使用基于遗传算法特征提取的深度学习对心电图信号进行分类
Hossein Khezripour1, Saadat Pour Mozaffari2, Midia Reshadi1
1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Biomedical physics & engineering express
|June 7, 2023
概括
这项研究引入了一种新的深度学习方法,用于使用心电图 (ECG) 信号对心律不整进行分类. 该方法在识别各种心律状况方面取得了高精度,有助于及时诊断.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 心律不整需要及时诊断才能有效治疗.
- 电心电图 (ECG) 信号对于识别心律异常至关重要.
- 准确地分类各种心律失常仍然是医学研究中的一个挑战.
研究的目的:
- 开发和评估一种新的基于深度学习的方法,用于从心电图信号分类心律不整.
- 为了提高心电图信号分类的灵敏度和准确性,用于各种心脏病.
- 通过将心电图信号分为不同的节奏类别来诊断心律疾病.
主要方法:
- 使用消除噪音的过器预处理了心电图信号.
- 基于波纹分解能和PQRS形态特征的特征提取使用了离散波纹转换 (DWT).
- 利用遗传算法来减少特征向量,并优化人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 的权重.
- 为信号分类实施了深度学习框架.
主要成果:
- 提出的方法实现了高学习准确性:在ANN培训中99.9%,在ANN测试中88.92%.
- 安菲斯的准确性也相当于此:在训练方面达到99.8%,在测试方面达到88.83%.
- 该分类成功地区分了正常心跳和各种心律不整,包括充血性心力衰竭,心室心律不整,心房动和心房动.
结论:
- 开发的深度学习方法显示了使用心电图信号准确地分类心律失常的显著前景.
- 该方法为诊断心律疾病提供了灵敏有效的工具.
- 代码技术,遗传算法和深度学习模型 (ANN,ANFIS) 的结合为医疗信号分析提供了一个强大的框架.
相关概念视频
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01:29Electrocardiogram
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01:28Electrocardiogram Fundamentals
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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
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Parts of an ECG
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Definition
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01:25Correlation between ECG and Cardiac Cycle
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