Lab S-9: Interference Removal from Electro-Cardiogram (ECG) Signals

Similar documents
1 PeZ: Introduction. 1.1 Controls for PeZ using pezdemo. Lab 15b: FIR Filter Design and PeZ: The z, n, and O! Domains

Lab S-5: DLTI GUI and Nulling Filters. Please read through the information below prior to attending your lab.

Lab S-4: Convolution & FIR Filters. Please read through the information below prior to attending your lab.

GEORGIA INSTITUTE OF TECHNOLOGY. SCHOOL of ELECTRICAL and COMPUTER ENGINEERING. ECE 2026 Summer 2018 Lab #8: Filter Design of FIR Filters

Lab 8: Frequency Response and Filtering

Digital Filtering: Realization

Lab 6: Sampling, Convolution, and FIR Filtering

Lab P-10: Edge Detection in Images: UPC Decoding. Please read through the information below prior to attending your lab.

Lab S-2: Direction Finding: Time-Difference or Phase Difference

Lab S-8: Spectrograms: Harmonic Lines & Chirp Aliasing

DSP First Lab 08: Frequency Response: Bandpass and Nulling Filters

Lab 15c: Cochlear Implant Simulation with a Filter Bank

1 Introduction and Overview

ijdsp Workshop: Exercise 2012 DSP Exercise Objectives

Lab P-4: AM and FM Sinusoidal Signals. We have spent a lot of time learning about the properties of sinusoidal waveforms of the form: ) X

Lab S-7: Spectrograms of AM and FM Signals. 2. Study the frequency resolution of the spectrogram for two closely spaced sinusoids.

Lecture 17 z-transforms 2

EE 422G - Signals and Systems Laboratory

Lab S-3: Beamforming with Phasors. N r k. is the time shift applied to r k

DIGITAL FILTERS. !! Finite Impulse Response (FIR) !! Infinite Impulse Response (IIR) !! Background. !! Matlab functions AGC DSP AGC DSP

ELEC3104: Digital Signal Processing Session 1, 2013 LABORATORY 3: IMPULSE RESPONSE, FREQUENCY RESPONSE AND POLES/ZEROS OF SYSTEMS


DSP First Lab 03: AM and FM Sinusoidal Signals. We have spent a lot of time learning about the properties of sinusoidal waveforms of the form: k=1

ECE 2026 Summer 2016 Lab #08: Detecting DTMF Signals

6.555 Lab1: The Electrocardiogram

THE HONG KONG POLYTECHNIC UNIVERSITY Department of Electronic and Information Engineering. EIE2106 Signal and System Analysis Lab 2 Fourier series

DSP First. Laboratory Exercise #7. Everyday Sinusoidal Signals

SMS045 - DSP Systems in Practice. Lab 1 - Filter Design and Evaluation in MATLAB Due date: Thursday Nov 13, 2003

STANFORD UNIVERSITY. DEPARTMENT of ELECTRICAL ENGINEERING. EE 102B Spring 2013 Lab #05: Generating DTMF Signals

Digital Signal Processing ETI

Digital Signal Processing ETI

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL and COMPUTER ENGINEERING. ECE 2025 Fall 1999 Lab #7: Frequency Response & Bandpass Filters

Spring 2014 EE 445S Real-Time Digital Signal Processing Laboratory Prof. Evans. Homework #2. Filter Analysis, Simulation, and Design

Lecture 3 Complex Exponential Signals

George Mason University ECE 201: Introduction to Signal Analysis

The University of Texas at Austin Dept. of Electrical and Computer Engineering Final Exam

DSP Laboratory (EELE 4110) Lab#10 Finite Impulse Response (FIR) Filters

ECE 203 LAB 2 PRACTICAL FILTER DESIGN & IMPLEMENTATION

Electrical & Computer Engineering Technology

ECE 4213/5213 Homework 10

The University of Texas at Austin Dept. of Electrical and Computer Engineering Midterm #1

