From e9e540ad8008beb04125897ef3aa473e218919b4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=AD=8F=E9=81=87=E5=8D=BF?= <358614401@qq.com> Date: Thu, 16 Mar 2023 16:06:03 +0800 Subject: [PATCH] =?UTF-8?q?take=20CNN=20test=20by=20keras=20or=20nokeras?= =?UTF-8?q?=20use=20withtf=E3=80=81pytorch?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- _mian_pytorch.py | 92 ++++++++++++++++++++++++++++++++++++ _mian_tensorflow.py | 97 ++++++++++++++++++++++++++++++++++++++ _mian_tensorflow_keras_.py | 92 ++++++++++++++++++++++++++++++++++++ noise_take_ecg.py | 7 ++- wfdb_use.py | 16 +++++-- 5 files changed, 298 insertions(+), 6 deletions(-) create mode 100644 _mian_pytorch.py create mode 100644 _mian_tensorflow.py create mode 100644 _mian_tensorflow_keras_.py diff --git a/_mian_pytorch.py b/_mian_pytorch.py new file mode 100644 index 0000000..22c4b63 --- /dev/null +++ b/_mian_pytorch.py @@ -0,0 +1,92 @@ +import numpy as np +import pywt +import wfdb +import torch + +from ECG_read_weiyuqing_without_wfdb import PATH + + +def denoise(date): + # Wavelet db5 level9 + coeffs = pywt.wavedec(data=date, wavelet='db5', level=9) + cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs + # noice del + threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1)))) + # take cD 1,2 turn 0 to denoise + cD1.fill(0) + cD2.fill(0) + for i in range(1, len(coeffs) - 2): + coeffs[i] = pywt.threshold(coeffs[i], threshold) + # get signal that before wavelet + rdata = pywt.waverec(coeffs=coeffs, wavelet='db5') + return rdata + + +# get ECG data,date type and denoise +# this function without return +# use append take return data to pass data +def getDataSet(number, X_data, Y_data): + ecgClassSet = ['N', 'A', 'V', 'l', 'R'] + print('get' + number + ' ECG data') + record = wfdb.rdrecord(PATH + number, channel_names=['MLII']) + data = record.p_signal.flatten() + # use def denoise + rdata = denoise(date=data) + + # get type of ECG data + annotation = wfdb.rdann(PATH + number, 'atr') + Rlocation = annotation.sample # annotation.sample:get R + Rclass = annotation.symbol + + # delete stable date near R + start = 10 + end = 5 + i = start + j = len(annotation.symbol) - end + + # get NAVLR type data + while i < j: + try: + lable = ecgClassSet.index(Rclass[i]) + x_train = rdata[Rlocation[i] - 99:Rlocation[i] + 201] + X_data.append(x_train) + Y_data.append(lable) + i += 1 + except ValueError: + i += 1 + return + + +# RATIO=The ratio of the training set to the test set，usually is 0.3 +def loadData(RATIO): + numberSet = [ + '100', '101', '103', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '119', + '121', '122', '123', '124', '200', '201', '202', '203', '205', '208', '210', '212', '213', '214', '215', '217', + '219', '220', '221', '222', '223', '228', '230', '231', '232', '233', '234' + ] + dataSet = [] + labelSet = [] + for n in numberSet: + getDataSet(n, dataSet, labelSet) + + dataSet = np.array(dataSet).reshape(-1, 300) + labelSet = np.array(labelSet).reshape(-1, 1) + train_ds = np.hstack((dataSet, labelSet)) + np.random.shuffle(train_ds) + + X = train_ds[:, :300].reshape(-1, 300, 1) + Y = train_ds[:, 300] + # cut test and train data from ECG data + shuffle_index = np.random.permutation(len(X)) + test_length = int(RATIO * len(shuffle_index)) + test_index = shuffle_index[:test_length] + train_index = shuffle_index[test_index:] + X_test, Y_test = X[test_index:], Y[test_index] + X_train, Y_train = X[train_index], Y[train_index] + return X_train, Y_test, X_test, Y_train + + +class CNN_model_level_one(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv1 = torch.nn.Conv1d(in_channels=1,out_channels=1,) \ No newline at end of file diff --git a/_mian_tensorflow.py b/_mian_tensorflow.py new file mode 100644 index 