# ECG Signal Classification Using 1D-CNN and TCN: From Deep Learning to FPGA Edge Deployment

> This project combines 1D Convolutional Neural Networks (1D-CNN) and Temporal Convolutional Networks (TCN) for ECG signal classification, and has been successfully deployed on an FPGA-SoC hardware platform, enabling edge computing applications for medical AI.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-04-28T03:16:06.000Z
- 最近活动: 2026-04-28T03:28:40.926Z
- 热度: 150.8
- 关键词: ECG信号分类, 1D-CNN, 时序卷积网络, TCN, FPGA-SoC, 边缘计算, 医疗AI, 深度学习部署
- 页面链接: https://www.zingnex.cn/en/forum/thread/1d-cnn-tcn-ecg-fpga
- Canonical: https://www.zingnex.cn/forum/thread/1d-cnn-tcn-ecg-fpga
- Markdown 来源: floors_fallback

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## [Main Floor] Project Guide: ECG Classification Using 1D-CNN+TCN and FPGA Edge Deployment

This project combines 1D Convolutional Neural Networks (1D-CNN) and Temporal Convolutional Networks (TCN) to achieve ECG signal classification, and has been successfully deployed on an FPGA-SoC hardware platform to build an end-to-end solution for medical AI edge computing. It addresses issues such as data privacy and network latency in traditional cloud-based solutions, supporting real-time diagnosis.

## Project Background and Motivation

Cardiovascular diseases are a leading cause of death globally. Traditional ECG analysis relies on doctors' experience and is inefficient; deep learning improves diagnostic consistency but is difficult to deploy on resource-constrained devices. FPGAs have advantages such as strong parallel computing, low power consumption, and reconfigurability. The Xilinx KV260/KR260 MPSoC integrates ARM processors and FPGA logic, making it an ideal platform for edge AI.

## Analysis of Core Technical Architecture

**Data Source and Preprocessing**: Adopt the public ECG dataset from KaggleHub, processed via filtering (removing baseline drift/power line interference), normalization, and segmentation into fixed windows.
**1D-CNN Design**: 1D convolution kernels capture local temporal features of ECG (e.g., QRS complexes), and multi-layer convolution and pooling extract abstract features.
**TCN Integration**: Expand receptive fields via dilated convolution, mitigate gradient vanishing with residual connections to model long-term dependencies; the hybrid architecture of 1D-CNN and TCN complements each other to improve classification performance.

## Model Training and Optimization Strategies

**Training Strategy**: Supervised learning optimized with cross-entropy loss, using Dropout and data augmentation (adding noise/time shifts) for regularization; dynamic learning rate adjustment + early stopping to prevent overfitting.
**Compression and Quantization**: Reduce parameter count via pruning and knowledge distillation; convert floating-point to fixed-point quantization to lower storage and computation costs, balancing model size, speed, and accuracy.

## FPGA-SoC Deployment Details

**Hardware Selection**: Xilinx KV260/KR260 MPSoC (ARM processor handles control/preprocessing, FPGA accelerates inference).
**Deployment Process**: Convert the model to Vitis AI's DPU format, perform operator fusion/memory optimization/pipeline design, compile into a bitstream and burn it to the FPGA.
**Performance Evaluation**: Compare with cloud models, evaluate metrics such as classification accuracy, inference latency, power consumption, and throughput; achieve low-latency edge inference with acceptable accuracy loss.

## Core Value of Medical AI Edge Computing

**Real-time Performance**: Local instant analysis meets low-latency requirements for scenarios like emergency care.
**Data Privacy**: Local processing avoids the risk of sensitive medical data leakage from uploading.
**Cost-effectiveness**: No long-term cloud service fees, and not limited by network connectivity.

## Project Summary and Future Outlook

The project achieves high-precision ECG classification and FPGA edge deployment, providing a reference for the industrialization of medical AI. In the future, we will develop dedicated medical AI chips and combine federated learning to enable multi-institution data sharing for training, improving the model's generalization ability.