Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641-008, Tamil Nadu, India.
Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Trichy, 621112, Tamil Nadu, India.
Med Biol Eng Comput. 2023 Sep;61(9):2453-2466. doi: 10.1007/s11517-023-02839-6. Epub 2023 May 5.
Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal. Input data are taken from the MIT-BIH arrhythmia dataset. Then the input data was pre-processed using Python in Jupyter Notebook which run the code in an isolated manner and was able to keep code, formula, comments, and images. Then, Residual Exemplars Local Binary Pattern is applied for extracting statistical features. The extracted features are given to ensemble classifiers, like Support vector machines (SVM), Naive Bayes (NB), and random forest (RF) for classifying the arrhythmia as normal (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). The proposed AD-Ensemble SVM-NB-RF method is implemented in Python. The proposed AD-Ensemble SVM-NB-RF method is 44.57%, 52.41%, and 29.49% higher accuracy; 2.01%, 3.33%, and 3.19% higher area under the curve (AUC); and 21.52%, 23.05%, and 12.68% better F-Measure compared with existing models, like multi-model depending on the ensemble of deep learning for ECG heartbeats arrhythmia categorization (AD-Ensemble CNN-LSTM-RRHOS), ECG signal categorization utilizing VGGNet: a neural network based classification method (AD-Ensemble CNN-LSTM) and higher performance arrhythmic heartbeat categorization utilizing ensemble learning along PSD based feature extraction method (AD-Ensemble MLP-NB-RF).
心电图（ECG）是一种非侵入性医学工具，可揭示人体心脏的节律和功能。这在心脏病检测中广泛应用，包括心律失常。心律失常是一种广义的术语，用于描述异常的心跳节律，可以分为许多类别。在心脏病人监测系统中，通过心律失常分类提供自动心电图分析。它可以帮助心脏病专家诊断心电图信号。在这项工作中，提出了一种基于集合分类器的心律失常检测方法，使用心电图信号。输入数据来自麻省理工学院-比彻心律失常数据集。然后，使用 Python 在 Jupyter Notebook 中对输入数据进行预处理，Jupyter Notebook 以隔离的方式运行代码，能够保存代码、公式、注释和图像。然后，应用残差示例局部二值模式提取统计特征。提取的特征被提供给集合分类器，如支持向量机（SVM）、朴素贝叶斯（NB）和随机森林（RF），用于将心律失常分类为正常（N）、室上性异位搏动（S）、室性异位搏动（V）、融合搏动（F）和未知搏动（Q）。所提出的 AD-Ensemble SVM-NB-RF 方法在 Python 中实现。与现有的模型相比，所提出的 AD-Ensemble SVM-NB-RF 方法具有更高的准确性（44.57%、52.41%和 29.49%）；更高的曲线下面积（AUC）（2.01%、3.33%和 3.19%）；以及更好的 F-Measure（21.52%、23.05%和 12.68%），如多模型基于深度学习的 ECG 心跳心律失常分类（AD-Ensemble CNN-LSTM-RRHOS）、利用 VGGNet 的 ECG 信号分类：一种基于神经网络的分类方法（AD-Ensemble CNN-LSTM）和利用基于 PSD 的特征提取方法的集合学习的更高性能心律失常心跳分类（AD-Ensemble MLP-NB-RF）。
