Repository: CVxTz/EEG_classification
Branch: master
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Directory structure:
gitextract_jfjg8v0s/
├── .gitignore
├── LICENSE
├── README.md
├── code/
│ ├── baseline.py
│ ├── cnn_crf_model.py
│ ├── cnn_crf_model_20_folds.py
│ ├── cnn_model.py
│ ├── eda.py
│ ├── lstm_model.py
│ ├── models.py
│ ├── run.sh
│ └── utils.py
├── deepsleepnet_data/
│ ├── dhedfreader.py
│ ├── download_physionet.sh
│ ├── prepare_physionet.py
│ └── readme.md
└── requirements.txt
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FILE: README.md
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[](https://doi.org/10.5281/zenodo.4060151)
# EEG_classification
Description of the approach : https://towardsdatascience.com/sleep-stage-classification-from-single-channel-eeg-using-convolutional-neural-networks-5c710d92d38e
Sleep Stage Classification from Single Channel EEG using Convolutional Neural
Networks
*****
Photo by [Paul
M](https://unsplash.com/photos/7i9yLoUgoP8?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
on
[Unsplash](https://unsplash.com/search/photos/owl?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
Quality Sleep is an important part of a healthy lifestyle as lack of it can
cause a list of
[issues](https://www.webmd.com/sleep-disorders/features/10-results-sleep-loss#1)
like a higher risk of cancer and chronic fatigue. This means that having the
tools to automatically and easily monitor sleep can be powerful to help people
sleep better.
Doctors use a recording of a signal called EEG which measures
the electrical activity of the brain using an electrode to understand sleep
stages of a patient and make a diagnosis about the quality if their sleep.
In this post we will train a neural network to do the sleep stage classification
automatically from EEGs.
### **Data**
In our input we have a sequence of 30s epochs of EEG where each epoch has a
label [{“W”, “N1”, “N2”, “N3”,
“REM”}](https://en.wikipedia.org/wiki/Sleep_cycle).
Fig 1 : EEG Epoch
Fig 2 : Sleep stages through the night
This post is based on a publicly available EEG Sleep data (
[Sleep-EDF](https://www.physionet.org/physiobank/database/sleep-edfx/) ) that
was done on 20 subject, 19 of which have 2 full nights of sleep. We use the
pre-processing scripts available in this
[repo](https://github.com/akaraspt/deepsleepnet) and split the train/test so
that no study subject is in both at the same time.
The general objective is to go from a 1D sequence like in fig 1 and predict the
output hypnogram like in fig 2.
### Model Description
Recent approaches [[1]](https://arxiv.org/pdf/1703.04046.pdf) use a sub-model
that encodes each epoch into a 1D vector of fixed size and then a second
sequential sub-model that maps each epoch’s vector into a class from [{“W”,
“N1”, “N2”, “N3”, “REM”}](https://en.wikipedia.org/wiki/Sleep_cycle).
Here we use a 1D CNN to encode each Epoch and then another 1D CNN or LSTM that
labels the sequence of epochs to create the final
[hypnogram](https://en.wikipedia.org/wiki/Hypnogram). This allows the prediction
for an epoch to take into account the context.
Sub-model 1 : Epoch encoder
Sub-model 2 : Sequential model for epoch classification
The full model takes as input the sequence of EEG epochs ( 30 seconds each)
where the sub-model 1 is applied to each epoch using the TimeDistributed Layer
of [Keras](https://keras.io/) which produces a sequence of vectors. The sequence
of vectors is then fed into a another sub-model like an LSTM or a CNN that
produces the sequence of output labels.
We also use a linear Chain
[CRF](https://en.wikipedia.org/wiki/Conditional_random_field) for one of the
models and show that it can improve the performance.
### Training Procedure
The full model is trained end-to-end from scratch using Adam optimizer with an
initial learning rate of 1e⁻³ that is reduced each time the validation accuracy
plateaus using the ReduceLROnPlateau Keras Callbacks.
