Python代写｜Assignment 2 Digital Signal processing FIR filters

本次英国代写是一个Python数字信号处理的assignment

The task of this assignment is to filter an ECG with FIR filters and to detect the
R peaks. In contrast to the FFT assignment we write filter code which can be
used for realtime processing. This means that the FIR filter needs to be
implemented with the help of delay lines and the impulse response is
truncated.

ECG filtering

Record an ECG in the lab. Book a time slot and record it (see instructions on
moodle how to record it and complying with the ethics by signing the consent
sheet). Alternatively, use an ECG from moodle matching the last digit of your
matriculation number. Biomedical students or those with a strong interest in
biosignals should record their own ECG as it’s an important part of their
training.

1. Create two functions which calculate and return the FIR filter coefficients
numerically (= using python’s IFFT command) for a

a) highpass filter and

b) a bandstop filter.

Name these functions “highpassDesign” and “bandstopDesign”.

Both functions should automatically decide how many coefficients are
required. The function arguments should be the sampling rate and the
cutoff frequencies (and any other optional arguments you like to
provide). Feel free to put both functions in a class. [25%]

2. Create a Python FIR filter class which implements an FIR filter which has
a method of the form value dofilter(self,value) where both the value
argument and return value are scalars and not vectors (!) so that it can
be used in a realtime system. The constructor of the class takes the
coefficients as its input:

class FIRfilter:

def __init__(self,_coefficients):
# your code here
def dofilter(self,v):
# your code here
return result

Filter your ECG with the above FIR filter class using the coefficients from

1. by removing the 50Hz interference and the baseline wander with the
highpass. Decide which cutoff frequencies are needed and provide
explanations by referring to the spectra and/or fundamental frequencies
of the ECG. Simulate realtime processing by feeding the ECG sample by
sample into your FIR filter class. Make sure that the ECG looks intact and
that it is not distorted (PQRST intact). Provide appropriate plots. [25%]

3. Instead of using a bandstop filter to remove the 50Hz use an adaptive
LMS filter by providing it with a 50Hz sine wave as the reference noise.
Add an adaptive LMS filter command to your FIR filter class and name it:
“doFilterAdaptive(self,signal,noise,learningRate)” which returns the
cleaned up ECG. As before also this function must receive only scalars
(i.e. sample by sample) and return a scalar. Plot and compare the result
from the adaptive filter and that from the FIR filter design. [25%]

4. ECG heartbeat detection The task is to detect R-peaks in your ECG. Write
a matched filter which uses a wavelet as your R-peak template because
wavelets look pretty much like R-peaks. Make sure the wavelet has the
correct duration and shape that it matches your R-peaks. It’s important in
the report that you show a plot of a real R peak and the wavelet side by
side. Plot the momentary heartrate (i.e. inverse intervals between R
peaks) against time. [25%]

Every report must be based on a different ECG recording or on the recoding
matching your matriculation number. Please keep it short but it should make
clear what you have done and why you have done it. Include the complete
Python code as well as plots of the ECGs (timedomain) and their frequency
representation (with proper labels). If necessary annotate the plots with
InkScape, Corel Draw or Illustrator by labelling the ECG peaks and remember to
use vector based image formats, for example EPS, SVG, PDF or EMF and not
pixel based formats for the report. These figures need to be in the report at the
correct place and not attached separately. Also, show zoomed in ECG traces of
one heartbeat so that it is possible to identify the different parts of a single
heartbeat and that it’s possible to check if it’s still intact.

No high level Python functions except of FFT/IFFT and the window functions are
allowed. Any use of “lfilter”, “firwin”, “conv”, “correl” and any a-causal
processing (i.e. data array in and array out) commands will result in zero or
very low marks. As before submit a zip file containing all files and test the zip
before submission by unzipping it and then running python from the command
line in a terminal (not spyder). Also check that all plots are generated when
running the script from the commandline. See moodle for the exact filename
conventions.