Abstract
In this era of electronic health, healthcare data is very important because it contains information about human survival. In addition, the Internet of Things (IoT) revolution has redefined modern healthcare systems and management by providing continuous monitoring. In this case, the data related to the heart is more important and requires proper analysis. For the analysis of heart data, Electrocardiogram (ECG) is used. In this work, machine learning techniques, such as adaptive boosting (AdaBoost) is used for detecting normal sinus rhythm, atrial fibrillation (AF), and noise in ECG signals to improve the classification accuracy. The proposed model uses ECG signals as input and provides results in the form of the presence or absence of disease AF, and classifies other signals as normal, other, or noise. This article derives different features from the signal using Maximal Information Coefficient (MIC) and minimum Redundancy Maximum Relevance (mRMR) technique, and then classifies them based on their attributes. Since the ECG contains some kind of noise and irregular data streams so the purpose of this study is to remove artifacts from the ECG signal by deploying the method of Second-Order-Section (SOS) (filter) and correctly classify them. Several features were extracted to improve the detection of ECG data. Compared with existing methods, this work gives promising results and can help improve the classification accuracy of the ECG signals.
Highlights
In order to fulfill the body’s needs such as oxygen supply and nutrition, the heart is the main organ located in the chest and assists the entire circulatory system inside the body
The classification is performed after obtaining the numerical characteristics of the features
Atrial fibrillation is one of the most important cardiovascular diseases and has received great attention from computer science and engineering disciplines to automatically detect it with the help of different algorithms
Summary
In order to fulfill the body’s needs such as oxygen supply and nutrition, the heart is the main organ located in the chest and assists the entire circulatory system inside the body. The cardiac cycle has two phases of contraction or depolarization and diastole or repolarization [2]. Systole is a period when the ventricles contract to empty all blood on the other hand, extra-cardiac diastole is part of the cardiac cycle, during which the heart is emptied and refilled with blood during the systolic and repolarization period. Doctors can use electrodes and a suitable device called an electrocardiogram (ECG) to capture the signal produced by the pacemaker. An ECG is a diagnostic tool that can measure and record the electrical activity of the cardiac cycle in impeccable detail. It records the heart’s electrical pulses in vivid graphics
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