code: def electrocardiogram():
      """
      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:: 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.
  
      Returns
      -------
      ecg : ndarray
          The electrocardiogram in millivolt (mV) sampled at 360 Hz.
  
      Notes
      -----
      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.
  
      .. _record 208: https://physionet.org/physiobank/database/html/mitdbdir/records.htm#208
  
      .. versionadded:: 1.1.0
  
      References
      ----------
      .. [1] 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`
      .. [2] 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`
  
      Examples
      --------
      >>> 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)
      >>> 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()
      """
      import os
  
      file_path = os.path.join(os.path.dirname(__file__), "ecg.dat")
      with load(file_path) as file:
          ecg = file["ecg"].astype(int)  # np.uint16 -> int
      # Convert raw output of ADC to mV: (ecg - adc_zero) / adc_gain
      ecg = (ecg - 1024) / 200.0
      return ecg
  
file_path: scipy/scipy/misc/_common.py
imports: from scipy._lib.deprecation import _deprecated
  from scipy._lib._finite_differences import _central_diff_weights, _derivative
  from numpy import array, frombuffer, load
label_desc: Use image node_id 5 for calling a global function with example usage: electrocardiogram() and returns: ecg