Accuracy Training curves
### Results
We compare 3 different models :
* CNN-CNN : This ones used a 1D CNN for the epoch encoding and then another 1D CNN
for the sequence labeling.
* CNN-CNN-CRF : This model used a 1D CNN for the epoch encoding and then a 1D
CNN-CRF for the sequence labeling.
* CNN-LSTM : This ones used a 1D CNN for the epoch encoding and then an LSTM for
the sequence labeling.
We evaluate each model on an independent test set and get the following results
:
* CNN-CNN : F1 = 0.81, ACCURACY = 0.87
* CNN-CNN-CRF : F1 = 0.82, ACCURACY =0.89
* CNN-LSTM : F1 = 0.71, ACCURACY = 0.76
The CNN-CNN-CRF outperforms the two other models because the CRF helps learn the
transition probabilities between classes. The LSTM based model does not work as
well because it is most sensitive to hyper-parameters like the optimizer and the
batch size and requires extensive tuning to perform well.
Ground Truth Hypnogram
Predicted Hypnogram using CNN-CNN-CRF
Source code available here :
[https://github.com/CVxTz/EEG_classification](https://github.com/CVxTz/EEG_classification)
I look forward to your suggestions and feedback.
[[1] DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw
Single-Channel EEG](https://arxiv.org/pdf/1703.04046.pdf)
How to cite:
```
@software{mansar_youness_2020_4060151,
author = {Mansar Youness},
title = {CVxTz/EEG\_classification: v1.0},
month = sep,
year = 2020,
publisher = {Zenodo},
version = {v1.0},
doi = {10.5281/zenodo.4060151},
url = {https://doi.org/10.5281/zenodo.4060151}
}
```
================================================
FILE: code/baseline.py
================================================
import numpy as np
from glob import glob
import os
from sklearn.model_selection import train_test_split
base_path = "/media/ml/data_ml/EEG/deepsleepnet/data_npy"
files = glob(os.path.join(base_path, "*.npz"))
train_val, test = train_test_split(files, test_size=0.15, random_state=1337)
train, val = train_test_split(train_val, test_size=0.1, random_state=1337)
train_dict = {k: np.load(k) for k in train}
test_dict = {k: np.load(k) for k in test}
val_dict = {k: np.load(k) for k in val}
================================================
FILE: code/cnn_crf_model.py
================================================
from models import get_model_cnn_crf
import numpy as np
from utils import gen, chunker, WINDOW_SIZE, rescale_array
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from sklearn.metrics import f1_score, accuracy_score, classification_report
from glob import glob
import os
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import matplotlib.pyplot as plt
base_path = "/media/ml/data_ml/EEG/deepsleepnet/data_npy"
files = sorted(glob(os.path.join(base_path, "*.npz")))
ids = sorted(list(set([x.split("/")[-1][:5] for x in files])))
#split by test subject
train_ids, test_ids = train_test_split(ids, test_size=0.15, random_state=1338)
train_val, test = [x for x in files if x.split("/")[-1][:5] in train_ids],\
[x for x in files if x.split("/")[-1][:5] in test_ids]
train, val = train_test_split(train_val, test_size=0.1, random_state=1337)
train_dict = {k: np.load(k) for k in train}
test_dict = {k: np.load(k) for k in test}
val_dict = {k: np.load(k) for k in val}
model = get_model_cnn_crf()
file_path = "cnn_crf_model.h5"
# model.load_weights(file_path)
checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
early = EarlyStopping(monitor="val_acc", mode="max", patience=20, verbose=1)
redonplat = ReduceLROnPlateau(monitor="val_acc", mode="max", patience=5, verbose=2)
callbacks_list = [checkpoint, early, redonplat] # early
model.fit_generator(gen(train_dict, aug=False), validation_data=gen(val_dict), epochs=100, verbose=2,
steps_per_epoch=1000, validation_steps=300, callbacks=callbacks_list)
model.load_weights(file_path)
preds = []
gt = []
for record in tqdm(test_dict):
all_rows = test_dict[record]['x']
record_y_gt = []
record_y_pred = []
for batch_hyp in chunker(range(all_rows.shape[0])):
X = all_rows[min(batch_hyp):max(batch_hyp)+1, ...]
