Shannon’s Energy Based Algorithm in ECG Signal Processing

Physikalisch-Technische Bundesanstalt (PTB) database is electrocardiograms (ECGs) set from healthy volunteers and patients with different heart diseases. PTB is provided for research and teaching purposes by National Metrology Institute of Germany. The analysis method of complex QRS in ECG signals f...

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Bibliographic Details
Published in:Computational and mathematical methods in medicine Vol. 2017; no. 2017; pp. 1 - 16
Main Authors: Beyramienanlou, Hamed, Lotfivand, Nasser
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017
Hindawi
Subjects:
Algorithms
Databases, Factual
Electrocardiography
Germany
Healthy Volunteers
Heart Diseases - diagnostic imaging
Humans
Models, Statistical
Predictive Value of Tests
Reproducibility of Results
Signal Processing, Computer-Assisted
Software
ISSN:1748-670X, 1748-6718, 1748-6718
Online Access:Get full text
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Summary:Physikalisch-Technische Bundesanstalt (PTB) database is electrocardiograms (ECGs) set from healthy volunteers and patients with different heart diseases. PTB is provided for research and teaching purposes by National Metrology Institute of Germany. The analysis method of complex QRS in ECG signals for diagnosis of heart disease is extremely important. In this article, a method on Shannon energy (SE) in order to detect QRS complex in 12 leads of ECG signal is provided. At first, this algorithm computes the Shannon energy (SE) and then makes an envelope of Shannon energy (SE) by using the defined threshold. Then, the signal peaks are determined. The efficiency of the algorithm is tested on 70 cases. Of all 12 standard leads, ECG signals include 840 leads of the PTB Diagnostic ECG Database (PTBDB). The algorithm shows that the Shannon energy (SE) sensitivity is equal to 99.924%, the detection error rate (DER) is equal to 0.155%, Positive Predictivity (+P) is equal to 99.922%, and Classification Accuracy (Acc) is equal to 99.846%.
Bibliography:ObjectType-Article-1
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Academic Editor: Luis J. Mena
ISSN:1748-670X
1748-6718
1748-6718
DOI:10.1155/2017/8081361