0000000..37eb11d --- /dev/null +++ b/_mian_tensorflow.py @@ -0,0 +1,97 @@ +import numpy as np +import pywt +import wfdb +import tensorflow as tf +from tensorflow import keras + +from ECG_read_weiyuqing_without_wfdb import PATH + + +def denoise(date): + # Wavelet db5 level9 + coeffs = pywt.wavedec(data=date, wavelet='db5', level=9) + cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs + # noice del + threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1)))) + # take cD 1,2 turn 0 to denoise + cD1.fill(0) + cD2.fill(0) + for i in range(1, len(coeffs) - 2): + coeffs[i] = pywt.threshold(coeffs[i], threshold) + # get signal that before wavelet + rdata = pywt.waverec(coeffs=coeffs, wavelet='db5') + return rdata + + +# get ECG data,date type and denoise +# this function without return +# use append take return data to pass data +def getDataSet(number, X_data, Y_data): + ecgClassSet = ['N', 'A', 'V', 'l', 'R'] + print('get' + number + ' ECG data') + record = wfdb.rdrecord(PATH + number, channel_names=['MLII']) + data = record.p_signal.flatten() + # use def denoise + rdata = denoise(date=data) + + # get type of ECG data + annotation = wfdb.rdann(PATH + number, 'atr') + Rlocation = annotation.sample # annotation.sample:get R + Rclass = annotation.symbol + + # delete stable date near R + start = 10 + end = 5 + i = start + j = len(annotation.symbol) - end + + # get NAVLR type data + while i < j: + try: + lable = ecgClassSet.index(Rclass[i]) + x_train = rdata[Rlocation[i] - 99:Rlocation[i] + 201] + X_data.append(x_train) + Y_data.append(lable) + i += 1 + except ValueError: + i += 1 + return + + +# RATIO=The ratio of the training set to the test set，usually is 0.3 +def loadData(RATIO): + numberSet = [ + '100', '101', '103', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '119', + '121', '122', '123', '124', '200', '201', '202', '203', '205', '208', '210', '212', '213', '214', '215', '217', + '219', '220', '221', '222', '223', '228', '230', '231', '232', '233', '234' + ] + dataSet = [] + labelSet = [] + for n in numberSet: + getDataSet(n, dataSet, labelSet) + + dataSet = np.array(dataSet).reshape(-1, 300) + labelSet = np.array(labelSet).reshape(-1, 1) + train_ds = np.hstack((dataSet, labelSet)) + np.random.shuffle(train_ds) + + X = train_ds[:, :300].reshape(-1, 300, 1) + Y = train_ds[:, 300] + # cut test and train data from ECG data + shuffle_index = np.random.permutation(len(X)) + test_length = int(RATIO * len(shuffle_index)) + test_index = shuffle_index[:test_length] + train_index = shuffle_index[test_index:] + X_test, Y_test = X[test_index:], Y[test_index] + X_train, Y_train = X[train_index], Y[train_index] + return X_train, Y_test, X_test, Y_train + + +class CNN_model_level_one(): + def __init__(self): + newModel = tf.keras.models.Sequential([ + tf.keras.InputLayer(input_shape=(300, 1)), + tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='same', activation='relu'), + tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='same'), + tf.keras.layers.Conv1D(filters=16,) + ]) diff --git a/_mian_tensorflow_keras_.py b/_mian_tensorflow_keras_.py new file mode 100644 index 0000000..0e06d9e --- /dev/null +++ b/_mian_tensorflow_keras_.py @@ -0,0 +1,92 @@ +import numpy as np +import pywt +import wfdb +import tensorflow as tf + +from ECG_read_weiyuqing_without_wfdb import PATH + + +def denoise(date): + # Wavelet db5 level9 + coeffs = pywt.wavedec(data=date, wavelet='db5', level=9) + cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs + # noice del + threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1)))) + # take cD 1,2 turn 0 to denoise + cD1.fill(0) + cD2.fill(0) + for i in range(1, len(coeffs) - 2): + coeffs[i] = pywt.threshold(coeffs[i], threshold) + # get signal