Book ChapterDOI
Predicting Cardiac Arrhythmia Using QRS Detection and Multilayer Perceptron
Harika Gundala,Mayank Sethia,Mehul Sethia,Shreyas Gonjari,Akshay Gugale,Rajeshkannan Regunathan +5 more
- pp 781-789
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TLDR
The focus is mainly on predicting whether the patient has cardiac arrhythmia or not based on electrocardiography (ECG) reports, and Pan–Tompkins algorithm has been used for QRS detection which predicts the abnormal deflections that lead to the arrhythmmic events.Abstract:
Most deaths occur around the world because of cardiac disorders. Cardiac rhythm disorders may cause severe strokes and heart diseases. Arrhythmias occur when the electric signals to the heart are irregular or not working properly. Mostly, these irregular heartbeats feel like racing hearts. Many times, arrhythmias are harmless, but if they are abnormal or they result due to damaged heart, then they can be fatal. Cardiac arrhythmia, being the leading cause of death in both men and women, can be prevented with the early and correct diagnosis. In this paper, the focus is mainly on predicting whether the patient has cardiac arrhythmia or not based on electrocardiography (ECG) reports. Pan–Tompkins algorithm has been used for QRS detection which predicts the abnormal deflections that lead to the arrhythmic events. The same reports have been used to classify which type of cardiac arrhythmia the patient has using Multilayer Perceptron (MLP) algorithm.read more
References
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Journal ArticleDOI
A Real-Time QRS Detection Algorithm
Jiapu Pan,Willis J. Tompkins +1 more
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Journal ArticleDOI
The impact of the MIT-BIH Arrhythmia Database
George B. Moody,Roger G. Mark +1 more
TL;DR: The history of the database, its contents, what is learned about database design and construction, and some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database are reviewed.
Journal ArticleDOI
A deep learning approach for ECG-based heartbeat classification for arrhythmia detection
Giovanna Sannino,G. De Pietro +1 more
TL;DR: A novel deep learning approach for ECG beat classification is proposed that is not only more efficient than the state of the art in terms of accuracy, but also competitive in Terms of sensitivity and specificity.
Proceedings ArticleDOI
ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm
TL;DR: Experimental results demonstrate that the approach adopted better classifies ECG signals, and four types of arrhythmias were distinguished with 93% accuracy.
Proceedings ArticleDOI
Diagnosis of cardiac arrhythmia using kernel difference weighted KNN classifier
TL;DR: Experimental results on the UCI cardiac arrhythmia database indicate that, KDF-WKNN is superior to the nearest neighbor classifier, and is very competitive while compared with several state-of-the-art methods in terms of classification accuracy.