Y = test_dict[record]['y'][min(batch_hyp):max(batch_hyp)+1]
X = np.expand_dims(X, 0)
X = rescale_array(X)
Y_pred = model.predict(X)
Y_pred = Y_pred.argmax(axis=-1).ravel().tolist()
gt += Y.ravel().tolist()
preds += Y_pred
record_y_gt += Y.ravel().tolist()
record_y_pred += Y_pred
# fig_1 = plt.figure(figsize=(12, 6))
# plt.plot(record_y_gt)
# plt.title("Sleep Stages")
# plt.ylabel("Classes")
# plt.xlabel("Time")
# plt.show()
#
# fig_2 = plt.figure(figsize=(12, 6))
# plt.plot(record_y_pred)
# plt.title("Predicted Sleep Stages")
# plt.ylabel("Classes")
# plt.xlabel("Time")
# plt.show()
f1 = f1_score(gt, preds, average="macro")
print("Seq Test f1 score : %s "% f1)
acc = accuracy_score(gt, preds)
print("Seq Test accuracy score : %s "% acc)
print(classification_report(gt, preds))
================================================
FILE: code/cnn_crf_model_20_folds.py
================================================
from models import get_model_cnn_crf
import numpy as np
from utils import gen, chunker, WINDOW_SIZE, rescale_array
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from sklearn.metrics import f1_score, accuracy_score, classification_report
from glob import glob
import os
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import matplotlib.pyplot as plt
base_path = "/media/ml/data_ml/EEG/deepsleepnet/data_npy"
files = sorted(glob(os.path.join(base_path, "*.npz")))
ids = list(set([x.split("/")[-1][:5] for x in files]))
list_f1 = []
list_acc = []
preds = []
gt = []
for id in ids:
test_ids = {id}
train_ids = set([x.split("/")[-1][:5] for x in files]) - test_ids
train_val, test = [x for x in files if x.split("/")[-1][:5] in train_ids],\
[x for x in files if x.split("/")[-1][:5] in test_ids]
train, val = train_test_split(train_val, test_size=0.1, random_state=1337)
train_dict = {k: np.load(k) for k in train}
test_dict = {k: np.load(k) for k in test}
val_dict = {k: np.load(k) for k in val}
model = get_model_cnn_crf(lr=0.0001)
file_path = "cnn_crf_model_20_folds.h5"
# model.load_weights(file_path)
checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
early = EarlyStopping(monitor="val_acc", mode="max", patience=20, verbose=1)
redonplat = ReduceLROnPlateau(monitor="val_acc", mode="max", patience=5, verbose=2)
callbacks_list = [checkpoint, redonplat] # early
model.fit_generator(gen(train_dict, aug=False), validation_data=gen(val_dict), epochs=40, verbose=2,
steps_per_epoch=1000, validation_steps=300, callbacks=callbacks_list)
model.load_weights(file_path)
for record in tqdm(test_dict):
all_rows = test_dict[record]['x']
record_y_gt = []
record_y_pred = []
for batch_hyp in chunker(range(all_rows.shape[0])):
X = all_rows[min(batch_hyp):max(batch_hyp)+1, ...]
