--- a
+++ b/model/lstmRNNModel.py
@@ -0,0 +1,105 @@
+"""
+	LSTM-RNN model for  OSA detection.
+"""
+
+from keras.models import Sequential
+from keras.layers import Dense, LSTM
+from keras.utils import plot_model
+import os
+import numpy as np
+import tensorflow as tf
+import keras.backend.tensorflow_backend as KTF
+
+from model.common import TrainingMonitor, ModelCheckpoint, LossHistory
+
+RR_INTERVALS_INTERPOLATION = 240
+# handcraft_features
+
+test_number = 1
+base_floder_path = "result/lstm/" + "test_" + str(test_number) + "/"
+
+if not os.path.exists(base_floder_path):
+	os.makedirs(base_floder_path)
+train_loss_path = base_floder_path + "train_loss.txt"
+validation_loss_path = base_floder_path + "validation_loss.txt"
+train_acc_path = base_floder_path + "train_acc.txt"
+validation_acc_path = base_floder_path + "validation_acc.txt"
+
+# GPU config
+config = tf.ConfigProto()
+config.gpu_options.allow_growth = True
+config.gpu_options.per_process_gpu_memory_fraction = 0.7
+sess = tf.Session(config=config)
+KTF.set_session(sess)
+
+
+def get_dataset():
+	train_rri_amp_edr = np.load("G:/python project/apneaECGCode/data/apnea-ecg_train_clear_rri_ramp_edr.npy")
+	train_label = np.load("G:/python project/apneaECGCode/data/apnea-ecg_train_clear_label.npy")
+	test_rri_amp_edr = np.load("G:/python project/apneaECGCode/data/apnea-ecg_test_clear_rri_ramp_edr.npy")
+	test_label = np.load("G:/python project/apneaECGCode/data/apnea-ecg_test_clear_label.npy")
+	
+	train_label = train_label.astype(dtype=np.int)
+	test_label = test_label.astype(dtype=np.int)
+	return train_rri_amp_edr, train_label, test_rri_amp_edr, test_label
+
+
+def create_lstm_model(input_shape):
+	model = Sequential()
+	model.add(LSTM(384, input_shape=input_shape, use_bias=True, dropout=0.1,
+				   recurrent_dropout=0.05, return_sequences=True))
+	# model.add(LeakyReLU(alpha=1))
+	# model.add(BatchNormalization())
+	model.add(LSTM(384, use_bias=True, dropout=0.2,
+				   recurrent_dropout=0.05, return_sequences=True))
+	# model.add(LeakyReLU(alpha=1))
+	# model.add(BatchNormalization())
+	model.add(LSTM(384, use_bias=True, dropout=0.3,
+				   recurrent_dropout=0.05))
+	# model.add(LeakyReLU(alpha=1))
+	# model.add(BatchNormalization())
+	# model.add(LSTM(64, use_bias=True,
+	# 			   dropout=0.7, recurrent_dropout=0.7))
+	# model.add(LeakyReLU(alpha=1))
+	# model.add(BatchNormalization())
+	model.add(Dense(128))
+	# model.add(Dropout(0.8))
+	# model.add(LeakyReLU(alpha=1))
+	model.add(Dense(64))
+	model.add(Dense(32))
+	# model.add(Dropout(0.5))
+	# model.add(LeakyReLU(alpha=1))
+	model.add(Dense(1, activation="sigmoid"))
+	
+	model.compile(loss="binary_crossentropy", optimizer="adam", metrics=['accuracy'])
+	
+	model.summary()
+	plot_model(model, to_file=base_floder_path + '/lstm_model.png', show_shapes=True)
+	
+	return model
+
+
+def train_network():
+	print("read data...")
+	X_train1, y_train, X_test1, y_test = get_dataset()
+	
+	model = create_lstm_model(input_shape=(RR_INTERVALS_INTERPOLATION, 3))
+	fig_path = base_floder_path
+	model_file_path = base_floder_path + "/model"
+	if not os.path.exists(model_file_path):
+		os.makedirs(model_file_path)
+	model_file_path += "/model_{epoch:02d}-{val_acc:.6f}.hdf5"
+	checkpoint = ModelCheckpoint(model_file_path, monitor='val_acc', verbose=1, save_best_only=True)
+	callbacks = [
+		TrainingMonitor(fig_path, model, train_loss_path, validation_loss_path, train_acc_path, validation_acc_path)
+		, checkpoint]
+	print("Training")
+	history = LossHistory()
+	history.init()
+	model.fit(X_train1, y_train, batch_size=128, epochs=500, callbacks=callbacks, validation_data=(X_test1, y_test))
+	return model
+
+
+if __name__ == '__main__':
+	train_network()
+