scholarly journals Identification of Cardiac Diseases from(ECG) Signals based on Fractal Analysis

2014 ◽  
Vol 13 (6) ◽  
pp. 4556-4565
Author(s):  
Rashiq Marie ◽  
Maram H. Al Alfi
Keyword(s):  
Heart Diseases ◽  
Healthy Person ◽  
Spectral Density Function ◽  
Cardiac Diseases ◽  
Power Spectral Density Function ◽  
Ecg Signals ◽  
Power Spectral ◽  
Fractal Signal ◽  
Power Spectrum Method ◽  
Proper Analysis

This paper investigates the use of fractal geometry for analyzing ECG time series signals. A technique of identifying cardiac diseases is proposed which is based on estimation of Fractal Dimension (FD) of ECG recordings. Using this approach, variations in texture across an ECG signal can be characterized in terms of variations in the FD values. An overview of methods for computing the FD is presented focusing on the Power Spectrum Method (PSM) that makes use of the characteristic of Power Spectral Density Function (PSDF) of a Random Scaling Fractal Signal. A 20 dataset of ECG signals taken from MIT-BIH arrhythmia database has been utilized to estimate the FD, which established ranges of FD for healthy person and persons with various heart diseases. The obtained ranges of FD are presented in tabular fashion with proper analysis. Moreover, the experimental results showing comparison of Normal and Abnormal (arrhythmia) ECG signals and demonstrated that the PSM shows a better distinguish between the ECG signals for healthy and non-healthy persons versus the other methods.

Spectral analysis of palaeomagnetic time series and the geomagnetic spectrum

1982 ◽  
Vol 306 (1492) ◽  
pp. 203-209 ◽  
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Spectral Density ◽  
Density Function ◽  
Power Density ◽  
Spectral Density Function ◽  
Power Spectral Density Function ◽  
Power Spectral ◽  
Polarity Reversals ◽  
The Continuum

There are now sufficient archaeomagnetic data from rapidly deposited sediments and baked clays to start bridging the gap in the geomagnetic spectrum between the frequency ranges covered by observatory records and polarity reversals. The form of the continuum spectrum of internal origin can be only loosely constrained but is broadly consistent with earlier speculations. The power spectral density function appears to increase rapidly with period up to periods of about 60 years, then more slowly up to a plateau in the region of 10 4 to 10 5 years, and thereafter starts to fall. There is somewhat inconclusive evidence for a drop in power density at periods around 10 2 years. Prospects for refining the spectrum are excellent.

Detection of Heart Defects using Electrocardiogram (ECG)

2018 ◽  
Vol 4 (11) ◽  
Author(s):  
Kenil Shah ◽  
Mayur Rane ◽  
Dr. Vahid Emamian
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Heart Diseases ◽  
Heart Defects ◽  
Healthy Person ◽  
Feature Extraction Method ◽  
Ecg Signals ◽  
Processing Techniques ◽  
Electrocardiogram Ecg ◽  
Ecg Data

Electrocardiogram (ECG) signals are vital to identifying cardiovascular disease. The numerous availability of signal processing and neural networks techniques for processing of ECG signals has inspired us to do research on extracting features of ECG signals to identify different cardiovascular diseases. We distinguish between a healthy person ECG data and person having disease ECG data using signal processing and neural network toolbox in Matlab. The data was downloaded from physiobank. To distinguish normal and abnormal ECG, Neural network is used. Feature extraction method is used to identify heart diseases. The diseases that are identified include Tachycardia, Bradycardia, first- degree Atrioventricular (AV) and a healthy person. Subsequently, ECG signals are very noisy; signal processing techniques are used to remove the noise impurity. The heart rate can be calculated by detecting the distance between R-R intervals of the signal. The algorithm successfully distinguished between normal and abnormal ECG data.

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