Fig. 4 | Scientific Reports

Fig. 4

From: Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals

Fig. 4

Architecture of the bidirectional long short-term memory (biLSTM) deep learning neural network model for predicting the ECG wave from BCG signals. The model is composed of a sequential input layer (SI), three biLSTM layers, a fully connected layer, and a regression output layer (RO). Each biLSTM layer consists of 128 hidden cells. A dropout layer (rate of 0.2) is added after each biLSTM layer to reduce overfitting. For simplicity, only a single biLSTM with the dropout layer is shown. BCG ballistocardiography signal, |hBCG| full-wave rectified bandpass filter BCG signal, pECG predicted ECG signal.

Back to article page