DESIGN OF EFFICIENT FILTER FOR ECG- BASED PROCESSOR 1S

Journal of Recent Research in Engineering and Technology, 3(10), OCT 2016, PP.31-41

ISSN (Online): 2349 –2252, ISSN (Print):2349 –2260 © Bonfay Publications, 2016

31

Research Article

DESIGN OF EFFICIENT FILTER FOR ECG- BASED PROCESSOR

1S.Manjulambigai , 2Dr. J.Jaya MTech., PhD 1M.E., Applied Electronics, Akshaya college of Engineering and Technology.

[email protected] 2Principal, Akshaya college of Engineering and Technology.

Received 20 Sep 2016; Accepted 24 OCT 2016

Abstract

ECG (Electrocardiogram) is considered to be a must have feature for a medical diagnostic imaging system. Electrocardiogram (ECG), a noninvasive technique is used as a primary diagnostic tool for cardiovascular diseases. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. It provides valuable information about the functional aspects of the heart and cardiovascular system. The objective of the work is to automatic detection of cardiac arrhythmias in ECG signal. Recently developed digital signal processing and pattern reorganization technique is used in this project for detection of cardiac arrhythmias. The detection of cardiac arrhythmias in the ECG signal consists of following stages: detection of QRS complex in ECG signal; feature extraction from detected QRS complexes; classification of beats using extracted feature set from QRS complexes. The system is designed using Verilog HDL and implemented using Xilinx ISE tool. FPGAs (Field Programmable Gate Arrays) are finding wide acceptance in medical systems for their ability for rapid prototyping of a concept that requires hardware/software co-design, for performing custom processing in parallel at high data rates and be programmed in the field after manufacturing. Medical companies can now move over to FPGAs saving cost and delivering highly-efficient upgradable systems.

Keyword

ECG signal, QRS complex, Xilinx ISE tool, Verilog HDL, Field Programmable Gate Arrays

1. INTRODUCTION

1.1 Electrocardiogram

Electrocardiogram (ECG) is a diagnosis

tool that reported the electrical activity of

heart recorded by skin electrode. The

morphology and heart rate reflects the

cardiac health of human heart beat [1]. It

is a noninvasive technique that means

this signal is measured on the surface

of human body, which is used in

identification of the heart diseases [2].

Any disorder of heart rate or rhythm, or

change in the morphological pattern, is

an indication of cardiac arrhythmia,

which could be detected by analysis of the

recorded ECG waveform. The amplitude

mailto:[email protected]

S.Manjulambigai Journal of Recent Research in Engineering and Technology

40

and duration of the P-QRS-T wave

contains useful information about the

nature of disease afflicting the heart. The

electrical wave is due to depolarization

and re polarization of Na+ and k- ions in

the blood [2].The ECG signal provides the

following information of a human heart

[3]:

Heart position and its relative

chamber size

Impulse origin and propagation

Heart rhythm and conduction

disturbances

Extent and location of myocardial

ischemia

Changes in electrolyte

concentrations

Drug effects on the heart.

ECG does not afford data on cardiac

contraction or pumping function.

1.2 The heart anatomy

The heart contains four

chambers that is right atrium, left

atrium, right ventricle, left ventricle and

several atrioventricular and sinoatrial

node as shown in the fig1.1 [1]. The

two upper chambers are called the left

and right atria, while the lower two

chambers are called the left and right

ventricles. The atria are attached to the

ventricles by fibrous, non-conductive

tissue that keeps the ventricles

electrically isolated from the atria. The

right atrium and the right ventricle

together form a pump to the circulate

blood to the lungs. Oxygen-poor blood is

received through large veins called the

superior and inferior vena cava and

flows into the right atrium.

The right atrium contracts and

forces blood into the right ventricle,

stretching the ventricle and maximizing

its pumping (contraction) efficiency. The

right ventricle then pumps the blood to

the lungs where the blood is oxygenated.

Similarly, the left atrium and the left

ventricle together form a pump to

circulate oxygen-enriched blood

received from the lungs (via the

pulmonary veins) to the rest of the body

[4].

