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Paper Title: Electrocardiogram features extraction approaches- A review
Authors Name: Anurag Krishna Shukla , Atul Kumar Shrivastava
Unique Id: IJRTI1707031
Published In: Volume 2 Issue 7, July-2017
Abstract: Heart diseases are one of the most dangerous diseases that lead to the cause of illness among the humans worldwide. According to the survey conducted, it was found that, heart disease are one of major cause of the death among the humans and it is growing at fast pace. Due to the change in daily unhealthy routine, several peoples face the problem of cardiovascular disease. It can be cured only by the earlier detection or by making change from unhealthy routine to healthy one. In order to keep your heart healthy and away from various diseases, ECG was developed. ECG is an electrocardiography system which is used for monitoring the functionality of the heart. ECG is done for the purpose of detecting various heart related diseases. ECG observes the movements in the heart by sensing the electrical variations in the human skin by using small sensors which are implanted on the chest of the patients. In traditional work many algorithms have been developed for detecting heart disease. It is concluded from the literature study that most of the work in biomedical ECG analysis for the prediction or detection was done only up-to the feature extraction, no further decision system development field was highlighted. This study provides an overview to the various feature extraction techniques that were used to extract the feature of P, Q, R,S and T peaks from the ECG signals.
Keywords: ECG signals, P,Q,R,S,T signal waves, Feature Extraction
Cite Article: "Electrocardiogram features extraction approaches- A review", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.2, Issue 7, page no.199 - 203, July-2017, Available :http://www.ijrti.org/papers/IJRTI1707031.pdf
Downloads: 00050
Publication Details: Published Paper ID: IJRTI1707031
Registration ID:170443
Published In: Volume 2 Issue 7, July-2017
DOI (Digital Object Identifier):
ISSN Number: 2456 - 3315
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