Task 1: Plot the data in the range from 9 to 11 seconds! [30 Points]

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# TODO your plotting code goes here

# please take the sampling rate into account to only plot the range

# between 9 and 11 seconds

Task 2: Use Neurokit to detect the R spikes! [30 Points]

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# now can you see the P Q R S T waves? it's actually a pretty good signal despite

# the shift in the baseline

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# we now need to install the neurokit2 library

!pip install neurokit2

import neurokit2 as nk

Requirement already satisfied: neurokit2 in /usr/local/lib/python3.6/dist-packages (0.0.42) Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from neurokit2) (1.4.1) Requirement already satisfied: sklearn in /usr/local/lib/python3.6/dist-packages (from neurokit2) (0.0) Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from neurokit2) (3.2.2) Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from neurokit2) (1.1.5) Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from neurokit2) (1.19.5) Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn->neurokit2) (0.22.2.post1) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->neurokit2) (2.8.1) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->neurokit2) (1.3.1) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->neurokit2) (0.10.0) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->neurokit2) (2.4.7) Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->neurokit2) (2018.9) Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn->neurokit2) (1.0.0) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->neurokit2) (1.15.0)

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# we can use neurokit's function to detect the R spikes

_, rpeaks = nk.ecg_peaks(ecg, sampling_rate=SAMPLING_RATE)

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# look at rpeaks - the numbers indicate the samples that are R spikes

print(rpeaks['ECG_R_Peaks'][0:10]) # here only the first ten

[ 125 343 552 748 944 1130 1317 1501 1691 1880]

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# you can also plot the first two detected R spikes

plot = nk.events_plot(rpeaks['ECG_R_Peaks'][0:2], ecg[0:SAMPLING_RATE])

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# TODO Can you write code to plot the R spikes between 9 and 11 seconds?

# Hint: one solution could be to just detect peaks in the range you specified for Task 1

# Hint 2: you might need to extend the range by +- 1 second to see the five peaks from Task 1

Task 3: Detect Heartrate. [15 Points]

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# TODO Does the patient have a regular heart rate? If yes, which one? (estimate is fine)

# Hint: you can use the rpeaks for that

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Task 4: Detect P Q S T Peaks. [25 Points]

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# We know the R spikes now but what about P Q S T waves?

# TODO Can you use nk.ecg_delineate to detect them in the range of 9 to 11 seconds?

# Hint: Look at the tutorial: https://neurokit2.readthedocs.io/en/latest/examples/ecg_delineate.html#Locate-other-waves-(P,-Q,-S,-T)-and-their-onset-and-offset

# Hint 2: The plot will have blue, orange, green, and red circles :)

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# TODO Does the detection work well?

Bonus Task: Detect P Q S T Peaks for the full signal before and after filtering. [33 Points]

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# TODO As above use the nk.ecg_delineate function to detect peaks for the whole signal

# Hint: For plotting, 1000 samples / 5 rpeaks gives a nice overview.

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# TODO Let's filter the signal and see if the detection improves.

# Hint: You can use nk.signal_filter to define a bandpass.

# Hint 2: Which lowcut and highcut frequencies work well? Play with it :)

# Hint 3: You don't need to re-calculate the rpeaks since they are pretty accurate in general.

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# TODO Does the filtering improve the result?

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#

# Great job!!

#

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