electrocardiogram()
The returned signal is a 5 minute long electrocardiogram (ECG), a medical recording of the heart's electrical activity, sampled at 360 Hz.
The provided signal is an excerpt (19:35 to 24:35) from the `record 208`_ (lead MLII) provided by the MIT-BIH Arrhythmia Database on PhysioNet . The excerpt includes noise induced artifacts, typical heartbeats as well as pathological changes.
<Unimplemented 'target' '.. _record 208: https://physionet.org/physiobank/database/html/mitdbdir/records.htm#208'>
The electrocardiogram in millivolt (mV) sampled at 360 Hz.
Load an electrocardiogram as an example for a 1-D signal.
>>> from scipy.misc import electrocardiogram
... ecg = electrocardiogram()
... ecg array([-0.245, -0.215, -0.185, ..., -0.405, -0.395, -0.385])
>>> ecg.shape, ecg.mean(), ecg.std() ((108000,), -0.16510875, 0.5992473991177294)
As stated the signal features several areas with a different morphology. E.g., the first few seconds show the electrical activity of a heart in normal sinus rhythm as seen below.
>>> import matplotlib.pyplot as plt
... fs = 360
... time = np.arange(ecg.size) / fs
... plt.plot(time, ecg)
... plt.xlabel("time in s")
... plt.ylabel("ECG in mV")
... plt.xlim(9, 10.2)
... plt.ylim(-1, 1.5)
... plt.show()
After second 16, however, the first premature ventricular contractions, also called extrasystoles, appear. These have a different morphology compared to typical heartbeats. The difference can easily be observed in the following plot.
>>> plt.plot(time, ecg)
... plt.xlabel("time in s")
... plt.ylabel("ECG in mV")
... plt.xlim(46.5, 50)
... plt.ylim(-2, 1.5)
... plt.show()
At several points large artifacts disturb the recording, e.g.:
>>> plt.plot(time, ecg)
... plt.xlabel("time in s")
... plt.ylabel("ECG in mV")
... plt.xlim(207, 215)
... plt.ylim(-2, 3.5)
... plt.show()
Finally, examining the power spectrum reveals that most of the biosignal is made up of lower frequencies. At 60 Hz the noise induced by the mains electricity can be clearly observed.
>>> from scipy.signal import welch
... f, Pxx = welch(ecg, fs=fs, nperseg=2048, scaling="spectrum")
... plt.semilogy(f, Pxx)
... plt.xlabel("Frequency in Hz")
... plt.ylabel("Power spectrum of the ECG in mV**2")
... plt.xlim(f[[0, -1]])
... plt.show()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.misc._common.electrocardiogram
scipy.signal._peak_finding.find_peaks
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