1- Perform smoothing by using the scipy ECG signal used in the shared codes as an example. For smoothing, use the Simple Moving Average, Exponential Moving Average, Triple Exponential Moving Average, Savitzky-Golay and Ensemble Average methods. Find the PQRST points after smoothing. 2- Examine the entire ECG signal with STFT. Prepare the program that automatically labels the blank (no beat) regions, normal beat regions and abnormal beat regions on the STFT graph.

00_basic_operatins # -*- coding: utf-8 eauthor: A.c. SECKIN from scipy.misc import electrocardiogram import matplotlib.pyplot as plt import numpy as np #get ready to use ECG signal ecg = electrocardiogram () #sampling frequency 360Hz fs - 360 # sample abnormal signal X = ecg[17000:19000] # generating time axis values tx = np.arange (x.size) / fs plt.figure () plt.subplot( 321) plt.title( "Raw signal") plt.plot (tx, x) plt.grid() modulator=np.sin(2*np.pi+3*tx) plt.subplot( 322) plt.title("Modulator Signal") plt.plot (tx, modulator) y - x*modulator plt.subplot (323) plt.title( "multipled") plt.plot (tx, y) plt.grid() y = x+modulator plt.subplot (324) plt.titie("addtion") plt.plot (tx, y) plt.grid() #gradient or derivative of the signal dx=np.gradient (x) # generating time axis values tdx = np. arange (x.size) / fs plt.figure() plt.title( "Gradient/derivative") #plt.plot (tdx, dx) plt.plot (tx, x, "r", tdx, dx, "b") plt.grid()