Y = test_dict[record]['y'][min(batch_hyp):max(batch_hyp)+1]
X = np.expand_dims(X, 0)
X = rescale_array(X)
Y_pred = model.predict(X)
Y_pred = Y_pred.argmax(axis=-1).ravel().tolist()
gt += Y.ravel().tolist()
preds += Y_pred
record_y_gt += Y.ravel().tolist()
record_y_pred += Y_pred
f1 = f1_score(gt, preds, average="macro")
acc = accuracy_score(gt, preds)
print("acc %s, f1 %s"%(acc, f1))
================================================
FILE: code/cnn_model.py
================================================
from models import get_model_cnn
import numpy as np
from utils import gen, chunker, WINDOW_SIZE, rescale_array
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from sklearn.metrics import f1_score, accuracy_score, classification_report
from glob import glob
import os
from sklearn.model_selection import train_test_split
from tqdm import tqdm
base_path = "/media/ml/data_ml/EEG/deepsleepnet/data_npy"
files = sorted(glob(os.path.join(base_path, "*.npz")))
ids = sorted(list(set([x.split("/")[-1][:5] for x in files])))
#split by test subject
train_ids, test_ids = train_test_split(ids, test_size=0.15, random_state=1338)
train_val, test = [x for x in files if x.split("/")[-1][:5] in train_ids],\
[x for x in files if x.split("/")[-1][:5] in test_ids]
train, val = train_test_split(train_val, test_size=0.1, random_state=1337)
train_dict = {k: np.load(k) for k in train}
test_dict = {k: np.load(k) for k in test}
val_dict = {k: np.load(k) for k in val}
model = get_model_cnn()
file_path = "cnn_model.h5"
# model.load_weights(file_path)
checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
early = EarlyStopping(monitor="val_acc", mode="max", patience=20, verbose=1)
redonplat = ReduceLROnPlateau(monitor="val_acc", mode="max", patience=5, verbose=2)
callbacks_list = [checkpoint, early, redonplat] # early
model.fit_generator(gen(train_dict, aug=False), validation_data=gen(val_dict), epochs=100, verbose=2,
steps_per_epoch=1000, validation_steps=300, callbacks=callbacks_list)
model.load_weights(file_path)
preds = []
gt = []
for record in tqdm(test_dict):
all_rows = test_dict[record]['x']
for batch_hyp in chunker(range(all_rows.shape[0])):
X = all_rows[min(batch_hyp):max(batch_hyp)+1, ...]
Y = test_dict[record]['y'][min(batch_hyp):max(batch_hyp)+1]
X = np.expand_dims(X, 0)
X = rescale_array(X)
Y_pred = model.predict(X)
Y_pred = Y_pred.argmax(axis=-1).ravel().tolist()
gt += Y.ravel().tolist()
preds += Y_pred
f1 = f1_score(gt, preds, average="macro")
print("Seq Test f1 score : %s "% f1)
acc = accuracy_score(gt, preds)
print("Seq Test accuracy score : %s "% acc)
print(classification_report(gt, preds))
================================================
FILE: code/eda.py
================================================
import os
import h5py
import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
import collections
import librosa
path = "/media/ml/data_ml/EEG/deepsleepnet/data_npy/SC4061E0.npz"
data = np.load(path)
x = data['x']
y = data['y']
fig_1 = plt.figure(figsize=(12, 6))
plt.plot(x[100, ...].ravel())
plt.title("EEG Epoch")
plt.ylabel("Amplitude")
plt.xlabel("Time")
plt.show()
fig_2 = plt.figure(figsize=(12, 6))
plt.plot(y.ravel())
plt.title("Sleep Stages")
plt.ylabel("Classes")
plt.xlabel("Time")
plt.show()
================================================
FILE: code/lstm_model.py
================================================
from models import get_model_lstm
import numpy as np
from utils import gen, chunker, WINDOW_SIZE, rescale_array
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from sklearn.metrics import f1_score, accuracy_score, classification_report
from glob import glob
import os
