Load an electrocardiogram as an example for a 1-D signal.

The returned signal is a 5 minute long electrocardiogram (ECG), a medical recording of the heart’s electrical activity, sampled at 360 Hz.

Deprecated since version 1.10.0: electrocardiogram has been deprecated from scipy.misc.electrocardiogram in SciPy 1.10.0 and it will be completely removed in SciPy 1.12.0. Dataset methods have moved into the scipy.datasets module. Use scipy.datasets.electrocardiogram instead.


The electrocardiogram in millivolt (mV) 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 [1] on PhysioNet [2]. The excerpt includes noise induced artifacts, typical heartbeats as well as pathological changes.

Added in version 1.1.0.



Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID: 11446209); DOI:10.13026/C2F305


Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220; DOI:10.1161/01.CIR.101.23.e215


>>> 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 numpy as np
>>> 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)

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)

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)

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]])