GEORGIA INSTITUTE OF TECHNOLOGY. SCHOOL of ELECTRICAL and COMPUTER ENGINEERING

UNIT II IIR FILTER DESIGN

Signal Processing First Lab 02: Introduction to Complex Exponentials Multipath. x(t) = A cos(ωt + φ) = Re{Ae jφ e jωt }

Infinite Impulse Response Filters

Project 2 - Speech Detection with FIR Filters

DSP First. Laboratory Exercise #2. Introduction to Complex Exponentials

Problem Point Value Your score Topic 1 28 Discrete-Time Filter Analysis 2 24 Improving Signal Quality 3 24 Filter Bank Design 4 24 Potpourri Total 100

Electrical and Telecommunication Engineering Technology NEW YORK CITY COLLEGE OF TECHNOLOGY THE CITY UNIVERSITY OF NEW YORK

EE 470 Signals and Systems

Basic Signals and Systems

Laboration Exercises in Digital Signal Processing

Here are some of Matlab s complex number operators: conj Complex conjugate abs Magnitude. Angle (or phase) in radians

ECE438 - Laboratory 7a: Digital Filter Design (Week 1) By Prof. Charles Bouman and Prof. Mireille Boutin Fall 2015

Digital Video and Audio Processing. Winter term 2002/ 2003 Computer-based exercises

Lab S-1: Complex Exponentials Source Localization

y(n)= Aa n u(n)+bu(n) b m sin(2πmt)= b 1 sin(2πt)+b 2 sin(4πt)+b 3 sin(6πt)+ m=1 x(t)= x = 2 ( b b b b

1 Introduction and Overview

Lab P-3: Introduction to Complex Exponentials Direction Finding. zvect( [ 1+j, j, 3-4*j, exp(j*pi), exp(2j*pi/3) ] )

Experiments #6. Convolution and Linear Time Invariant Systems

Instruction Manual for Concept Simulators. Signals and Systems. M. J. Roberts

NH 67, Karur Trichy Highways, Puliyur C.F, Karur District DEPARTMENT OF INFORMATION TECHNOLOGY DIGITAL SIGNAL PROCESSING UNIT 3

Digital Filters IIR (& Their Corresponding Analog Filters) Week Date Lecture Title

Team proposals are due tomorrow at 6PM Homework 4 is due next thur. Proposal presentations are next mon in 1311EECS.

ELEC-C5230 Digitaalisen signaalinkäsittelyn perusteet

(i) Understanding of the characteristics of linear-phase finite impulse response (FIR) filters

ASN Filter Designer Professional/Lite Getting Started Guide

Designing Filters Using the NI LabVIEW Digital Filter Design Toolkit

(i) Understanding of the characteristics of linear-phase finite impulse response (FIR) filters

Signal Processing First Lab 02: Introduction to Complex Exponentials Direction Finding. x(t) = A cos(ωt + φ) = Re{Ae jφ e jωt }

Lab 1: Simulating Control Systems with Simulink and MATLAB

1. page xviii, line 23:... conventional. Part of the reason for this...

DESIGN OF FIR AND IIR FILTERS

Signal Processing First Lab 20: Extracting Frequencies of Musical Tones

Digital Filters. Linearity and Time Invariance. Implications of Linear Time Invariance (LTI) Music 270a: Introduction to Digital Filters

George Mason University ECE 201: Introduction to Signal Analysis Spring 2017

EEO 401 Digital Signal Processing Prof. Mark Fowler

SECTION 7: FREQUENCY DOMAIN ANALYSIS. MAE 3401 Modeling and Simulation

Problem Point Value Your score Topic 1 28 Filter Analysis 2 24 Filter Implementation 3 24 Filter Design 4 24 Potpourri Total 100

Biosignal filtering and artifact rejection. Biosignal processing I, S Autumn 2017

Lab E5: Filters and Complex Impedance

16.30 Learning Objectives and Practice Problems - - Lectures 16 through 20

Demonstrating in the Classroom Ideas of Frequency Response

ECE 5650/4650 MATLAB Project 1

DSP First Lab 06: Digital Images: A/D and D/A

EEM478-WEEK8 Finite Impulse Response (FIR) Filters

Spring 2018 EE 445S Real-Time Digital Signal Processing Laboratory Prof. Evans. Homework #2. Filter Analysis, Simulation, and Design