that before wavelet + rdata = pywt.waverec(coeffs=coeffs, wavelet='db5') + return rdata + + +# get ECG data,date type and denoise +# this function without return +# use append take return data to pass data +def getDataSet(number, X_data, Y_data): + ecgClassSet = ['N', 'A', 'V', 'l', 'R'] + print('get' + number + ' ECG data') + record = wfdb.rdrecord(PATH + number, channel_names=['MLII']) + data = record.p_signal.flatten() + # use def denoise + rdata = denoise(date=data) + + # get type of ECG data + annotation = wfdb.rdann(PATH + number, 'atr') + Rlocation = annotation.sample # annotation.sample:get R + Rclass = annotation.symbol + + # delete stable date near R + start = 10 + end = 5 + i = start + j = len(annotation.symbol) - end + + # get NAVLR type data + while i < j: + try: + lable = ecgClassSet.index(Rclass[i]) + x_train = rdata[Rlocation[i] - 99:Rlocation[i] + 201] + X_data.append(x_train) + Y_data.append(lable) + i += 1 + except ValueError: + i += 1 + return + + +# RATIO=The ratio of the training set to the test set，usually is 0.3 +def loadData(RATIO): + numberSet = [ + '100', '101', '103', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '119', + '121', '122', '123', '124', '200', '201', '202', '203', '205', '208', '210', '212', '213', '214', '215', '217', + '219', '220', '221', '222', '223', '228', '230', '231', '232', '233', '234' + ] + dataSet = [] + labelSet = [] + for n in numberSet: + getDataSet(n, dataSet, labelSet) + + dataSet = np.array(dataSet).reshape(-1, 300) + labelSet = np.array(labelSet).reshape(-1, 1) + train_ds = np.hstack((dataSet, labelSet)) + np.random.shuffle(train_ds) + + X = train_ds[:, :300].reshape(-1, 300, 1) + Y = train_ds[:, 300] + # cut test and train data from ECG data + shuffle_index = np.random.permutation(len(X)) + test_length = int(RATIO * len(shuffle_index)) + test_index = shuffle_index[:test_length] + train_index = shuffle_index[test_index:] + X_test, Y_test = X[test_index:], Y[test_index] + X_train, Y_train = X[train_index], Y[train_index] + return X_train, Y_test, X_test, Y_train + + +class CNN_model_level_one(): + def __init__(self): + super().__init__() + self.conv1 = tf.layers.Conv1d(filter=4) diff --git a/noise_take_ecg.py b/noise_take_ecg.py index 10fa811..4b7eff5 100644 --- a/noise_take_ecg.py +++ b/noise_take_ecg.py @@ -3,9 +3,14 @@ import pywt import wfdb from matplotlib import pyplot as plt +#file error + +#use db5 ,level9 do deviose +from ECG_read_weiyuqing_without_wfdb import PATH + #from ECG_read_weiyuqing_without_wfdb import PATH -record=wfdb.rdrecord('./mit-bih-arrhythmia-database-1.0.0/'+100,channel_names=['MLII']) +record=wfdb.rdrecord(PATH+100,channel_names=['MLII']) data=record.p_signal.flatten() coeffs=pywt.wavedec(data=data,wavelet='db5',level=9) diff --git a/wfdb_use.py b/wfdb_use.py index 427e8c8..9da84a0 100644 --- a/wfdb_use.py +++ b/wfdb_use.py @@ -7,6 +7,8 @@ import matplotlib.pyplot PATH = './mit-bih-arrhythmia-database-1.0.0/' +# file error with read_ecg_data(data) parameter data false + def read_ecg_data(data): # get the data from the ecg data file and set begin and end points record = wfdb.rdrecord(PATH + data, sampfrom=0, sampto=1500) @@ -27,19 +29,23 @@ def read_ecg_data(data): print(record.fs) - annotations = wfdb.rdann(PATH+data,'atr') + # get type + annotations = wfdb.rdann(PATH + data, 'atr') print(type(annotations)) print(dir(annotations)) - #get R + # get R print(annotations.sample) - #get label of Heart beat + # get label of Heart beat print(annotations.sample) - #num + # num print(annotations.ann_len) - #show label of beat + # show label of beat print(wfdb.show_ann_labels()) matplotlib.pyplot.draw_ecg(record.p_signal) return record.p_signal + +if __name__ == '__main__': + read_ecg_data(100)