from sklearn.model_selection import train_test_split
from tqdm import tqdm
base_path = "/media/ml/data_ml/EEG/deepsleepnet/data_npy"
files = sorted(glob(os.path.join(base_path, "*.npz")))
ids = sorted(list(set([x.split("/")[-1][:5] for x in files])))
#split by test subject
train_ids, test_ids = train_test_split(ids, test_size=0.15, random_state=1338)
train_val, test = [x for x in files if x.split("/")[-1][:5] in train_ids],\
[x for x in files if x.split("/")[-1][:5] in test_ids]
train, val = train_test_split(train_val, test_size=0.1, random_state=1337)
train_dict = {k: np.load(k) for k in train}
test_dict = {k: np.load(k) for k in test}
val_dict = {k: np.load(k) for k in val}
model = get_model_lstm()
file_path = "lstm_model.h5"
# model.load_weights(file_path)
checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
early = EarlyStopping(monitor="val_acc", mode="max", patience=20, verbose=1)
redonplat = ReduceLROnPlateau(monitor="val_acc", mode="max", patience=5, verbose=2)
callbacks_list = [checkpoint, early, redonplat] # early
model.fit_generator(gen(train_dict, aug=False), validation_data=gen(val_dict), epochs=100, verbose=2,
steps_per_epoch=1000, validation_steps=300, callbacks=callbacks_list)
model.load_weights(file_path)
preds = []
gt = []
for record in tqdm(test_dict):
all_rows = test_dict[record]['x']
for batch_hyp in chunker(range(all_rows.shape[0])):
X = all_rows[min(batch_hyp):max(batch_hyp)+1, ...]
Y = test_dict[record]['y'][min(batch_hyp):max(batch_hyp)+1]
X = np.expand_dims(X, 0)
X = rescale_array(X)
Y_pred = model.predict(X)
Y_pred = Y_pred.argmax(axis=-1).ravel().tolist()
gt += Y.ravel().tolist()
preds += Y_pred
f1 = f1_score(gt, preds, average="macro")
print("Seq Test f1 score : %s "% f1)
acc = accuracy_score(gt, preds)
print("Seq Test accuracy score : %s "% acc)
print(classification_report(gt, preds))
================================================
FILE: code/models.py
================================================
from keras import optimizers, losses, activations, models
from keras.layers import Dense, Input, Dropout, Convolution1D, MaxPool1D, GlobalMaxPool1D, GlobalAveragePooling1D, \
concatenate, SpatialDropout1D, TimeDistributed, Bidirectional, LSTM
from keras_contrib.layers import CRF
from utils import WINDOW_SIZE
def get_model():
nclass = 5
inp = Input(shape=(3000, 1))
img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = GlobalMaxPool1D()(img_1)
img_1 = Dropout(rate=0.01)(img_1)
dense_1 = Dropout(rate=0.01)(Dense(64, activation=activations.relu, name="dense_1")(img_1))
dense_1 = Dropout(rate=0.05)(Dense(64, activation=activations.relu, name="dense_2")(dense_1))
dense_1 = Dense(nclass, activation=activations.softmax, name="dense_3")(dense_1)
model = models.Model(inputs=inp, outputs=dense_1)
opt = optimizers.Adam(0.001)
model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc'])
model.summary()
return model
def get_base_model():
inp = Input(shape=(3000, 1))
img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = GlobalMaxPool1D()(img_1)
img_1 = Dropout(rate=0.01)(img_1)
dense_1 = Dropout(0.01)(Dense(64, activation=activations.relu, name="dense_1")(img_1))
base_model = models.Model(inputs=inp, outputs=dense_1)
opt = optimizers.Adam(0.001)
base_model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc'])
#model.summary()
return base_model
def get_model_cnn():
nclass = 5
seq_input = Input(shape=(None, 3000, 1))
base_model = get_base_model()
# for layer in base_model.layers:
# layer.trainable = False
encoded_sequence = TimeDistributed(base_model)(seq_input)
encoded_sequence = SpatialDropout1D(rate=0.01)(Convolution1D(128,
kernel_size=3,
activation="relu",
padding="same")(encoded_sequence))