1. In the command window, type "help conv" and press [enter]. Read the information displayed.

Introduction to Simulink

IIR Filter Design Chapter Intended Learning Outcomes: (i) Ability to design analog Butterworth filters

Concordia University. Discrete-Time Signal Processing. Lab Manual (ELEC442) Dr. Wei-Ping Zhu

Experiment 6: Multirate Signal Processing

FINITE IMPULSE RESPONSE (FIR) FILTERS

b) discrete-time iv) aperiodic (finite energy)

Word length Optimization for Fir Filter Coefficient in Electrocardiogram Filtering

Lab 4 Digital Scope and Spectrum Analyzer

EE 5410 Signal Processing

Digital Filters FIR and IIR Systems

A Lower Transition Width FIR Filter & its Noise Removal Performance on an ECG Signal

INTRODUCTION DIGITAL SIGNAL PROCESSING

Transcription:

DSP First, 2e Signal Processing First Lab S-9: Interference Removal from Electro-Cardiogram (ECG) Signals Pre-Lab: Read the Pre-Lab and do all the exercises in the Pre-Lab section prior to attending lab. Verification: The Exercise section of each lab should be completed during your assigned Lab time and the steps marked Instructor Verification signed off during the lab time. When you have completed a step that requires verification, demonstrate the result to your instructor and answer any questions about it. Turn in the completed verification sheet before you leave the lab. Lab Homework Questions: The Lab-Homework Sheet has a few lab related questions that can be answered at your own pace. The completed Lab-HW sheet is due at the beginning of the next lab. 1 Pre-Lab 1.1 Objective The goal of this lab is to explore the connection between the time domain (n), the frequency domain ( O!), and the z-transform domain, using the GUI PeZ in MATLAB. 1. Placing Zeros: When placed on the unit circle, zeros of A.z/ will force the frequency response to be zero which can then be used to null out sinusoids at one frequency. 2. Placing Poles: When placed near the unit circle (but inside), roots of B.z/ will create peaks in the frequency response which can be used to form BPFs. 3. Designing IIR Notch Filters: requires conjugate zeros on the unit circle (UC) with a poles at the same angle, just inside the UC. The frequency response of the notch is much sharper than a nulling filter which is an FIR filter formed only from the conjugate zeros on the UC. 1.2 Overview IIR filters can be used for many tasks; bandpass filtering is the most common, but nulling or notch filters are also very useful for removing interference, especially narrowband interference caused by power lines. PeZ is an ideal tool for studying the poles and zeros of IIR filters. 1.3 PeZ: Introduction In order to build an intuitive understanding of the relationship between the location of poles and zeros in the z-domain, the impulse response hœn in the n-domain, and the frequency response H.e j O! / (the O!-domain), A graphical user interface (GUI) called PeZ was written in MATLAB for doing interactive explorations of the three domains. 1 PeZ is based on the system function, represented as a ratio of polynomials in z 1, which can be expressed in either factored or expanded form as: H.z/ D B.z/ A.z/ D G MY.1 z k z 1 / kd1 D NY.1 p`z 1 / 1 `D1 MX b k z k kd0 NX a`z ` `D1 (1) 1 The original PeZ was written by Craig Ulmer; a later version by Koon Kong is the one that we will use in this lab. 1 McClellan, Schafer and Yoder, Signal Processing First.

