DWT-PoolFormer: Discrete Wavelet Transform-based Quantized Parallel PoolFormer Network Implemented in FPGA for Wearable ECG Monitoring

This paper presents a Discrete Wavelet Transform based poolformer network model (DWT-Poolformer) for real-time electrocardiogram (ECG) signal processing on wearable devices. By integrating a FPGA-based CWT module together with advanced quantization techniques, this model addresses the computation and resource constraints challenges faced by the wearable devices. This design achieved optimum balance between computational efficiency and diagnostic accuracy. Tested with the MIT-HIB dataset, this model achieved high accuracy of 99.4 2% with 4 -bit weight resolution and 8 -bit activation resolution while significantly reducing computational load and memory usage. The results highlight the potential of this model for real-time diagnostic applications in AI-powered wearable healthcare devices.