encoded_sequence = Dropout(rate=0.05)(Convolution1D(128,
kernel_size=3,
activation="relu",
padding="same")(encoded_sequence))
#out = TimeDistributed(Dense(nclass, activation="softmax"))(encoded_sequence)
out = Convolution1D(nclass, kernel_size=3, activation="softmax", padding="same")(encoded_sequence)
model = models.Model(seq_input, out)
model.compile(optimizers.Adam(0.001), losses.sparse_categorical_crossentropy, metrics=['acc'])
model.summary()
return model
def get_model_lstm():
nclass = 5
seq_input = Input(shape=(None, 3000, 1))
base_model = get_base_model()
for layer in base_model.layers:
layer.trainable = False
encoded_sequence = TimeDistributed(base_model)(seq_input)
encoded_sequence = Bidirectional(LSTM(100, return_sequences=True))(encoded_sequence)
encoded_sequence = Dropout(rate=0.5)(encoded_sequence)
encoded_sequence = Bidirectional(LSTM(100, return_sequences=True))(encoded_sequence)
#out = TimeDistributed(Dense(nclass, activation="softmax"))(encoded_sequence)
out = Convolution1D(nclass, kernel_size=1, activation="softmax", padding="same")(encoded_sequence)
model = models.Model(seq_input, out)
model.compile(optimizers.Adam(0.001), losses.sparse_categorical_crossentropy, metrics=['acc'])
model.summary()
return model
def get_model_cnn_crf(lr=0.001):
nclass = 5
seq_input = Input(shape=(None, 3000, 1))
base_model = get_base_model()
# for layer in base_model.layers:
# layer.trainable = False
encoded_sequence = TimeDistributed(base_model)(seq_input)
encoded_sequence = SpatialDropout1D(rate=0.01)(Convolution1D(128,
kernel_size=3,
activation="relu",
padding="same")(encoded_sequence))
encoded_sequence = Dropout(rate=0.05)(Convolution1D(128,
kernel_size=3,
activation="linear",
padding="same")(encoded_sequence))
#out = TimeDistributed(Dense(nclass, activation="softmax"))(encoded_sequence)
# out = Convolution1D(nclass, kernel_size=3, activation="linear", padding="same")(encoded_sequence)
crf = CRF(nclass, sparse_target=True)
out = crf(encoded_sequence)
model = models.Model(seq_input, out)
model.compile(optimizers.Adam(lr), crf.loss_function, metrics=[crf.accuracy])
model.summary()
return model
================================================
FILE: code/run.sh
================================================
#python cnn_model.py > cnn_logs.txt
#python cnn_crf_model.py > cnn_crf_logs.txt
#python lstm_model.py > lstm_logs.txt
python cnn_crf_model_20_folds.py > cnn_crf_folds_logs.txt
================================================
FILE: code/utils.py
================================================
import h5py
import numpy as np
import random
WINDOW_SIZE = 100
def rescale_array(X):
X = X / 20
X = np.clip(X, -5, 5)
return X
def aug_X(X):
scale = 1 + np.random.uniform(-0.1, 0.1)
offset = np.random.uniform(-0.1, 0.1)
noise = np.random.normal(scale=0.05, size=X.shape)
X = scale * X + offset + noise
return X
def gen(dict_files, aug=False):
while True:
record_name = random.choice(list(dict_files.keys()))
batch_data = dict_files[record_name]
all_rows = batch_data['x']
for i in range(10):
start_index = random.choice(range(all_rows.shape[0]-WINDOW_SIZE))
X = all_rows[start_index:start_index+WINDOW_SIZE, ...]
Y = batch_data['y'][start_index:start_index+WINDOW_SIZE]
X = np.expand_dims(X, 0)
Y = np.expand_dims(Y, -1)
Y = np.expand_dims(Y, 0)
if aug:
X = aug_X(X)
X = rescale_array(X)
yield X, Y
def chunker(seq, size=WINDOW_SIZE):
return (seq[pos:pos + size] for pos in range(0, len(seq), size))
================================================
FILE: deepsleepnet_data/dhedfreader.py
================================================
#Source : https://github.com/akaraspt/deepsleepnet
'''
Reader for EDF+ files.