where M is the number of zeros and N the number of poles. If the poles and zeros occur in pairs that satisfy the complex-conjugate property, then the following property holds: A polynomial with real coefficients has roots that are real, or occur in complex-conjugate pairs. The PeZ GUI is contained in the SP-First toolbox. To run PeZ, type pezdemo at the command prompt and you will see the GUI shown in Fig. 1. 2 Figure 1: GUI interface for pezdemo running in MATLAB version 6. A length-3 FIR filter is shown. Zero locations are given in rectangular coordinates. 1.4 Controls for PeZ using pezdemo The PeZ GUI is controlled by the Pole-Zero Plot panel in the upper left where the user can add (or delete) poles and zeros, as well as move them around with the pointing device. For example, Fig. 1 shows a case where two (complex-conjugate) zeros have been added, and PeZ has automatically included two poles at z D 0 so that the system is causal. The button named ZZ was used to add these zeros; the button PP would be used to add poles. By default, the Add with Conjugate property is turned on, so poles and zeros are typically added in pairs to satisfy the complex-conjugate property, giving real coefficients. To learn about the other controls in pezdemo, access the menu item called Help for extensive information about all the PeZ controls and menus. Here are a few things to try. In the Pole-Zero Plot panel you can selectively place poles and zeros in the z-plane, and then observe (in the other plotting panels) how their placement affects the impulse and frequency responses. The red ray in the z-domain window is tied 2 The command pez will invoke the older version of PeZ which is distinguished by a black background in all the plot regions. 2 McClellan, Schafer and Yoder, Signal Processing First.

to the red vertical lines on the frequency responses, and they move together. This helps identify frequency domain features that are caused by pole locations or zero locations, because the angle around the unit circle corresponds to frequency O!. In PeZ an individual pole/zero pair can be moved around and the corresponding H.e j O! / and hœn plots will be updated as you drag the pole (or zero). Since exact placement of poles and zeros with the mouse is difficult, an Edit button is provided for numerical entry of the real and imaginary parts. Before you can edit a pole or zero, however, you must first select it in the list of Pole Locations or Zero Locations. Next, you will have to make the necessary change in the edit field (box above the Edit button) and then press the Edit button for the change to take effect. Removal of individual poles or zeros can also be performed by using the -P or -Z buttons, or with the Delete button. Note that all poles and/or zeros can be easily cleared by clicking on the -A button. 1.5 Relationships between z, n, and O! domains Work through the following exercises and keep track of your observations by filling in the worksheet at the end of this assignment. In general, you want to make note of the following quantities: Is the length of hœn finite or infinite? For the FIR case, what is the total length of the filter? How does hœn change with respect to its rate of decay for IIR filters? For example, when the impulse response is of the form hœn D a n uœn, the impulse response will fall off more rapidly when a is smaller; and if jaj > 1 the impulse response will grow without bound. A stable system is one that obeys the bounded-input gives a bounded-output property. When the impulse response grows without bound, the output is not bounded even though the input is ıœn with is bounded by one. If hœn exhibits an oscillating component, what is the period of oscillation? Also, estimate the decay rate of the envelope that overlays the oscillation. How does H.e j O! / change with respect to null location, peak location and/or peak width? Note: review the Three-Domains - FIR under the Demos link for Chapter 9 and Three-Domains - IIR under the Demos link for Chapter 10 for movies and examples of these relationships. Try various operations with PeZ to gain some familiarity with the interface. Below are some suggested filters that you can create. 1.6 Zeros 1.6.1 Create an FIR Filter with PeZ Implement the following FIR system: H.z/ D 1 z 1 C z 2 by factoring the polynomial and placing the two zeros correctly. Observe the following two facts: The impulse response hœn values are equal to the polynomial coefficients of H.z/. The frequency response has nulls because the zeros of H.z/ lie exactly on the unit circle. Compare the frequencies of the nulls to the angles of the zeros Move the zero-pair around the unit circle and observe that the location of the null also moves. 3 McClellan, Schafer and Yoder, Signal Processing First.

