Efficient VLSI Architecture for Data Analysis of ECG

EFFICIENT VLSI ARCHITECTURE FOR
DATA ANALYSIS OF ECG
Presented by
1.CH.REVATHI 16MQ1A0407
2.B.N.SULOCHANA 16MQ1A0404
3.P.N.V.LAKSHMI 16MQ1A0463
4.A.SAI RAMESH 17MQ5A0412
Under Esteemed Guidence
By
Mr.K.SAI SUDHEER,M.Tech.,
Assistant Professor

CONTENTS
 Abstract
 Introduction
 Existed system
 Dis-advantages of the Existed System
 Proposed system
 Explanation of each block
 Simulation
 Advantages
 Applications
 References

ABSTRACT
 In this project we will use the efficient VLSI architecture for the data
analysis of ECG.
 Here firstly the data will be saved in digital form.
 The normal heart rate of the lady is taken and saved in digital form.
 when ever we want to know the condition of the baby,again we will note the
heart rate of the mother and baby.
 After applying filter, digital report is obtained. In this digital report the health
condition of baby is present.

 If the obtained digital form of baby from filter is exactly same as the stored
digital form of the reference baby signal .
 Then we can say that the baby is in Healthy condition.
 So that we can generate the baby's ECG signal in digital form from it.
 Hence this architecture will give automatically the health report of baby’s
condition and mother’s condition.

INTRODUCTION
 Recently, due to the huge and remarkable advancement in biotechnology,
the development of a dedicated hardware system for accurate analysis,
classification and prediction of ECG Signals in real time has become
possible.
 The main requirements are low-power consumption and low-energy
operation of hardware so that we can increase the battery life along with
the small area for wear ability.
Many attempts succeeded to implement ECG signal processing and
classification of systems in hardware

EXISTED SYSTEM
 In existing one's no ECGS systems are used.
 If ECG is not used we can not predict the health condition of the
baby ,then there will be loss of mother's and baby's life also.

DISADVANTAGES OF EXISTED
SYSTEM
 No indication of threats in health condition.
 Loss of human life.

MEMORY
(BABY HEART BEAT)
PROPOSED SYSTEM
BABY
HEALTH
REPORT
ANALYSER

Continution……….
 By using ECG the health condition of both baby and mother will be given.
 The record of that patient will be saved effectively in memory.
 Here first mother ECG signal will be taken.
 General baby ECG signal is also stored in digitl form.
 Next mother + baby ECG signal will be taken.
 At last by analyzing these two baby binary ECG signal is appeared.
 Now obtained signal is compared with the baby's digial form.
 From this we can say whether the baby is in Healthy condition or not.

Mother and Fetal Combined ECG
signal

Comparison of Mother and Baby
ECG Signal

EXPLANATION OF EACH
BLOCK
• Binary signal
• Memory
• Analyser
• ECG Signal

BINARY SIGNAL
• Binary signal consists of only two possible values.
• These values are represented by the binary digits, or bits, 1 and 0.
• We are converting the analog signal in to binary data, as we are required
binary data for the procedure.

MEMORY
• Memory is used to store the bits in cells.
• Here we are storing the binary data of the mother.
• We are using this data to extract the baby signal from mother + baby signal
at the required month.

ANALYZER
 Analyzer is a tool used to analyze the given input data.
 In this Analyer the inputs are Mother+Baby Signal Data and Mother Signal
Data.
 The Output of the Analyser is Baby Signal Data Which Comprises of that
Baby's Heart Beat.

ECG SIGNAL
 An ECG signal will be acquired using a bio-potential amplifier and then
displayed using instrumentation software.
 where a gain control will be created to adjust its amplitude.
 Finally, the recorded ECG will be analyzed.
 Electrocardiography is the process of producing an electrocardiograma
recording – a graph of voltage versus time – of the electrical activity of the
heart[4] using electrodes placed on the skin.

 These electrodes detect the small electrical changes that are a consequence
of cardiac muscle depolarization followed by repolarization during each
cardiac cycle (heartbeat).
There are three main components to an ECG:
 the P wave, which represents the depolarization of the atria.
 the QRS complex, which represents the depolarization of the ventricles.
 the T wave, which represents the repolarization of the ventricles.

Efficient VLSI Architecture for Data Analysis of ECG

Convert PQRST wave to Heart Rate

Heart Rate of a Pregnant lady
• The normal heart rate of a woman is about 73 to 77 beats per minute (bpm).
It increases to 86 to 90 bpm as the pregnancy progresses.
Explanation:
First trimester
 Heart rate changes begin in the first trimester.
 During the first trimester, the heart rate increases to 80-84 bpm.

 Second trimester
 By the beginning of the second trimester, the heart is pumping 30 % to 50
% more blood than normal.
 The smooth muscle relaxes and the arteries dilate to handle the increase in
circulating blood volume while maintaining normal blood pressure.
 However, the heart rate increases to 82-86 bpm.

 Third trimester
 By the end of the third trimester,
the heart is pumping from 40 % to
90 % more blood than before
pregnancy.
 The resting heart rate increases to
86-90 bpm.

SOFTWARE USED
 Xilinx 14.7 ISE software
 FPGA family: virtex 6
 Windows 10 with 64 bit operating system

ADVANTAGES
• Detects very fast.
• Accurate Signals will be obtained.
• We can Reduce the Area of Design by Implementing this in Chip Level.

APPLICATIONS
 Useful in Emergency conditions.
 Health condition will be stored and compared when ever we want.

REFERENCES
• N. Bayasi, T. Tekeste, H. Saleh, A. Khandoker, B. Mohammad, and M.
Ismail, “Adaptive technique for P and T wave delineation in
electrocardiogram signals,” in Proc. IEEE 36th Annu. Int. Conf. Eng. Med.
Biol. Soc., Aug. 2014, pp. 90–93.
• P. Tadejko and W. Rakowski, “Mathematical morphology based ECG
feature extraction for the purpose of heartbeat classification,” in Proc. IEEE
6th Int. Conf. Comput. Inf. Syst. Ind. Manage. Appl. (CISIM), Jun. 2007,
pp. 322–327.
• A.L. Goldberger et al., “Physiobank, physiotoolkit, and physionet:
Components of a new research resource for complex physiologic
signals,”Circulation, vol. 101, no. 23, pp. e215–e220, Jun. 2000.

Efficient VLSI Architecture for Data Analysis of ECG

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