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Electrocardiogram Signal Analysis Using Zoom FFT

Kamalapriya Murugan

Central Research LaboratoryBharat Electronics

Bangalore,India

kamalapriyam@bel.co.in

Renulakshmi Ramesh

Central Reasearch LaboratoryBharat Electronics

Bangalore,India

renulakshmim@bel.co.in

AbstractThis paper deals with the analysis of ECG

signals using zoom FFT. The Zoom FFT is interesting

because it blends complex down conversion, lowpass

filtering, and sample rate change through decimation

in a spectrum analysis application. It is used when

fine spectral resolution is needed within a small

portion of a signal's overall frequency range. In the

first step an attempt was made to generate ECG

waveforms by developing a suitable MATLAB

algorithm and in the second step, analysis of the ECGis generated using zoom FFT .Then QRS complexes

were detected and each complex was used to find the

peaks of the individual waves like P and T, and also

their deviations. The results were compared with the

existing FFT methods.KeywordsElectrocardiogram (ECG), Zoom FFT, SpectralAnalysis.

INTRODUCTION

Virtually all sciences in the world contribute to themaintenance of human health and the practice of medicine.

Medical physicists and biomedical engineers support theeffective utilization of this medical science and technology astheir responsibilities to enhance human health care with thenew development of the medical tools such asElectrocardiogram (ECG). ECG is a broad term that includesseveral more heart conditions [1].

The ECG is a noninvasive and the record of variation of thebiopotential signal of the human heartbeats. The noninvasivetechnique meaning that this signal can be measured withoutentering the body at all. Electrodes are placed on the usersskin to detect the bioelectric potentials given off by the heartthat reach the skins surface. The ECG detection which showsthe information of the heart and cardiovascular condition isessential to enhance the patient living quality and appropriatetreatment. It is valuable and an important tool in diagnosing the

condition of the heart and various disorders [2].

The ECG is a realistic record of the direction andmagnitude of the electrical commotion that is generated bydepolarization and repolarization of the atria and ventricles [3].

Fig 1: ECG Signal

ECG varies from person to person due to thedifference in position, size, anatomy of the heart, age, relatively

body weight, chest configuration and various other factors.There is strong evidence that hearts electrical activity embedshighly distinctive characteristics, suitable for variousapplications and diagnosis. The ECG is characterized by arecurrent wave sequence of P, QRS, T and U wave associatedwith each beat. The QRS complex is the most striking

waveform, caused by ventricular depolarization of the humanheart. A typical ECG wave of a normal heartbeat consists of aPwave, a QRS complex, and a Twave. Fig. 1 depicts the basicshape of a healthy ECG heartbeat signal with P, Q, R, S, J, Tand U characteristics and the standard ECG intervals QTinterval, ST interval and PR interval [4],[5],[6][7].

The previously proposed method of ECG signalanalysis was based on time domain method. But this is notalways adequate to study all the features of ECG signals.Therefore the frequency representation of a signal is required.The deviations in the normal electrical patterns indicate variouscardiac disorders [8], [9], [10].

This paper is structured as follows. Section 1discusses the ECG signal. Section 3 gives a description of the

Zoom FFT .Section 4 describes proposed methods of ECGsignal analysis using Zoom FFT and section 5 concludes the

paper with results and discussions

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I. ZOOM FFT

The Zoom FFT is interesting because it blendscomplex down conversion, lowpass filtering, and sample ratechange through decimation in a spectrum analysis application.The Zoom FFT method of spectrum analysis is used when finespectral resolution is needed within a small portion of a signal'soverall frequency range. This technique is more efficient thanthe traditional FFT in such a situation.

The Zoom-FFT is a process where an input signal ismixed down to baseband and then decimated, prior to passingit into a standard FFT. It is efficiently a subset of FFT. Theadvantage is for example that if you have a sample rate of 10MHz and require at least 10Hz resolution over a smallfrequency band (say 1 KHz) then you do not need a 1 Mega

point FFT, just decimate by a factor of 4096 and use a 256point FFT which is obviously quicker. In contrast, the zoom-FFT uses digital down conversion techniques to localize thestandard FFT to a narrow band of frequencies that are centeredon a higher frequency. The zoom-FFT is used to reduce thesample rate required when analyzing narrowband signals - E.G.in HF communications. Thus, you can save a significantamount of processing power and time using this method [11].