1.6.2 Create an FIR Filter with Many Zeros The 6-point running-sum FIR digital filter has the following system function: H.z/ D 5X nd0 z n D 1 z 6 1 z 1 (a) Use the roots function in MATLAB to determine the zeros of H.z/ for this 5-th order FIR filter. Note: The formula for H.z/ involves a numerator-denominator cancellation that removes the zero at z D 1. This is called a pole-zero cancellation. (b) In PeZ place the five zeros of the FIR filter at the correct locations; use the edit feature to enter the exact value of the zeros. Observe that the impulse response will be all ones when you have the correct zero locations. Describe the frequency response of the filter: low pass versus high pass. (c) List all the frequencies, O! 2. ;, that are nulled by the length-6 FIR filter. (d) Make a different FIR filter that has the system function H c.z/ D 1 z 6 which is the numerator in part (a). Use PeZ to place the six zeros of this filter, or add one zero to the previous configuration. Write a formula for the impulse response, h c Œn using weighted, shifted impulses. (e) The frequency response of the FIR filter in the previous part is called a comb filter. Explain why is it called a comb, and determine where its nulls are located for O! 2. ;. (f) One of the zeros will be at z D 1. Take that zero and move it from z D 1 to z D C1. Write down the system function H.z/ for the new filter. Hint: The GUI contains the answer if you know where to look. (g) Describe the frequency response of the filter created in part (6), i.e., LPF, HPF, or BPF. 1.7 Real Poles 1 2 (a) Use PeZ to place a single pole at z D and a single zero at z D 1. You may have to use the Edit button to get the location exactly right. Describe the important features of the impulse response hœn, and also the important features of the frequency response H.e j O! /. Define the passband as the region where the magnitude response is greater than 90% of the maximum; and the stopband as the region where the magnitude response is less than 10% of the maximum. Use freqz if you need more precision in measuring the passband and stopband. Finally, use these plots in PeZ as the reference for answering the next three parts. (b) Move the pole slowly from a location close to the origin out to z D 1 2, and then to out to z D 0:999. Stay on the real axis by using Options->Move on Real Line from the GUI menu. Observe the changes in the impulse response hœn and the frequency response H.e j O! /. Record your observations on the Verification Sheet. When you move poles and zeros, the impulse response and frequency response plots are updated continually in PeZ. Select the pole you want to move and start to drag it slowly. At the same time, watch for the update of the plots in the impulse response and frequency response panels. (c) Move the pole from z D 0:999 to a radius outside the unit circle. Describe the changes in hœn. Explain how the appearance of hœn validates the statement that the system is not stable. In this case, the frequency response H.e j O! / is not legitimate because the system is no longer stable. 4 McClellan, Schafer and Yoder, Signal Processing First.

(d) In general, where should poles be placed to guarantee system stability? By stability we mean that the system s output does not blow up as n! 1. 1.7.1 Create a First-Order IIR Filter with PeZ Implement the following first-order IIR system: H.z/ D 1 z 1 1 C 0:9z 1 by placing its pole and zero at the correct locations in the z-plane. First, try placing the pole and zero with the mouse, and then use the Edit feature under Pole Location or Zero Location to specify exact locations. Since PeZ wants to add complex-conjugate pairs, you might have to delete one of the poles/zeros that were added; or you can turn off the Add with Conjugate feature. Look at the frequency response and determine what kind of filter you have, i.e., HPF, LPF, or BPF. Now, use the mouse to grab the pole and move it from z D 0:9 to z D 0 and then to z D C0:9. To move along the real axis, you can use Options -> Move on Real Line from the GUI menu. Observe how the frequency response changes. Describe the type of filter for z D 0:9, i.e., HPF, LPF, or BPF. 1.8 Create a Second-Order IIR BPF with PeZ Use the PeZ interface to implement the following second-order system: H.z/ D 1 z 2 1 C 0:8z 1 C 0:64z 2 Factor the numerator and denominator to get the two poles and the two zeros. Then place the poles and zeros at the correct locations in the z-plane with PeZ. If it is hard to place the poles and zeros precisely with the mouse, then use the Edit feature to enter the exact locations. Since PeZ wants to add complex-conjugate pairs, you should only have to add one of the poles; for the zeros, the Add with Conjugate feature should be turned off because you will be adding two real-valued zeros. ) Look at the frequency response and verify that you have created a BPF. 1.9 Using Poles to Create Filters that are Not Bandpass Filters It is tempting to think that with two poles the frequency response always has a peak, but there are two interesting cases where that doesn t happen: (1) all-pass filters where jh.e j O! /j D constant, and (2) IIR notch filters that null out one frequency, but have a relatively flat nonzero magnitude responses elsewhere. Implement the following second-order system: H.z/ D 64 C 80z 1 C 100z 2 1 C 0:8z 1 C 0:64z 2 by factoring to get the two poles and the two zeros, and then placing the poles and zeros in PeZ at the correct locations in the z-plane. Adjust the gain.g/ to get the correct numerator coefficients. ) Look at the frequency response and determine what kind of filter you have. 3 Now, use the mouse to grab the zero-pair and move the zeros to be exactly on the unit-circle at the same 3 The relationship between the poles and zeros of an all-pass filter is zero = 1/(pole)*; this situation where two poles and two zeros are linked together via conjugate-inverse symmetry can be done with the PZ option in PeZ. 5 McClellan, Schafer and Yoder, Signal Processing First.

