Abstract
This paper describes an automatic heartbeat recognition based on QRS detection, feature extraction and classification. In this paper five different type of ECG beats of MIT BIH arrhythmia database are automatically classified. The proposed method involves QRS complex detection based on the differences and approximation derivation, inversion and threshold method. The computation of combined Discrete Wavelet Transform (DWT) and Dual Tree Complex Wavelet Transform (DTCWT) of hybrid features coefficients are obtained from the QRS segmented beat from ECG signal which are then used as a feature vector. Then the feature vectors are given to Extreme Learning Machine (ELM) and k- Nearest Neighbor (kNN) classifier for automatic classification of heartbeat. The performance of the proposed system is measured by sensitivity, specificity and accuracy measures.
Highlights
The Electrocardiogram is the electrical activity of the heart which is very important to diagnose the heart disease
To evaluate the performance of the proposed system using performance metrics such as sensitivity (Se), specificity (Sp) and Accuracy (ACC) which are given as follows: Sensitivity defines the ratio of number of correctly detected events to the total number of events in the given events are calculated by below equation: TP Se = TP + FN × 100 Where True Positive (TP) defines the total number of events and False Negative (FN) indicates the number of missed events
In this paper supervised learning approach of k- Nearest Neighbor (kNN) and Extreme Learning Machine (ELM) classifier is used as a classifier because it provides good results compared to unsupervised classifier and it is easy to implement
Summary
The Electrocardiogram is the electrical activity of the heart which is very important to diagnose the heart disease. Arrhythmia is a term used to define the ECG shape in abnormality condition. It is a common term in heart beat diagnosis and it differs from normal sinus rhythm (Tsipouras & Fotiadis, 2004) [2]. The second type is formed by a set of irregular heartbeat and is called as rhythmic arrhythmias (Eduardo et al, 2015) [3]. The detection and classification of arrhythmias are more important in clinical cardiology in the time of real time application. This is obtained through extracted waves from ECG signal (Tsipouras et al, 2005) [4]
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