Fig 2: Zoom FFT for Spectral analysis

Fig 3: Steps of Zoom FFT

The various advantages of the Zoom FFT includeincreased frequency domain resolution, reduced hardware costand complexity and wider spectral range. Zoom FFT finds

application in Ultrasonic blood flow analysis, R.F.communications, Mechanical stress analysis and Doppler radar.

II. ECG ANALYSIS

A lot of work has been done in the field of ECGsignal analysis using various approaches and methods [12][13].The basic principle of all the methods however involves

transformation of ECG signal using different transformationtechniques including Fourier Transform, Hilbert Transform,Wavelet transform etc. Physiological signals like ECG areconsidered to be quasi-periodic in nature. They are of finiteduration and non stationary. Hence, a technique like Fourierseries (based on sinusoids of infinite duration) is inefficient forECG.

The zoom FFT analysis of ECG Signal is done inMATLAB. MATLAB is a high performance and interactivesystem which allows solving many technical problems. Thenormal ECG wave form and the waveforms with abnormalitiesare shown in Figures 2, 3 and 4 were loaded in MATLABworkspace.

The zoom FFT algorithm was applied to the normal

ECG signal. The most important values to be analyzed areregularity of the signal, the distances between characteristic

points and interferences in the shape of single heartbeat. Signalmay also contain artifacts which are generated by externalfactors such as the movement of electrodes and the chestmuscles activity. This noise was detected and excluded before

the analysis in order to identify the exact proper ECG signal.The shifted, filtered and down sampled signal was obtainedwith the better resolution and less computation.

As a next step two ECG signals with abnormalitieswere also analyzed. The abnormalities taken into considerationare Sinus Tachycardia (Beat 145) and Sinus Bracardia (Rate of45) and the figure are shown below.

Fig 4: ECG abnormality Sinus Tachycardia

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Fig 5: ECG abnormality Sinus Bracardia.

Windowing techniques were used to get the precisespectrum and thus the information of the concentrated part. Thesignals were analyzed and accordingly windowing functionwas selected. Chebyshev, Hamming and Kaiser Window gavegood results [14].

III. RESULTS ANDDISCUSSION

The normal as well as abnormal ECG signals were

analyzed using zoom FFT and the results were compared withexisting FFT methods. The two abnormalities that wereconsidered are tachycardia and Bracardia. Model ECGwaveforms were taken from the Physionet database [15], [16].

As a part of the study spectral analysis of the normalECG signal as well as abnormal ECG signal were carried out.In the experiment, it is assumed that the processor can dealwith 2048 points FFT in maximum. Then the ECG signalmentioned above is divided into 8 parts equally. The resolutionof the spectrum got was 2048/2=1024. But for the 1/8 partspectrum which is the concentrated part, the resolution

obtained was 2048*1/2*1/8=128. Fig 7 shows thespectrum of 2048 samples FFT

After the Zoom-FFT, the spectrums for the originalsignal, shifting signal, filtered signal and down sampling signalwas obtained. It was found that the resolution was 8 times

better than FFT. Therefore this resolution is good for theanalysis. Although Zoom-FFT is a good method to get precisespectrum for the concentrated part, it still has some errorscomparing with FFT Therefore some windowing techniqueswas used after down sampling to improve the spectrum.

One of the crucial steps in the ECG analysis is toaccurately detect the different waves forming the entire cardiaccycle. Most of the studies based around wavelet transformationidentify 99.8% of ECG waveforms. Compared to the normalFFT, Zoom FFT avoids limitations of bottleneck capability ofthe processor, where in normal FFT algorithm cannot beimplemented. Quality of spectrum is far superior in zoom FFT

where by analysis of the ECG signal becomes easier.

The time consumption of the algorithm using a DSPProcessor needs to be studied, which would be our future work.Windowing after the zoom FFT gives far excellent results.

Also since the application of Zoom FFT in electrocardiology is relatively new field of research, many furtheraspects like sensitivity require further investigations in order toimprove the clinical usefulness of this novel signal processingtechnique. Simultaneously diagnostic and prognosticsignificance of FFT techniques in various fields of electrocardiology needs to be established in large clinical studies.

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ACKNOWLEDGEMENT

We would like to acknowledge the guidance andsupport of Dr.A.T.Kalghatgi, Chief Scientist CRL BEL. Wewould also thank to Dr.C.Ramesh, CRL BEL, for his technicalguidance, suggestions and constant encouragement.

REFERENCES

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