angle as the poles. Observe how the frequency response changes. In addition, determine the H.z/ for this filter, which should have the following form: H.z/ D b 0 C b 1 z 1 C b 2 z 2 1 C 0:8z 1 C 0:64z 2 ) Describe the type of filter that you have now created. 2 Lab Exercise The lab verification requires that you write down your observations on the verification sheet when using the PeZ GUI. These written observations will be graded. 2.1 IIR Allpass Filter An IIR allpass filter will pass all frequency components with magnitude response that is prefectly flat across the entire frequency band, but its phase resonse in nonlinear versus frequency. It has one complex-conjugate zero pair and one complex-conjugate pole pair. Zeros at.1=r/e j Poles at re j where r is a number slightly less than one, so that the system is stable. Thus the system function is: H.z/ D B.z/ A.z/ D G.1 r 1 e j z 1 /.1 r 1 e j z 1 /.1 re j z 1 /.1 re j z 1 / You can use PeZ to exhibit a typical frequency response for an alllpass filter. (a) To create an example in PeZ, use r D 0:9 and D =3 with the PZ option which automatically places the zeros at the conjugate-inverse locations. Alternatively, you can calculate the zeros, and then use the Zero Location edit window to enter the exact values. (b) Adjust the gain.g/ so that the frequency response magnitude is always equal to 10. Show the final PeZ result to your Lab instructor or TA. (c) Write out the expression for the numerator and denominator of H.z/ created in part (1). You should create H.z/ by multiplying out first-order terms with the poles and zeros in (2). Hint: use MATLAB s poly function to do the calculation. Also, the GUI provides H.z/, so you can check your work. 2.2 IIR Notch Filters A common heart test is an electrocardiogram (ECG or EKG) which records electrical activity that changes during the cardiac cycle. Metal electrodes placed at several locations on the body pick up these cardiac electrical signals. Unfortunately, the electrodes also pick up signals from other electrical sources, most notably harmonics of the 60-Hz power signal (or 50-Hz in some other countries). The objective of this short lab is to show that you can remove a sinusoidal interference from a corrupted ECG signal, and produce a cleaned-up signal. This will tap into your DSP skills as an accomplished filter designer. An IIR notch filter will null out one frequency, while having a frequency response that is relatively flat across the rest of the frequency band. It has one complex-conjugate zeros and poles: (2) Zeros at e j Poles at re j 6 McClellan, Schafer and Yoder, Signal Processing First.