Figure 1.1 The Heart conduction

systems

In heart Sino-atrial (S-A) node

spontaneously generates regular

electrical impulses, which then spread

through the conduction system of the

heart and initiate contraction of the

myocardium. Propagation of an electrical

impulse through excitable tissue is

achieved through a process called

depolarization. Depolarization of the

Journal of Recent Research in Engineering and Technology S.Manjulambigai

35

heart muscles collectively generates a

strong ionic current [1]. This current

flows through the resistive body tissue

generating a voltage drop. The magnitude

of the voltage drop is sufficiently large to

be detected by electrodes attached to the

skin. ECGs are thus recordings of voltage

drops across the skin caused by ionic

current flow generated from myocardial

depolarisations[5]. Atrial depolarization

results in the spreading of the electrical

impulse through the atrial myocardium

and appears as the P-wave. Similarly,

ventricular depolarization results in the

spreading of the electrical impulse

throughout the ventricular myocardium.

1.3 Motivation

The state of cardiac heart is

generally reflected in the shape of ECG

waveform and heart rate. ECG, if properly

analyzed, can provide information

regarding various diseases related to

heart. However, ECG being a non-

stationary signal, the irregularities may

not be periodic and may not show up all

the time, but would manifest at certain

irregular intervals during the day.

Clinical observation of ECG can hence

take long hours and can be very

tedious. Moreover, visual analysis cannot

be relied upon and the possibility of the

analyst missing the vital information is

high. Hence, computer based analysis

and classification of diseases can be very

helpful in diagnosis. Various

contributions have been made in

literature regarding beat detection and

classification of ECG signal. Most of them

use either time or frequency domain

representation of the ECG waveforms,

on the basis of which many specific

features are defined, allowing the

recognition between the beats belonging

to different classes. The most difficult

problem faced by today’s automatic ECG

analysis is the large variation in the

morphologies of ECG waveforms.

Moreover, we have to consider the time

constraints as well. Thus our basic

objective is to come up with a simple

method having less computational time

without compromising with the

efficiency. This objective has motivated

me to search and experiment with various

techniques. In this project, R-peak

detection of ECG signal is implemented

using the properties of autocorrelation

and Hilbert transform and classification

has been done using multilayer

perceptron (MLP) and radial basis

function (RBF), taking the features as

temporal features, heart beat interval

features and ECG morphological features.

2. PROCESSORS FOR PROCESSING ECG

SIGNALS

This section discusses the methods that

have been used for implementing ECG.

2.1 TRADITIONAL METHOD

The traditional methods involve using

leads to detect heart rate. The

standard 12-lead electrocardiogram is a

representation of the heart's electrical

activity recorded from electrodes on the

body surface. The electrical signals then

get passed on to a data logger system that

performs all the necessary signal


40

processing and shows an ECG waveform

on the screen. This method is widely used

in diagnostic centers. [4,5]

2.2 PULSE OXIMETRY

This method uses the principle of light

absorbance to determine oxygenated

blood from deoxygenated. In doing so, it

then calculates the heart rate and an ECG.

Pulse oximetry uses a sensor which is

placed on a sensitive part of the body,

usually fingertip or earlobes. The basic

idea is to pass light of two different

wavelengths through the body. Typically,

light emitting diodes (LEDs) are used in

this process. One emits red light while the

other emits infrared light.

The absorption of light depends on the

wavelengths as well as the blood inside

the human body.

Absorption of radiation as a function of

wavelength is calculated. Absorption of

both the lights differs significantly for

oxygenated and deoxygenated blood as it

depends on the amount of oxygen

present. Based on it, the device can

determine a heartbeat or a pulse. In

smartphone apps, the camera is used to

emit light. Placing one’s fingertip on the

camera is same as placing it on a sensor of

a pulse oximetry device. The app triggers

the phone to emit light of certain

wavelengths and then calculates the heart

rate.

3. ECG SIGNAL PROCESSOR

Figure 3.1 ESP Architecture

3.1 ESP Architecture

The architecture includes the

modules of the three stages along with a

main FSM that controls the flow of the

data between the different stages. The

processing of the data is done using fixed

point representation. The digitized ECG

data are applied in series (from

testbench) at the input to the

preprocessing stage with a resolution of 8

bit, while a variable number of bits were

utilized in the different stages to enhance

the accuracy and avoid truncations

errors.

3.2 Stages in ESP

The system consists of three main

stages, which are the ECG preprocessing,

feature extraction, and classification, as

shown in Fig. 3.2. . In the first stage, the

ECG preprocessing is responsible for

three tasks: 1) ECG filtering; 2) QRS

complex detection; and 3) T and P wave

delineation. After that, the QRS complex is


35

detected using the Pan and Tompkins

(PAT) algorithm. In the final stage, naive

Bayes algorithm is used to identify the

signals that are susceptible to ventricular

arrhythmia.