TODO:
- add support for log-transformed channels:
http://www.edfplus.info/specs/edffloat.html and test with
data generated with
http://www.edfplus.info/downloads/software/NeuroLoopGain.zip.
- check annotations with Schalk's Physiobank data.
Copyright (c) 2012 Boris Reuderink.
'''
import re, datetime, operator, logging
import numpy as np
from collections import namedtuple
EVENT_CHANNEL = 'EDF Annotations'
log = logging.getLogger(__name__)
class EDFEndOfData: pass
def tal(tal_str):
'''Return a list with (onset, duration, annotation) tuples for an EDF+ TAL
stream.
'''
exp = '(?P[+\-]\d+(?:\.\d*)?)' + \
'(?:\x15(?P\d+(?:\.\d*)?))?' + \
'(\x14(?P[^\x00]*))?' + \
'(?:\x14\x00)'
def annotation_to_list(annotation):
return unicode(annotation, 'utf-8').split('\x14') if annotation else []
def parse(dic):
return (
float(dic['onset']),
float(dic['duration']) if dic['duration'] else 0.,
annotation_to_list(dic['annotation']))
return [parse(m.groupdict()) for m in re.finditer(exp, tal_str)]
def edf_header(f):
h = {}
assert f.tell() == 0 # check file position
assert f.read(8) == '0 '
# recording info)
h['local_subject_id'] = f.read(80).strip()
h['local_recording_id'] = f.read(80).strip()
# parse timestamp
(day, month, year) = [int(x) for x in re.findall('(\d+)', f.read(8))]
(hour, minute, sec)= [int(x) for x in re.findall('(\d+)', f.read(8))]
h['date_time'] = str(datetime.datetime(year + 2000, month, day,
hour, minute, sec))
# misc
header_nbytes = int(f.read(8))
subtype = f.read(44)[:5]
h['EDF+'] = subtype in ['EDF+C', 'EDF+D']
h['contiguous'] = subtype != 'EDF+D'
h['n_records'] = int(f.read(8))
h['record_length'] = float(f.read(8)) # in seconds
nchannels = h['n_channels'] = int(f.read(4))
# read channel info
channels = range(h['n_channels'])
h['label'] = [f.read(16).strip() for n in channels]
h['transducer_type'] = [f.read(80).strip() for n in channels]
h['units'] = [f.read(8).strip() for n in channels]
h['physical_min'] = np.asarray([float(f.read(8)) for n in channels])
h['physical_max'] = np.asarray([float(f.read(8)) for n in channels])
h['digital_min'] = np.asarray([float(f.read(8)) for n in channels])
h['digital_max'] = np.asarray([float(f.read(8)) for n in channels])
h['prefiltering'] = [f.read(80).strip() for n in channels]
h['n_samples_per_record'] = [int(f.read(8)) for n in channels]
f.read(32 * nchannels) # reserved
assert f.tell() == header_nbytes
return h
class BaseEDFReader:
def __init__(self, file):
self.file = file
def read_header(self):
self.header = h = edf_header(self.file)
# calculate ranges for rescaling
self.dig_min = h['digital_min']
self.phys_min = h['physical_min']
phys_range = h['physical_max'] - h['physical_min']
dig_range = h['digital_max'] - h['digital_min']
assert np.all(phys_range > 0)
assert np.all(dig_range > 0)
self.gain = phys_range / dig_range
def read_raw_record(self):
'''Read a record with data and return a list containing arrays with raw
bytes.
'''
result = []
for nsamp in self.header['n_samples_per_record']:
samples = self.file.read(nsamp * 2)
if len(samples) != nsamp * 2:
raise EDFEndOfData
result.append(samples)
return result
def convert_record(self, raw_record):
'''Convert a raw record to a (time, signals, events) tuple based on
information in the header.