where r is a number slightly less than one. Thus the system function is: H.z/ D B.z/ A.z/ D G.1 ej z 1 /.1 e j z 1 /.1 re j z 1 /.1 re j z 1 / You can use PeZ to exhibit a typical frequency response for a notch filter. To see an example, select r D 0:95 and D =4. It is also possible to multiply out the numerator and denominator of (3) to get the filter coefficients. In MATLAB the function poly will create a (z-transform) polynomial from its roots. 2.3 IIR Filter Implementation In MATLAB the function that does IIR filtering is called filter. It requires the numerator (num) and denominator (den) coefficients, yy = filter( num, den, xx ): For example, hn = filter([1,2],[1,-0.9],[1,zeros(1,50)]); will generate the impulse response of a first-order IIR filter. Use a stem plot to view hn versus the time index n. 2.4 Notch Filter Removes Sinusoidal Interference The ECG signal (plus interference) will be generated by the MATLAB function ECGmake.p. The function ECGmake.p reads data from a file ECGdataS11.mat, changes the sampling rate, and then adds a sinusoidal interference. These recorded ECG signals are shown in Fig. 2. The output of this function will depend on your login ID here is the calling template. function [ecgsig,fs,fint] = ECGmake( IDstring, dur ) % % ecgsig = vector of signal samples at fs samples/sec % fint = frequency of the interfering sinusoid (near 50 or 60 Hz) % IDstring = your login ID as a string, e.g., gburdell7 or gtg555q % dur = duration of the signal (optional); default = 15 secs. (a) Run ECGmake and note the values for the sampling rate.f s / and interference frequency.f int /. (b) Design the IIR notch filter by specifying its poles and zeros. Explain how the angle./ of the poles and zeros is determined by the interference frequency.f int / and the sampling rate.f s /. Draw the pole-zero diagram on the verification sheet. (c) Compute the frequency response of the notch filter, and plot the frequency response magnitude versus analog frequency f in Hz. Make a sketch of what you see on the screen (no print out is needed). Verify that the notch filter will null out a sinusoid at the given interference frequency. (d) Convert the poles and zeros of the IIR notch filter to filter coefficients, so that you can write a difference equation for the filter. (e) Apply the notch filter to the ECG signal and show that you can remove all the interference. Make a two-panel subplot comparing the time-domain signals before and after filtering. Show three or four periods of the ECG at the same time in both signals. (3) 7 McClellan, Schafer and Yoder, Signal Processing First.

(f) Show spectrograms of the signal before and after the notch filter. Point out where you see the interference in the input signal s spectrogram, and also where it has been removed in the output spectrogram. (a) (b) Figure 2: (a) The first ECG is very regular and follows the classic pattern. The P-wave, QRS complex, and T-wave are labeled. The P-wave results from depolarization of the sinoatrial (SA) node in the heart, leading to depolarization and contraction of the atrial cardiac muscle tissues. The QRS complex arises from depolarization of the atrioventricular (AV) node and resulting depolarization and contraction of the ventricular muscle tissue. The Q and S components of the complex are not always exhibited in a normal ECG. The T-wave results from repolarization of the ventricular tissue. (b) The second ECG is nonstandard. In place of the P and Q waves there is an irregular waveform. It appears that, in this case, the heart is in a constant state of atrial fibrillation. This normally is not a problem, as the atria contribute very little to the overall pumping ability of the heart, however it can present problems during strenuous exercise. (Recorded and described by Georgia Tech students Josh Hammel and Chris Clarke, April 2006.) 8 McClellan, Schafer and Yoder, Signal Processing First.

Lab: ECG Interference Removal INSTRUCTOR VERIFICATION PAGE Name: Date of Lab: Part 2.1(a, b, c): Exhibit the allpass frequency response in PeZ and write out the numerator and denominator of the system function so that jh.e j O! /j D 10: H.z/ D Verified: Date/Time: Part 2.4(a): Record the values of the sampling rate and interference frequency. f s D f int D Imaginary part 1.5 1 0.5 0-0.5 jh.j 2f /j -1-1.5-1 0 1 Real part Parts 2.4(b, c): Plot the poles and zeros of your notch filter on the z-plane grid above. In addition, sketch the frequency response in the space to the right of the pole-zero plot. Use a frequency range of 0 to f s =2. f (Hz) Verified: Date/Time: Part 2.4(d): List the filter coefficients of the IIR filter by filling in the IIR difference equation below. yœn D yœn 1 C yœn 2 C xœn C xœn 1 C xœn 2 Part 2.4(e): Apply the notch filter to the ECG signal and show that you can remove all the interference. Show the two panel subplot that compares the time-domain signals before and after filtering. Zoom in to verify that all interference has been removed completely, except for a short duration at the beginning. Verified: Date/Time: Part 2.4(f): Show spectrograms of the signal before and after nulling and explain how the filter affects the frequency-domain characteristics of the signal. Verified: Date/Time: 9 McClellan, Schafer and Yoder, Signal Processing First.