Identification of Premature Ventricular Contraction (PVC) of Electrocardiogram using Statistical Tools and Non-Linear Analysis

Article ID

P415R

Identification of Premature Ventricular Contraction (PVC) of Electrocardiogram using Statistical Tools and Non-Linear Analysis

Farhana Akter Mou
Farhana Akter Mou University of Asia Pacific (UAP)
Effat Jerin
Effat Jerin
Md. Abdullah Al Mahmud
Md. Abdullah Al Mahmud University of Asia Pacific
A.H.M Zadidul Karim
A.H.M Zadidul Karim
DOI

Abstract

Non-linear analysis is a useful technique in a medical field specially in cardiac cases. Statistics tools & Non-linear parameters have shown potentiality to the identification of diseases, especially in the analysis of biomedical signals like electrocardiogram (ECG). In this work, premature ventricular contraction (i.e abnormality) in ECG signals has been analysed using various non-linear techniques. First, the ECG signal is processed through a series of steps to extract the QRS complex. From this extracted feature, bit-to-bit interval (BBI) and instantaneous heart rate (IHR) have been calculated.

Identification of Premature Ventricular Contraction (PVC) of Electrocardiogram using Statistical Tools and Non-Linear Analysis

Non-linear analysis is a useful technique in a medical field specially in cardiac cases. Statistics tools & Non-linear parameters have shown potentiality to the identification of diseases, especially in the analysis of biomedical signals like electrocardiogram (ECG). In this work, premature ventricular contraction (i.e abnormality) in ECG signals has been analysed using various non-linear techniques. First, the ECG signal is processed through a series of steps to extract the QRS complex. From this extracted feature, bit-to-bit interval (BBI) and instantaneous heart rate (IHR) have been calculated.

Farhana Akter Mou
Farhana Akter Mou University of Asia Pacific (UAP)
Effat Jerin
Effat Jerin
Md. Abdullah Al Mahmud
Md. Abdullah Al Mahmud University of Asia Pacific
A.H.M Zadidul Karim
A.H.M Zadidul Karim

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Farhana Akter Mou. 2016. “. Global Journal of Research in Engineering – F: Electrical & Electronic GJRE-F Volume 16 (GJRE Volume 16 Issue F5): .

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Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

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GJRE-F Classification: FOR Code: 090699
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Identification of Premature Ventricular Contraction (PVC) of Electrocardiogram using Statistical Tools and Non-Linear Analysis

Farhana Akter Mou
Farhana Akter Mou University of Asia Pacific (UAP)
Effat Jerin
Effat Jerin
Md. Abdullah Al Mahmud
Md. Abdullah Al Mahmud University of Asia Pacific
A.H.M Zadidul Karim
A.H.M Zadidul Karim

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