通过使用来自心电图和PPG的多特征时间序列数据对心房的分类进行深度学习方法
Bader Aldughayfiq1, Farzeen Ashfaq2, N Z Jhanjhi2
1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|July 29, 2023
概括
这项研究开发了一个深度学习模型,使用光电图 (PPG) 信号来准确检测心房动 (AF). 混合1D CNN和BiLSTM网络实现了95%的准确性,提供了一个有前途的非侵入性AF分类方法.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 人工智能在医学中的应用
背景情况:
- 心房动 (AF) 是一种常见的心律失常,对健康有重大影响.
- 非侵入性检测方法,如心电图 (ECG) 和光电图 (PPG) 正在获得引力.
- 现有的基于ECG的AF检测面临挑战,突出了像PPG这样的替代或补充方法的需要.
研究的目的:
- 使用光电脑图 (PPG) 时间序列数据和深度学习来分类心房动 (AF) 和非AF.
- 为了解决深度学习对传输PPG信号进行AF检测的不足研究的应用.
- 提出一种新的方法,将ECG和PPG信号整合在一起,以加强AF分类.
主要方法:
- 采用混合深度神经网络架构,结合1D卷积神经网络 (CNN) 和双向长短期记忆 (BiLSTM).
- 使用传输式PPG时间序列数据,与心电图信号集成,作为深度学习模型的多功能输入.
- 在测试数据上训练和评估AF分类的深度学习模型.
主要成果:
- 混合型1D CNN和BiLSTM模型在测试数据上识别心房动时达到95%的高准确性.
- 该分类模型表现出强的性能,精度为0.88和回忆 (灵敏度) 为0.85.
- 计算出F1得分为0.84,表明模型在区分AF和非AF病例方面的整体有效性.
结论:
- 拟议的混合深度学习模型有效地使用PPG信号对心房动进行分类,实现高精度和可靠性.
- 将PPG信号与深度学习集成为传统基于ECG的AF检测提供了一个有希望的,非侵入性的替代方案或补充.
- 该模型的强大性能指标表明其在AF查和诊断中具有实际临床应用的潜力.
关键词:
一维卷积神经网络 1D卷积神经网络在BiLSTM网络中,心房动是心房动的一种.深度学习是一种深度学习.电心电图 (ECG) 是一种心电图.摄影复合发电图谱 (Photoplethysmogram) 是一种摄影图谱.更多相关视频
相关概念视频
01:25ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias
45
Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
45
01:25Correlation between ECG and Cardiac Cycle
7.0K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
7.0K
01:29Dysrhythmias III: Characteristics of Dysrhythmias
12
Dysrhythmias, also known as arrhythmias, are irregular heart rhythms that result from abnormal electrical activity in the heart, affecting its ability to circulate blood efficiently. Tachyarrhythmias, a subset of dysrhythmias, are characterized by abnormally fast heart rates exceeding 100 beats per minute. Here are some types of tachyarrhythmias with their distinct ECG features:Sinus Tachycardia:Sinus tachycardia presents a regular heart rhythm with an increased rate of 101-180 beats per...
12
01:28Dysrhythmias II: Classification of Tachyarrhythmias
11
Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
11