Evaluation of the analytic representation of long-record ECG and its HRV signals for congestive heart failure classification

Author

Omar, Mohamed O A ; Mohamed, Abdalla S A

Author_Institution

Biomed. Eng. Dept., Misr Univ. for Sci. & Technol., Sixth October, Egypt

fYear

2011

fDate

26-28 April 2011

Firstpage

1

Lastpage

8

Abstract

Differential diagnosis of cardiac diseases is considered a real problem in cardiology. Moreover congestive heart disease [CHF] is one of the most life-threatening where it is characterized by neurologic complications, and decreased pulmonary flow. Analysis of long-record ECG trace and/or the extracted HRV signal need to consider the presence of non-stationary. In this work, Hilbert transform is applied to get the analytic representation of these signals. Instantaneous amplitude (envelop); phase; and frequency were calculated. K-means algorithm was applied on these outputs to classify CHF. Classification results were promising with ECG (92.1%) more than HRV (75.85).

Keywords

Hilbert transforms; electrocardiography; medical signal processing; patient diagnosis; ECG; HRV signal; Hilbert transform; K-means algorithm; cardiac diseases diagnosis; cardiology; congestive heart disease; congestive heart failure classification; electrocardiogram; heart rate variability signal; neurologic complication; pulmonary flow; Biomedical measurements; Electrocardiography; Heart rate variability; Electrocardiogram [ECG]; Heart rate variability [HRV]; Hilbert Transform; Hilbert spectrum; Instantaneous frequency; k-means; non-stationary time series;

fLanguage

English

Publisher

ieee

Conference_Titel

Radio Science Conference (NRSC), 2011 28th National

Conference_Location

Cairo

Print_ISBN

978-1-61284-805-1

Type

conf

DOI

10.1109/NRSC.2011.5873616

Filename

5873616