--- a
+++ b/4-Models/autoECG-tensorflow-keras/parameters.py
@@ -0,0 +1,60 @@
+import numpy as np
+
+
+##Sampling Rate and Duration
+sampling_rate = 250 #Hz
+duration = 10 #seconds
+
+## Gamma, a (12,5) matrix to modify the five waves' amplitudes of 12 leads
+gamma = np.array([[1, 0.1, 1, 1.2, 1],
+                   [2, 0.2, 0.2, 0.2, 3],
+                   [1, -0.1, -0.8, -1.1, 2.5],
+                   [-1, -0.05, -0.8, -0.5, -1.2],
+                   [0.05, 0.05, 1, 1, 1],
+                   [1, -0.05, -0.1, -0.1, 3],
+                   [-0.5, 0.05, 0.2, 0.5, 1],
+                   [0.05, 0.05, 1.3, 2.5, 2],
+                   [1, 0.05, 1, 2, 1],
+                   [1.2, 0.05, 1, 2, 2],
+                   [1.5, 0.1, 0.8, 1, 2],
+                   [1.8, 0.05, 0.5, 0.1, 2]])
+
+## Normal ECG Parameters
+Anoise = 0.01
+
+#heart rate
+mu_hr_1 = 60          # mean of the heart rate
+sigma_hr_1 = 7        # variance of the heart rate
+
+#noise
+min_noise_1 = 0.01      
+max_noise_1 = 0.1
+
+#For PQRST five spikes
+# t, the starting position along the circle of each interval in radius
+mu_t_1 = np.array((-70, -15, 0, 15, 100))  
+sigma_t_1 = np.ones(5)*3
+
+# a, the amplitude of each spike; b, the width of each spike
+mu_a_1 = np.array((1.2, -5, 30, -7.5, 0.75))
+mu_b_1 = np.array((0.25, 0.1, 0.1, 0.1, 0.4))
+sigma_a_1 = np.abs(mu_a_1/5)
+sigma_b_1 = np.abs(mu_b_1/5)
+
+## Abnormal ECG Parameters
+#heart rate
+mu_hr_2 = 60          # mean of the heart rate
+sigma_hr_2 = 7        # variance of the heart rate
+
+#noise
+min_noise_2 = 0.01      
+max_noise_2 = 0.1
+
+#t, a, b
+mu_t_2 = np.array((-70, -15, 0, 15, 100))
+mu_a_2 = np.array((1.2, -4, 25, -6.5, 0.75))
+mu_b_2 = np.array((0.25, 0.1, 0.1, 0.1, 0.4))
+sigma_t_2 = np.ones(5)*3
+sigma_a_2 = np.abs(mu_a_1/5)
+sigma_b_2 = np.abs(mu_b_1/5)
+