Synopsis Presentation on “ ECG R-Peaks Detection ” Sr. No.Name of The Students Roll No Group No Presented by Guided by Prof. – Asst. Professor

INTRODUCTION ECG - ECG stands for Electrocardiogram. ECG is representative signal containing information about the condition of the heart. It is measure of the electrical activity associated with the heart. It is characterized with different frequency content QRS complex, P & T waves.

Figure :- ECG signal waveform

P wave – ECG deflection representing atrial depolarization. Atrial repolarization occures during ventricular depolarization & is obscured. QRS wave – ECG deflection representing ventricular depolarization. T wave- ECG deflection representing ventricular Repolarization.

R-peaks detection The basic task of electrocardiogram (ECG) processing is R- peaks detection. There are some difficulties one can encounter in processing ECG: irregular distance between peaks, irregular peak form, presence of low-frequency component in ECG due to patient breathing etc. To solve the task the processing should contain particular stages to reduce influence of those factors. The aim is to show results of processing in main pipeline stages.

Figure :- R peak detections

ECG signals affected by noises such as baseline wandering, power line interference, mascular noise, and high frequency noises during data acquisition. The effect of noise on the repeatability of computer measured PR interval, QRS duration, QT interval and ST level were examined.

In order to eliminate the noises present in the ECG signal we use different types of transformation methods.

TRANSFORMATION METHOD OF ECG DETECTION FFT TRANSFORM HILBERT TRANSFORM WAVELET TRANSFORM Fig: ECG Detection Method

Hilbert Transform The accurate detection algorithm of QRS wave,R peak height,T peak,ECG base modulation technique based on first derivative of Hilbert Transform is proposed.First derivative which is implemented on smooth ECG signal is basically a high pass filtering which allow high frequency, QRS complex and attenuate low frequencyP&T wave region. Now hilbert transform is implemented on that diffrentiated signal. Maximum amplitude of that transformed signal is found out

The disvantage of Hilbert transform is very senstitive to noise of many kind.It only work after filtering of brain wave.criteria for optimal band pass has which allow high frequency QRS complex and attenuate low frequency, P&T wave region. P&T wave are attenutade. so that normal ECG signal can not be detected.

. Figure. Original ECG Signal, Smoothed, First Derivative and Hilbert Transform (From top): File S0304, Lead I,4000 samples.

FFT TRANSFORM Fast Fourier transforms are used to identify the peaks in the ECG signal and then Neural Networks are applied to identify the diseases. In FFT( Fast Fourier transform ) that produces the signal into an infinite length of sine and cosine wave functions. However, the transform losses the information is about time domain and gives only spectral information in frequency domain and vice versa.

Fig. 3. Filtered ECG — first pass. Fig. 2. FFT filtered ECG. Fig. 1. Original ECG.

WAVELET TRANSFORM The Wavelet Transform is a time-scale representation that has been used successfully in a broad range of applications, in particular signal compression. Recently, Wavelets have been applied to several problems in Electrocardiology, including data compression, analysis of ventricular late potentials, and detection of ECG characteristic points.

The WT uses a short time interval for evaluating higher frequencies and a long time interval for lower frequencies. Due to this property, high frequency components of short duration can be observed successfully by Wavelet Transform. One of the advantage of the Wavelet Transform is that it is able to decompose signals at various resolutions, which allows accurate feature extraction from non- stationary signals like ECG.

ECG Signal Preprocessing QRS Detection P wave Detection T wave Detection Identification Fig: Structure of ECG signal Processing

Figure : Baseline removed and Denoised Signal Figure : Daubechies4 Wavelet

ADVANTAGES  One of the main advantages of wavelets is that they offer a simultaneous localization in time and frequency domain.  The second main advantage of wavelets is that, using fast wavelet transform, it is computationally very fast.  A wavelet transform can be used to decompose a signal into component wavelet. RAIPUR.

Wavelets have the great advantage of being able to separate the fine details in a signal. Very small wavelets can be used to isolate very fine details in a signal, while very large wavelets can identify coarse details.

APPLICATION Blood-pressure, heart-rate and ECG analyses Signal processing Data compression Smoothing and image denoising Fingerprint verification DNA analysis, protein analysis In Internet traffic description, for designing the services size.

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FUTURE SCOPS 1)Fingerprint verification 2)Finance (which is more surprising), for detecting the properties of quick variation of values

REFERENCE (IJACSA) International Journal of Advanced Computer Science and Applications,(Vol. 1, No.6, December 2010) International Journal on Electrical Engineering and Informatics ‐ Volume 4, Number 2, July 2012 World Academy of Science, Engineering and Technology

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