FPGA-Based Edge Enabled Time-Frequency Domain Lightweight Deep Neural Network for Identification of Valvular Heart Diseases Using PCG Signals

Abstract

Early and automated identification of valvular heart diseases (VHDs) using phonocardiogram (PCG) signals provides a cost-effective solution for developing intelligent healthcare applications. In this letter, a lightweight deep convolutional neural network, LWHSNet, implemented on a field programmable gate array (FPGA)-based edge computing device, is proposed to identify VHDs via time-frequency domain (TFD) analysis of PCG signals. The TFD representation of the PCG signal is computed using the continuous wavelet transform. The LWHSNet model consists of 13 layers and is trained using TFD images of the PCG signals. Pruning and fixed-point (FxP) precision-based quantization are used to reduce the size of the LWHSNet model for VHD identification. The FPGA implementation of the proposed TFD-based LWHSNet model is performed using a high-level synthesis framework. The performance of the proposed FPGA-based TFD-based LWHSNet model is evaluated using PCG signals from a public database. The proposed LWHSNet model achieves an overall accuracy of 95% with a power consumption of 1.88 watts, a latency of 0.96 s, and a throughput of 207 instances per second on the PYNQ-Z2-based FPGA for VHD identification during inference. The proposed embedded healthcare system is well-suited for resource-efficient edge computing applications that utilize PCG signals to identify VHDs.