An Energy-Efficient ECG Classifier with On-Chip Learning Using Binarized Convolutional Neural Network

In ECG classification applications, binarized convolutional neural networks (bCNNs) show great potential to achieve extremely low power consumption through 1-bit quantization. Existing bCNN approaches typically extract spatial features from the full ECG image without leveraging its sparsity, thereby introducing unnecessary computations and hardware resources. Meanwhile, inter-patient variability of ECG features degrades the classification performance due to accuracy loss caused by the binarization operation. To address these challenges, this paper proposes an energy-efficient ECG classifier based on a bCNN with on-chip learning. A patch-by-patch computation approach is used to reduce both power consumption and memory usage. Instead of processing the entire image, the ECG image is divided into small patches, and only the patches containing valid data are involved in feature extraction. An on-chip learning method is employed to improve classification accuracy among patients by updating the model weights using both the acquired bCNN features and the R-peak interval data. In addition, a reconfigurable convolutional processing element array and a base-2 softmax structure are designed to further reduce the hardware resources. The proposed classifier is verified on an FPGA, achieving a classification accuracy of 97.55% and a specificity of 89.15%. Synthesized using a 55 nm CMOS process, the ECG classifier occupies an area of 0.43 mm². With a supply voltage of 1.2 V, the classifier consumes an average energy of 0.12 $μ$J per classification and 0.09 $μ$J per on-chip learning, making it suitable for wearable ECG classification application.