Fig. 5: Healthcare monitoring of CVD patients using a CNN.
From: Higher-dimensional processing using a photonic tensor core with continuous-time data
a, CNN architecture. The CNN is designed to identify CVD patients at the risk of sudden death. ECG signals are supplied to the input layer. The system presented in Fig. 4 performs higher-dimensional convolution. A rectified linear unit (ReLU) layer, a fully connected layer and a softmax layer are applied in sequence after convolution. b,c, Comparison of expected convolution results (CPU convolved) (i) and measured convolution results (photonic system convolved) (ii) of normal ECG signals when patients are safe (b) and when patients are at risk when experiencing ventricular fibrillation (c). All the convolutions are performed once, and the error bands represent the standard deviation of convolution results from 50 pulses generated by the same patient, showing the variation in the ECG signal generated by this patient. d, Convolution result accuracy. The inset shows the normalized error distribution. e, CNN classification accuracy. The classification accuracy using the measured convolution results (93.5%) is close to that using the expected convolution results (94.0%). Both accuracies are higher than that using the same neural network but without a convolution layer.