Samples

( Input ROM)FIR Filter

Filtered samples in

RAM

Classifier

(Naive Bayes)

P-QRS-T

(PAT Algorithm)

Classifier

(Naive Bayes)

Figure 3.2 Stages in ECG Signal

Processor

3.2.1 Preprocessing Stage

In the first stage, the ECG

preprocessing is responsible for three

tasks: 1) ECG filtering; 2) QRS complex

detection; and 3) T and P wave

delineation. The ECG filtering removes

the noise coupled with the ECG signal and

prepares it for further analysis. 1) ECG

Filtering: The block diagram of the

preprocessing stage. FIR Band pass

filtering of the raw ECG signal is the first

step in which the filter isolates the

predominant QRS energy centered at 10

Hz, and attenuates the low frequencies

characteristic of the P and T waves,

baseline drift, and higher frequencies

associated with electromyographic noise

and power line interference. The main

important point is not to lose the

information carried by the ECG signal

after being filtered out.[6]

Figure 3.3 FIR Filter

FIR Equiripple filter, windowing

FIR filters with Kaiser, Rectangular,

Hamming, Hanning and Blackman

functions are designed. The basic

specifications for design of filter are:

1. Cut-off frequency 0.5Hz

2. Sampling frequency 360Hz (MIT/BIH

database sampled at 360 Hz )

[8]The other parameters are pass-

band ripples and stop-band ripples. In the

design of FIR equiripple design, pass-

band ripple is 1 dB, stop-band ripple is 30

dB and the order of the filter was found to

be 320. The transition band of this filter is

approximately 0.5 Hz. The phase delay is

2.8 rad/ Hz. In case of window filters, cut-

off frequency at the 3 dB point is 0.5 Hz.

The window length in case of rectangular

and Kaiser Window is 451 which is

selected according to filter order 450

(window length is order plus one). The

phase delay is 3.92 rad/Hz. But in other

windows, order becomes very high and

reaches up to 1500 and it increase the

phase delay to 13.08rad/Hz.


40

3.3.2 P-QRS-T (PAT Algorithm)

Figure 3.4 P-QRS-T detection using

PAT Algorithm

The delineation of T and P waves is

based on a novel technique proposed in

figure 3.4. The method is based on

adaptive search windows along with

adaptive thresholds to accurately

distinguish T and P peaks from noise

peak. In each heartbeat, the QRS complex

is used as a reference for the detection of

T and P waves in which two regions are

demarcated with respect to R peaks.

These regions are then used to form the

forward and backward search windows of

the T and P waves. A forward search

window is assumed to contain the T wave,

and the boundaries are extended from the

QRS offset to two third of the previously

detected RR interval. [9]

Similarly, a backward search

window for the P wave is identified and

extended from the QRS onset backwardly

to one third of the previous RR interval.

The position of T and P peaks is

demarcated in their respective search

windows by finding the local maximum

or/and local minimum that are above the

associated thresholds. The thresholds,

given and are modified in each heartbeat

based on the most recent detected values

in the last 3 s. The technique of computing

the thresholds. By comparing the local

maximum or/and the local minimum

points with the thresholds, the waveform

morphology of each wave is identified

[positive monophasic, inverted, or

biphasic (+, −)/(−, +)]. [10]

If the value of T or P peak is

greater than the associated threshold,

then the T or P wave has a positive

monophasic waveform, and the local

maximum is stored to give a probable

position of the peak. Otherwise, the

waveform is identified as inverted, and

the local minimum of the ECG signal

within the same window is the correct

peak. In case of biphasic wave, both the

local maximum and the absolute value of

the local minimum should be greater than

the threshold.[11]

The method traces the onset and

offset values of the P-QRS-T waves by

finding the sample corresponding to the

zero slope of the entitled ECG signal. The

sample point that has a zero slope and

former to the peak is identified as the


35

onset point. Similarly, the offset point is

determined at other side of the peak.