'''
h = self.header
dig_min, phys_min, gain = self.dig_min, self.phys_min, self.gain
time = float('nan')
signals = []
events = []
for (i, samples) in enumerate(raw_record):
if h['label'][i] == EVENT_CHANNEL:
ann = tal(samples)
time = ann[0][0]
events.extend(ann[1:])
# print(i, samples)
# exit()
else:
# 2-byte little-endian integers
dig = np.fromstring(samples, ' 0:
remove_idx = np.hstack(remove_idx)
select_idx = np.setdiff1d(np.arange(len(raw_ch_df)), remove_idx)
else:
select_idx = np.arange(len(raw_ch_df))
print "after remove unwanted: {}".format(select_idx.shape)
# Select only the data with labels
print "before intersect label: {}".format(select_idx.shape)
label_idx = np.hstack(label_idx)
select_idx = np.intersect1d(select_idx, label_idx)
print "after intersect label: {}".format(select_idx.shape)
# Remove extra index
if len(label_idx) > len(select_idx):
print "before remove extra labels: {}, {}".format(select_idx.shape, labels.shape)
extra_idx = np.setdiff1d(label_idx, select_idx)
# Trim the tail
if np.all(extra_idx > select_idx[-1]):
n_trims = len(select_idx) % int(EPOCH_SEC_SIZE * sampling_rate)
n_label_trims = int(math.ceil(n_trims / (EPOCH_SEC_SIZE * sampling_rate)))
select_idx = select_idx[:-n_trims]
labels = labels[:-n_label_trims]
print "after remove extra labels: {}, {}".format(select_idx.shape, labels.shape)
# Remove movement and unknown stages if any
raw_ch = raw_ch_df.values[select_idx]
# Verify that we can split into 30-s epochs
if len(raw_ch) % (EPOCH_SEC_SIZE * sampling_rate) != 0:
raise Exception("Something wrong")
n_epochs = len(raw_ch) / (EPOCH_SEC_SIZE * sampling_rate)
# Get epochs and their corresponding labels
x = np.asarray(np.split(raw_ch, n_epochs)).astype(np.float32)
y = labels.astype(np.int32)
assert len(x) == len(y)
# Select on sleep periods
w_edge_mins = 30
nw_idx = np.where(y != stage_dict["W"])[0]
start_idx = nw_idx[0] - (w_edge_mins * 2)
end_idx = nw_idx[-1] + (w_edge_mins * 2)
if start_idx < 0: start_idx = 0
if end_idx >= len(y): end_idx = len(y) - 1
select_idx = np.arange(start_idx, end_idx+1)
print("Data before selection: {}, {}".format(x.shape, y.shape))
x = x[select_idx]
y = y[select_idx]
print("Data after selection: {}, {}".format(x.shape, y.shape))
# Save
filename = ntpath.basename(psg_fnames[i]).replace("-PSG.edf", ".npz")
save_dict = {
"x": x,
"y": y,
"fs": sampling_rate,
"ch_label": select_ch,
"header_raw": h_raw,
"header_annotation": h_ann,
}
np.savez(os.path.join(args.output_dir, filename), **save_dict)
print "\n=======================================\n"
if __name__ == "__main__":
main()
================================================
FILE: deepsleepnet_data/readme.md
================================================
The files in this folders were copied with minor modifications from #Source : https://github.com/akaraspt/deepsleepnet
#To get the dataset :
cd data
chmod +x download_physionet.sh
./download_physionet.sh
###Those scripts taken from the deepsleepnet only work with python2
python2 prepare_physionet.py --data_dir data --output_dir data/eeg_fpz_cz --select_ch 'EEG Fpz-Cz'
This subfolder is under the following license :
Copyright 2017 Akara Supratak and Hao Dong. All rights reserved.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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Copyright 2017 Akara Supratak and Hao Dong
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================================================
FILE: requirements.txt
================================================
librosa==0.5.1
numpy==1.15.2
Keras==2.2.2
tqdm==4.23.2
keras_contrib==2.0.8
h5py==2.8.0
matplotlib==2.1.0
scikit_learn==0.20.0