心电监测系统数据分析的智能方法_毕业论文.pdf

心电监测系统数据分析的智能方法

心血管疾病一直严重威胁着人类的生命健康安全，高居全球非传染性疾病死因

首位。 远程心电监测系统能以其便捷高效的诊疗服务模式对预防、 检测和治疗心血

管疾病发挥着重要作用。 但是，如何实现高效率、 高精度的心电信号智能化分析，

低、 精度不高、 适应性差、 泛化能力弱及智能化水 平低等问题，不能满足对心电信

(1) 提出了 一种基于生成对抗网络（Generative Adversarial Networks，GAN）的

心电信号智能降噪方法。 针对远程环境下采集的ECG信号含有多种类型噪声干扰的

问题，本文以一种新的对抗降噪视角将GAN模型的分布特征学习能力应用到ECG信

模型的训练过程，从局部和全局 的角度捕获ECG信号的重要特征，使降噪后的ECG

信号能够保留其医用价值而不失真。 基于MIT-BIH心电数据库的实验结果表明，本

文 所提出的方法优于其他先进方法，并将信噪比SNR平均提升了约62%，具有良好

的降噪能力，适应性强。

(2) 提出了一种基于编码器-解码器（Encoder-Decoder）模型的心电信号智能分

心电信号中的关键 点位置，平均灵敏度和查准率结果分别高达99.62%和99.81%，并

且能在噪声干扰的情况下取得90%以上的灵敏度和查准率。

(3) 提出了一种人机协同知识表示的可解释性心律失常分类方法。 针对心律失

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常分类识别问题，提出了一种适用于ECG信号特征的可解释性心律失常分类识别方

同知识表示用 于心律失常分类识别，提高了模型的可解释性。利 用自编码器结合监

督学习和无监督学习设计了一种两任务学习方法生成人机 协同知识表示，有利于提

环中（Human-in-the-loop，HIL）的交互诊断机制来干预深度模型的推理过程，以此

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心电监测系统数据分析的智能方法

Abstract

As the first cause of death of non-communicable diseases in the world, cardiovascular

disease has been a serious threat to human life and health. The remote ECG monitoring

system plays an important role in the prevention, detection and treatment of cardiovascular

diseases with its convenient and efficient diagnosis mode. However, how to achieve intelli-

gent analysis of ECG signals with high efficiency and accuracy is the main challenge faced

require external data assumptions, which has strong capabilities of feature extraction gener-

alization. AI provides powerful technical support and tools for promoting the development

of new smart medical systems. Aiming at key tasks such as noise reduction, dielineation,

arrhythmia classification in the remote ECG analysis process, this paper studies the applica-

tion of AI technology in the intelligent analysis of ECG signals from the level of algorithm

and model, thus improving the intelligent level of the remote ECG analysis process.

The main work and academic contributions of this paper are as follows.

(1) A method of noise reduction for ECG signals based on generative adversarial net-

work (GAN) is proposed. Aiming at the problem that ECG signals collected in the remote

environment contain various types of noise, this paper applies the distribution feature learn-

results based on MIT-BIH Arrhythmia database show that the proposed method is superior

to other methods, and the SNR is improved by about 62% on average. The proposed method

has good noise reduction ability and strong adaptability.

(2) A deep learning method for ECG signal delineation based on encoder-decoder

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model is proposed. In view of the diversity and variability of ECG signals, this paper uses

the encoder-decoder model with superior feature extraction capability to extract the hid-

den features in ECG signals and generate the corresponding rectangular wave to delineate

both the onset and offset of the P wave, the QRS complex, and the T wave in ECG signals.

Furthermore, this paper designs a knowledge encoding and alignment method to integrate

different types of medical knowledge into the ECG delineation process, which improves

the delineation performance of the model for ECG signals and the adaptability to different

and diagnosis of multi-class arrhythmias, an interpretable arrhythmia classification method

suitable for ECG signal characteristics is proposed. Aiming at the end-to-end “black box”

problem of the deep classification model, a human-machine collaborative knowledge repre-

sentation is designed to improve the interpretability of the model for automatic classification

of arrhythmia. Combining supervised learning and unsupervised learning, the proposed ap-

proach designs a two-task learning method based on an AutoEncoder (AE) to encode ECG

signals into human-machine collaborative knowledge representation. Aiming at the prob-

lem of poor interaction of the classification model, a human-in-the-loop (HIL) mechanism

is established to allow human intervention with the inference of the neural network to im-

prove the model’s interactive ability and classification performance. Experiments and eval-

problems caused by the telemedicine environment. It is of great significance for the early

prevention, detection and treatment of cardiovascular diseases, improving the intelligent

level of the remote ECG monitoring system, and alleviating the lack of uneven medical

resources and the imbalance of the medical system in our country.

Key Words: Remote ECG Monitoring System; ECG Data Analysis; Artificial Intelligence;

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心电监测系统数据分析的智能方法

Interpretable Classification;

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