network for prediction. Given recent innovations in this field, the prediction accuracy of novel models needs to be evaluated for bio-signals. In this paper, we evaluate the performance of deep learning models for respiratory rate prediction. We consider three datasets from bio-sensors which include electrocardiogram (ECG), photoplethysmogram (PPG) data, and surface electromyogram (sEMG) data. The deep learning models include Long short-term memory (LSTM) networks, Bidirectional LSTM (Bi-LSTM), attention-based variants of LSTM, CNN-LSTM and Convolutional-LSTM networks. The deep learning models are evaluated for two separate windows which are 32 s and 64 s window. The models' performance is evaluated using mean absolute error (MAE). The 64 s window has more accurate prediction compared to the 32 s window. Our results indicate Bi-LSTM with Bahdanu Attention has the best performance for the bio-signals. LSTM performs best with one of the datasets, yielding an MAE of 0.70 ± 0.02. Bi-LSTM with Bahdanau attention showed best results with two of the three datasets with MAE of 0.51 ± 0.03 for sEMG based data and MAE of 0.24 ± 0.03 with PPG and ECG based data.