使用短期心电图信号和机器学习进行心律失常的自动分类
Amar Bahadur Biswakarma1, Jagdeep Rahul2, Kurmendra Kurmendra3
1Electronics and Communication Engineering, Rajiv Gandhi University, Doimukh, Itanagar, Itanagar, Arunachal Pradesh, 791112, INDIA.
Biomedical physics & engineering express
|January 17, 2025
概括
这项研究引入了一种有效的方法来检测心律失常,使用信号处理和机器学习. 支持矢量机器分类器在从心电图数据中识别五种类型的心律异常时取得了高准确性.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 准确的心律失常检测对于预防心脏突然死亡至关重要.
- 现有的心电图 (ECG) 信号分析方法面临着噪音和复杂心律失常的挑战.
- 需要自动分类系统来提高临床环境中的诊断效率.
研究的目的:
- 开发和评估一个强大的自动化系统来分类五种类型的心律失常.
- 评估信号预处理技术和机器学习分类器的有效性,以检测心律失常.
主要方法:
- 使用双阶段离散波段转换 (DWT) 和中间波器预处理心电图信号以消除噪声.
- 在信号细分后提取QRS区域以提取特征.
- 提取了9个时间特征,并用于训练6个不同的机器学习分类器,包括支持矢量机 (SVM) 和合集树.
主要成果:
- 支持矢量机 (SVM) 和合集树分类器在分类正常节拍和四种类型的失律 (PVC,PAC,R-BBB,L-BBB) 中表现出卓越的性能.
- 使用高斯核的SVM分类器实现了高性能指标:97.44%的灵敏度,99.36%的特异性,97.44%的积极预测值,98.97%的准确性.
- 使用MIT-BIH心律失常数据库 (MIT-BIH AD) 进行绩效评估.
结论:
- 建议的信号预处理和特征提取的综合方法有效地对心律失常进行分类.
- SVM分类器的高性能表明其在临床应用和试验中具有可靠的自动心律失常检测潜力.
关键词:
心脏节律失常 心脏节律失常这是一个ECGECGECGECGECG.L-BBBB L-BBBB L-BBBB L-BBBB L-BBBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB L-BBB B-BBB L-BBBB L-BBBB BBB L-BBBBB L-BBBBB L-BBBBB L-BBBBB L-BB机器学习是机器学习.这就是为什么PAC PAC PAC.在PVC中,PVC是PVC.R-BBBBB 这是一个R-BBBB相关概念视频
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias
182
Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
182
Mechanism of Cardiac Arrhythmias
885
Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
885
Electrocardiogram
2.1K
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
2.1K
Disturbances in Heart Rhythm
892
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
892
ECG Interpretation of Rhythms
453
An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
453
Correlation between ECG and Cardiac Cycle
3.3K
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...
3.3K