Sometimes, however, a derivative sign

change occurs, which reflects a false

indicator. To solve this, the method adds

another criterion for a correct delineation

of the wave boundaries based on the fact

that the fiducial points tend to merge

smoothly with the isoelectric line. The

isoelectric line is approximated as the

average value of the beat signal after

removing the QRS complex. This idea is

utilized and combined with the zero slope

for an accurate and reliable delineation of

the fiducial points. The general FSM

which illustrates the delineation process

of T and P waves.[12]

4. RESULTS AND DISCUSSION

The ECG Signal Processor is

designed using Verilog HDL and

implemented using Xilinx ISE and

simulated using Modelsim 6.5e. The Xilinx

tool procedure is as follows.[13]

4.1 Result Discussion

The memory for storing the ECG

samples is done using the ROM design.

The FIR filter is designed which takes the

input from the ROM. The FIR is a 4 tap

filter that has one input and four

coefficients is shown in the figure 4.1.

Figure 4.1 FIR Filter

The QRS complex is detected using

the Pan and Tompkins (PAT) algorithm.

Finally, T and P waves are delineated, and

the corresponding fiducial points (P

onset, P peak, P offset, T onset, T peak,

and T offset) are extracted.

Figure 4.2 PAT Algorithm


40

Figure 4.3 Naive Bayes classifier

The Naive Bayes classifier is used

to classify the ECG signal naive Bayes

algorithm is used to identify the signals

that are susceptible to ventricular

arrhythmia. There are many reasons for

choosing the naive Bayes. First, the ECG

features have shown strong potential in

the prediction of ventricular arrhythmia

with a p-value < 0.001. Second, it was

intended to investigate the performance

of the system without introducing the

strong biasing effect of a classifier. The

normal and abnormal signal indicates

that the sample is normal or

abnormal.[14,15]

Figure 4.4 Top Module

4.2 Simulation output

Initially the input ECG samples are

stored in the ROM. Then they are

processed by the FIR filter and the filtered

output is stored in the RAM. Then by

fetching the data from the RAM the data is

given to PAT algorithm for P-QRS-T

detection. The PAT algorithm gives the

proper ECG signal to the Naive Bayes

classifier. The Classification is done by

comparing the received ECG signal with

the normal and abnormal data base. The

simulation output for the ESP is shown in

the figure 4.5. The comparison part of the

Naive Bayes classifier is shown in the

figure 4.6. The classifier takes the

average of the input ECG values from the

PAT algorithm and compares it with the

average values of the normal and

abnormal values. If the data Naive Bayes

classifier is normal the output will assert

the normal flag or if it is the abnormal

values abnormal flag is asserted.


41

Figure 4.5 Simulation output of the ESP

Figure 4.6 Simulation output of the Naive Bayes classifier


40

The summary of the design is listed below.

Figure 4.7 Summary of the design

Detailed Summary

Macro Statistics

# RAMs

: 1

6x16-bit dual-port distributed

RAM : 1

# ROMs

: 3

16x16-bit ROM

: 2

16x8-bit ROM

: 1

# Multipliers

: 4

8x8-bit registered multiplier

: 4

# Adders/Subtractors

: 6

16-bit adder

: 6

# Counters

: 7

3-bit up counter

: 1

4-bit up counter

: 6

# Registers

: 251

Flip-Flop

: 251

# Comparators

: 8

16-bit comparator equal

: 2

16-bit comparator greater

: 2

4-bit comparator great equal

: 2

4-bit comparator less

: 2

# Multiplexers

: 3


41

16-bit 8-to-1 multiplexer

: 3

The design summary includes logic cell

utilization and area utilization This is

listed below.

Design Statistics

# IOs : 20

Cell Usage:

# BELS : 588

# GND : 1

# INV : 11

# LUT1 : 13

# LUT2 : 129

# LUT3 : 90

# LUT3_D : 1

# LUT3_L : 1

# LUT4 : 101

# MUXCY : 124

# MUXF5 : 20

# VCC : 1

# XORCY : 96

# FlipFlops/Latches : 265

# FDC : 9

# FDCE : 141

# FDE : 51

# FDR : 64

# RAMS : 16

# RAM16X1D : 16

# Clock Buffers : 1

# BUFGP : 1

# IO Buffers : 19

# IBUF : 1

# OBUF : 18

# MULTs : 4

# MULT18X18SIO : 4

AREA

Number of Slices: 219

out of 2448 8%

Number of Slice Flip Flops: 265

out of 4896 5%

Number of 4 input LUTs: 378

out of 4896 7%

Number used as logic: 346

Number used as RAMs: 32

Number of IOs: 20

Number of bonded IOBs: 20 out

of 158 12%

Number of MULT18X18SIOs: 4 out

of 12 33%

Number of GCLKs: 1 out

of 24 4%

Timing Summary:

Minimum period: 6.926ns (Maximum

Frequency: 144.387MHz)

Minimum input arrival time before

clock: 4.015ns

Maximum output required time after

clock: 4.063ns

Delay

Delay: 6.926ns (Levels of Logic =

4)

Frequency

Frequency: 144.387MHz

Chapter 5

Conclusion and Future Work

5.1 Conclusion

In this project, a fully integrated

digital ESP for the prediction of

ventricular arrhythmia that combines a

unique set of ECG features with naive

Bayes was proposed. Real-time and


40

adaptive techniques for the detection and

delineation of the P-QRS-T waves were

investigated and employed to extract the

fiducial points. The combination of these

features has never been used in any

previous detection or prediction system.

The processor is designed using Verilog

HDL and implemented using Xilinx ISE

14.2.

5.2 Future Work

The preprocessing stage in the

processor can be modified. In the

processor the stage has the FIR filter. In

the future the FIR filter can be replaced

using IIR filter. IIR filters are harder to

design than the FIR filters, but the

benefits are extraordinary: IIR filters are

an order of magnitude more efficient than

an equivalent FIR filter. Even though FIR

is easier to design, IIR will do the same

work with fewer components, and fewer

components translate directly to less

money.

References

[1] R. Acharya, J. S. Suri, J. A.E. Spaan and

S .M. Krishnan, Advances in Cardiac Signal

Processing, springer, pp. 1-50.

[2] W. J. Germann and C. L. Standield,

“Principles of Human Physiology,”

Benjamin Cummings, San Francisco, 2002.

[3] A. J. Moss and S. Stern., “Noninvasive

Electro cardiology,” Clinical Aspects of

Holter, London, Philadelphia, W.B.

Saunders, 1996.

[4] M. Gabriel Khan, “Rapid ECG

interpretation” Third edition, 2003.

[5] Francis Morris, June Edhouse,

William J Brady, John Camm, “ABC of

Clinical Electrocardiography,” BMJ

Books, 2003.

[6]

http://en.wikipedia.org/wiki/Electrocard

iography.

[7] Y.C. Yeha, and W. J. Wang, “QRS

complexes detection for ECG signal The

Difference Operation Method (DOM),”

Computer methods and programs in

biomedicine, vol. 9, pp. 245–254, 2008.

[8] R.M. Rangayyan, Biomedical Signal

Analysis: A Case-study Approach,

Wiley–Interscience, New York, pp. 18–

28, 2001.

[9] G.M. Friesen, T.C. Jannett, M.A.

Jadallah, S.L. Yates, S.R. Quint, and H.T.

Nagle, “A comparison of the noise

sensitivity of nine QRS detection

algorithm,” IEEE Trans. Biomed. Eng. Vol.

37, pp. 85–98, 1990.

[10] MIT-BIH Database distribution,

Massachusetts Institute of Technology, 77

Massachusetts Avenue, Cambridge, MA

02139,1998.http://www.physionet.org/p

hysiobank/database/mitdb/

[11] B.U. Kohler, C. Henning, and R.

Orglmeister, “The principles of software

QRS detection,” IEEE Eng. Med. Biol.

Vol. 21, pp. 42–57, 2002.

[12] T.Ince, S. Kiranyaz, and M.

Gabbouj, “A generaric and robust

system for automated patient-specific

classification of ECG signals,” IEEE Trans.

Biomed. Eng. vol. 56, pp. 1415-1426,

2009.

[13] American National Standard for

Ambulatory Electrocardiographs,

publication ANSI/AAMI EC38-1994,

Association for the Advancement of

Medical Instrumentation, 1994.


41

[14] Omern T. Inan. L. Giovangrandi, and

T. A. Kovacs, “Robust Neural network

based classification of Premature

Ventricular Contraction using wavelet

transform and time interval features,”

IEEE Trans. Biomed. Eng. vol. 53, pp.

2507-2515, 2006.

[15] P.de Chazal, M.O. Duyer, and R.B.

Reilly, “Automatic classification of

heartbeat using ECG morphology and

heart beat interval features,” IEEE Trans.

Biomed. Eng. vol. 51, pp. 1196-1206,

2004.

Documents

DESIGN OF EFFICIENT FILTER FOR ECG- BASED PROCESSOR 1S