---
title: "CardioFusion-AI：面向信号退化的鲁棒ECG-PPG多模态融合框架"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.26000v1"
permalink: "https://feed.astralor.com/items/itm_e7e2812aa1cf698bcc48f7ee"
published: "2026-08-26T16:48:14.000Z"
score: 72
channel: "ai"
tags: ["CardioFusion-AI","ECG-PPG融合","信号质量指数","可穿戴健康监测"]
---

# CardioFusion-AI：面向信号退化的鲁棒ECG-PPG多模态融合框架

## AI 点评

> 这篇论文的价值不在于给出绝对最优融合法，而在于把“传感器丢了”和“信号脏了”分开对待。我在做多模态系统时特别容易默认两者等价，结果在真实设备上翻车。建议做可穿戴算法或医疗AI的团队重点关注其退化实验设计，而不是直接拿去用某个融合层。

## 速览

本研究提出CardioFusion-AI，用于可穿戴ECG与PPG信号的融合监测。针对运动伪影和传感器丢失导致单一模态退化的问题，框架融合了R峰/收缩峰检测、信号质量指数和逐搏脉搏传导时间估计，并在53份ICU记录及胎儿ECG数据库上验证。在六种退化机制下对比八种融合策略，注意力融合取得最低总体误差（1.66±0.43 bpm）；而在PPG完全缺失时，信号质量条件处理可接近单模态上限。研究表明，模态可用性与模态质量是自适应融合中的两个不同问题。

## 关键点

- 提出CardioFusion-AI，包含信号质量指数和逐搏脉搏传导时间估计的前端处理。
- 在53例ICU记录和胎儿ECG数据库上验证了心率估计与R峰检测性能。
- 系统比较8种融合策略，注意力融合误差最低（1.66±0.43 bpm）。
- 发现信号质量条件处理在PPG缺失时接近单模态上限，表明“模态缺失”与“信号质量”是不同问题。

## 全文

Wearable electrocardiogram (ECG) and photoplethysmogram (PPG) sensors are complementary but individually fragile: motion artifact, poor contact, and sensor dropout can degrade one or both signals. Fusion strategies that assume both modalities are equally trustworthy can become less reliable than a single clean modality under degradation. We present CardioFusion-AI, a framework whose signal-processing front end, including R-peak and systolic-peak detection, an Orphanidou-type signal-quality index, and beat-by-beat pulse transit time estimation, is validated on 53 real intensive-care recordings (848 windows; heart-rate mean absolute error 1.61 bpm for ECG and 2.78 bpm for PPG) and a real annotated fetal ECG database (R-peak F1 0.89-0.98). We then conduct a controlled synthetic degradation study comparing eight ECG-PPG fusion strategies across six degradation regimes spanning graded corruption and complete modality loss, using five independent training seeds. Attention fusion achieved the lowest descriptive overall error (1.66+/-0.43 bpm). Both adaptive gates reallocated weight toward the healthy modality under complete modality loss, but showed near-zero correlation between gate weight and signal quality under graded degradation (r = 0.10-0.24). Signal-quality conditioning produced a specific improvement under missing-PPG conditions (1.56+/-0.59 bpm), approaching the 1.48 bpm unimodal ceiling. With only five training seeds, no pairwise comparison survives Holm-corrected significance testing; effect sizes and confidence intervals are therefore reported. These results indicate that modality availability and modality quality are functionally distinct problems for adaptive fusion.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_e7e2812aa1cf698bcc48f7ee