Efficient ECG Beat Classification Using Inception Network on Software and FPGA Platforms

Convolutional Neural Networks (CNNs) have become a dominant solution for Electrocardiogram (ECG) beat classification, owing to their superior feature extraction capabilities. However, traditional CNN-based accelerators often encounter limitations such as large parameter counts, limited configurability, and inefficient hardware utilization. In this work, we propose a lightweight one-dimensional CNN (1D-CNN) inspired by the InceptionNet architecture, specifically designed for efficient and accurate ECG beat classification. The model integrates customized Inception blocks with parallel convolutional layers of varying kernel sizes to capture multi-scale temporal features. To reduce model complexity and enhance efficiency, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 \times 1$</tex> convolutions are used for dimensionality reduction, resulting in a compact network with only 7,740 parameters. Additionally, we present a hardware accelerator that exploits spatial data reuse across multiple processing units and employs dual-port RAM for concurrent data access, improving throughput and memory efficiency. When implemented on hardware with parallel and pipelined execution at 100 MHz, the system achieves a classification accuracy of 99.32%, sensitivity of 98.50%, specificity of 99.58%, and a positive predictive value (PPV) of 98.30% across five ECG classes with the MIT-BIH database, demonstrating its suitability for real-time deployment in resource-constrained environments.