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- 首页 >> Java编程 Assignment 2, Digital Signal processing: FIR
filters
The task of this assignment is to filter an ECG with FIR filters and to
detect the R peaks. In contrast the FFT assignment here 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.
1 ECG filtering
Download the ECG according to the last digit of your matric number.
1. Create a Python FIR filter class which implements an FIR filter which
has a method of the form value dofilter(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 FIR_filter:
def __init__(self,_coefficients):
# your code here
def dofilter(self,v):
# your code here
return result
Implement the FIR filter in an efficeint way for example by using a
ring buffer or by smart application of the Python slicing operations.
1
Minimise the amount of data being shifted and explain how / why you
have done it. Put the filter class in a separate file for example fir-
filter.py so that it turns into a module which can be imported by the
main program. [20%]
2. Add a unit test to the module fir-filter.py from 1 which tests if the
FIR filter works properly. For example the delay line and the proper
multiplication of the coefficients. The unit test should be called if one
starts the module with python fir-filter.py. Add the unit test by
calling a function called unittest if run as a main program:
if __name__ == "__main__":
unittest()
[20%]
3. Download a short ECG from moodle according to the last digits of
your matric number. Filter this ECG with the above FIR filter class
by removing the 50Hz interference and the DC. Decide which cutoff
frequencies are needed and provide explanations by referring to the
spectra and/or fundamental frequencies. Calculate the FIR filter coefficients
numerically ( = using python’s IFFT command). 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. [20%].
2 ECG heartrate detection
The task is to detect the momentary heart rate r(t) over a longer period of
time. For example, after exercise you should see a slow decay of the heart rate
to the baseline of perhaps 60 beats per minute. It is not the average heart
rate but the frequency derived from the times between adjacent heartbeats.
1. Create a matched filter by using one QRST complex from an ECG
and detect the R-peaks. Remember that for matched filters DC free
signals are important. Here, the pre-filtering of the ECGs can be done
differently to the filtering above to reduce as much DC as possible and
any interference. [20%]
2. Use an Einthoven II recording where the person is walking using this
ECG database: https://pypi.org/project/ecg-gudb-database/. The
BEng students take subject numbers 0-9 matching their matriculation
number and the postgraduate students subject numbers 10-19 with
matching matric numbers 0-9. The database has a Python API and
there is an example on github (https://github.com/berndporr/ECG-GUDB)
how to use it. Calculate the momentary heart rate r(t) over time (not
the average!) by measuring the intervals between the detected hearbeats
over the whole period of the ECG. Detect the heartbeats by
employing a threshold. Add code which removes wrong detections and
explain what the code does. [20%]
Every report must be based on a different ECG recording. Please keep it
short but it should make clear what you have done and why you have done
it. Include the Python code as well as plots of the ECGs (timedomain) and
their frequency representation (with proper lables). If necessary enhance the
plots with InkScape, Corel 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 (see Fig. 1) 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 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.
Deadline is 9th November.
Figure 1: A normal ECG with P,Q,R,S,T waves (taken from Wikipedia)
filters
The task of this assignment is to filter an ECG with FIR filters and to
detect the R peaks. In contrast the FFT assignment here 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.
1 ECG filtering
Download the ECG according to the last digit of your matric number.
1. Create a Python FIR filter class which implements an FIR filter which
has a method of the form value dofilter(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 FIR_filter:
def __init__(self,_coefficients):
# your code here
def dofilter(self,v):
# your code here
return result
Implement the FIR filter in an efficeint way for example by using a
ring buffer or by smart application of the Python slicing operations.
1
Minimise the amount of data being shifted and explain how / why you
have done it. Put the filter class in a separate file for example fir-
filter.py so that it turns into a module which can be imported by the
main program. [20%]
2. Add a unit test to the module fir-filter.py from 1 which tests if the
FIR filter works properly. For example the delay line and the proper
multiplication of the coefficients. The unit test should be called if one
starts the module with python fir-filter.py. Add the unit test by
calling a function called unittest if run as a main program:
if __name__ == "__main__":
unittest()
[20%]
3. Download a short ECG from moodle according to the last digits of
your matric number. Filter this ECG with the above FIR filter class
by removing the 50Hz interference and the DC. Decide which cutoff
frequencies are needed and provide explanations by referring to the
spectra and/or fundamental frequencies. Calculate the FIR filter coefficients
numerically ( = using python’s IFFT command). 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. [20%].
2 ECG heartrate detection
The task is to detect the momentary heart rate r(t) over a longer period of
time. For example, after exercise you should see a slow decay of the heart rate
to the baseline of perhaps 60 beats per minute. It is not the average heart
rate but the frequency derived from the times between adjacent heartbeats.
1. Create a matched filter by using one QRST complex from an ECG
and detect the R-peaks. Remember that for matched filters DC free
signals are important. Here, the pre-filtering of the ECGs can be done
differently to the filtering above to reduce as much DC as possible and
any interference. [20%]
2. Use an Einthoven II recording where the person is walking using this
ECG database: https://pypi.org/project/ecg-gudb-database/. The
BEng students take subject numbers 0-9 matching their matriculation
number and the postgraduate students subject numbers 10-19 with
matching matric numbers 0-9. The database has a Python API and
there is an example on github (https://github.com/berndporr/ECG-GUDB)
how to use it. Calculate the momentary heart rate r(t) over time (not
the average!) by measuring the intervals between the detected hearbeats
over the whole period of the ECG. Detect the heartbeats by
employing a threshold. Add code which removes wrong detections and
explain what the code does. [20%]
Every report must be based on a different ECG recording. Please keep it
short but it should make clear what you have done and why you have done
it. Include the Python code as well as plots of the ECGs (timedomain) and
their frequency representation (with proper lables). If necessary enhance the
plots with InkScape, Corel 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 (see Fig. 1) 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 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.
Deadline is 9th November.
Figure 1: A normal ECG with P,Q,R,S,T waves (taken from Wikipedia)