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Project 3
Automatic heart rate counter
Due on May 15th, 2024
The aim of this project is to practice quantitative analysis in biomedical applications. Specifically, you
are expected to use mathematical functions embedded in Matlab to process a practical ECG signal so
that the R-wave or heartbeat can be counted. Please prepare your report as follows:
1- A report in Word format should be submitted on UBLearns. No hardcopies are accepted. The
report must include all outputs and/or plots generated by your MATLAB programs. Your
Matlab code should be included at the end of the report. Name the file “Project 3 Name.docx”
where Name is replaced by your full name.
2- You may search for relevant materials or tutorial online to complete the design of the filter.
Please include all references used in the final report.
3- Copying of MATLAB programs from other students or any other form of plagiarism will
result in a zero grade for all involved.
Electrocardiography(ECG) signals are very weak physiological low-frequency electrical signals,
typically with a maximum amplitude of 5mV and a frequency range of 0.05 ~ 100Hz. These signals
are picked up by electrodes placed on the surface of the skin. However, the polarization phenomenon
between the electrodes and the skin tissue can cause significant interference with the ECG signals. It
is important to note that the human body is a complex living system. Physiological signals can
interfere with ECG signals, making weak ECG signals highly vulnerable. Common sources of
interference include:
(1) Muscle Tremor (2) Baseline Wander
In this project, you will be given a 10-second electrocardiogram in mat format. Follow the steps
below to reduce the above-mentioned interferences using Matlab.
Questions:
Use load ecg.mat to import the provided ECG signal M and its time points TIME. The sampling
frequency of the ECG signals is 1500.
1. To reduce muscle tremor (4 points)
The raw ECG signals typically contain interference from Electromyography (EMG) signals, The
frequency of the EMG signals ranges from 20 to 5000Hz, with its main component typically ranging
from 30 to 300Hz, depending on the type of muscle. In contrast, the frequency of the ECG signals is
mainly concentrated in the range of 5 to 20Hz. In this question, you need to
 Choose a low-pass filter to filter out the muscle electrical interference;
 Plot the time domain graph of the input signal and filtered output in one figure, use title
information for each sub-plot;
 Plot the Fourier transform graph of the input signal and filtered output in one figure, use title
information for each sub-plot.
2. To reduce baseline wander (4 points)
The energy spectrum range of ECG is between 1-80Hz. The frequency of baseline drift is typically
between 0.6 and 1.4Hz. This indicates that the baseline drift is a slowly changing ultra-low-frequency
signal. To correct baseline drift, low-frequency signals in the ECG signal should be removed while
preserving signal information such as amplitude and phase. The elliptic filter is one of the filters that
can fix the baseline wander. In this question, you are expected to
 Use Matlab to implement a 3rd order highpass elliptic filter that has a value of peak-to-peak
passband ripple of 0.1390dB, stopband attenuation 20dB and normalized passband edge
frequency 0.0019π rad/sample;
 Plot the time domain graph of the input signal and filtered output in one figure, use title
information for each sub-plot;
 Plot the Fourier transform graph of the input signal and filtered output in one figure, use title
information for each sub-plot.
3. Find and count the R-wave of the ECG signal (2 points)
Many investigators prefer to measure inter-beat intervals using the R-wave peak as the reference point.
This is because it can result in reduced errors, making it appropriate for studying RR or heart rate
variability. It is important to choose this method in order to obtain more accurate measurements of
intrinsic variability. In this question, you need to
 Get all peaks of R wave by using Matlab from the output generated from the 2nd question and
plot the peaks on the output of the 2nd question in a new figure.