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1DCNN.py

+import pandas as pd
+from keras.models import Sequential
+from keras.layers import Dense, Conv1D, LeakyReLU, MaxPooling1D, Flatten, Dropout
+from keras.wrappers.scikit_learn import KerasClassifier
+from sklearn.model_selection import cross_val_score
+from sklearn.preprocessing import LabelEncoder, StandardScaler, RobustScaler, OneHotEncoder
+from sklearn.model_selection import StratifiedKFold
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import plot_confusion_matrix, confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, roc_curve
+
+import csv
+import numpy as np
+from numpy import dstack
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn import svm, metrics, datasets
+import matplotlib.pyplot as plt
+from scipy import signal
+from scipy.signal import find_peaks
+from scipy.fftpack import fft
+import pywt
+
+def Extract(lst):
+    return [item[-1] for item in lst]
+  
+target = []
+lis = []
+data = []
+labels = ['apnea', 'no apnea']
+
+with open("drive/My Drive/ApneaData.csv") as csvDataFile:
+    csvReader = csv.reader(csvDataFile)
+    for line in csvReader:
+        lis.append(line[0].split())  # create a list of lists
+
+for i in range(1, len(lis)):
+    data.append(list(map(int, lis[i])))
+
+target = Extract(data)  # sleep apn or not
+
+data = np.array(data)
+target = np.array(target)
+