ECG signal analysis using wavelet transform

Dissertant

Wahhab, Husam Abd al-Husayn

Thesis advisor

Abd Allah, Hadil Nasrat

University

University of Technology

Faculty

-

Department

Department of Electrical Engineering

University Country

Iraq

Degree

Master

Degree Date

2013

English Abstract

ECG signals are the electrical activity criterion of the human heart muscle, so it is the signals that identify the condition of the heart and if the patient needs a medical treatment or not if the patient has one of the heart diseases.

The importance of ECG signals of the human leads to study, analyze, and diagnose ECG signals.

The ECG signals have nonstationary nature in its complete period, which have different waves in frequency and amplitude.

These waves are; P-wave, Qwave, R-wave, S-wave, and T-wave which have different time intervals, frequency, amplitude, and shape.

Also, ECG signal differs from person to another in shape and number of beats in one minute depending on the age of the person, weight, and sex.

These differences lead to propose a method which can deal with this type of signals.

The decomposition of ECG signal produces an approximate and detail coefficients.

The level of decomposition is chosen to be eight level decomposition, which makes it possible to pick the proper coefficients from the eight levels discarding all the unwanted levels of the coefficients as well as noise.

After completing the preprocessing of the signal and decomposition, the analysis starts to determine the locations and amplitudes of the peaks and then diagnose the signals depending on standard values.

The sensitivity (Sₑ), and positive predictivity (P+) are the performance analysis metric of any QRS detection method, the present work show high sensitivity (Sₑ) and positive predictivity (P+).

A sensitivity (Sₑ) of (99.71534%) and positive predictivity (P+) of (96.40332%) are results after running the Matlab code on ECG signals.

Main Subjects

Electronic engineering

Topics

• Heart
• Diseases
• Electrical properties
• Diagnosis
• Electrocardiography

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-417962