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Introduction to epilepsy
Epilepsy, commonly known as "epilepsy wind", is a chronic brain dysfunction syndrome caused by a variety of causes. It is the second brain disease after cerebrovascular disease. The direct cause of epileptic seizures is the intermittent central nervous system dysfunction caused by the recurrent sudden excessive discharge of neurons in the brain. Clinically, it is often manifested as sudden loss of consciousness, general convulsions and mental disorders. Epilepsy brings great pain and physical and mental injury to patients, even life-threatening when it is serious. Children patients will affect their physical and mental development.
EEG is an important tool to study the characteristics of epileptic seizures. It is a noninvasive biophysical examination method, and the information it reflects is not provided by other physiological methods. The analysis of EEG is mainly to detect the abnormal discharge activity of brain, including spike wave, sharp wave, spike and slow complex wave, etc. At present, medical workers carry out visual detection of patients' EEG according to experience. This work is not only very time-consuming, but also subjective due to human analysis. Different experts may have different judgment results for the same record, which leads to the increase of misdiagnosis rate. Therefore, the use of automatic detection, recognition and prediction technology for the timely and accurate diagnosis and prediction of epileptic EEG, the location of epileptic focus and reducing the storage of EEG data is an important part of the study of epileptic EEG signal [1].
data set
Dataset: epileptic seizure recognition dataset
Download address:
https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition
11500 samples of 178 data points (178 data points = EEG recording of 1 second) 11500 targets with 5 categories: 1 for epileptic seizure waveform, and 2-5 for non epileptic seizure waveform
Keras deep learning case
Code references are organized in:
http://dy.163.com/v2/article/detail/EEC68EH5054281P3.html
#Import tool library import pandas as pd import numpy as np import matplotlib.pyplot as plt from keras.models import Sequential from keras import layers from keras import regularizers from sklearn.model_selection import train_test_split from sklearn.metrics import roc_curve, auc # Load dataset data = "data.csv" df = pd.read_csv(data, header=0, index_col=0) """ //View the head and information of the dataset """ print(df.head()) print(df.info())
# Mapping and observing brain waves plt.plot(range(178), df.iloc[11496,0:178]) plt.show()
(11500, 178)
""" //Create model """ model = Sequential() model.add(layers.Dense(64, activation="relu", kernel_regularizer=regularizers.l1(0.001), input_shape = (178,))) model.add(layers.Dropout(0.5)) model.add(layers.Dense(64, activation="relu", kernel_regularizer=regularizers.l1(0.001))) model.add(layers.Dropout(0.5)) model.add(layers.Dense(1, activation="sigmoid")) model.summary() """ //Using the train ﹣ test ﹣ split function of sklearn, 20% of all data is regarded as the test set and the rest as the training set """ x_train, x_test, y_train, y_test = train_test_split(wave, target, test_size=0.2, random_state=42) #Compiling machine learning model model.compile(optimizer="rmsprop", loss="binary_crossentropy", metrics=["acc"]) """ //Training model epoch For 100, batch_size For 128, //Set 20% of data set as validation set """ history = model.fit(x_train, y_train, epochs=100, batch_size=128, validation_split=0.2, verbose=2) # Test data (forecast data) y_pred = model.predict(x_test).ravel() # Calculate ROC fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred) # Calculate AUC AUC = auc(fpr_keras, tpr_keras) # Draw ROC curve plt.plot(fpr_keras, tpr_keras, label='Keras Model(area = {:.3f})'.format(AUC)) plt.xlabel('False positive Rate') plt.ylabel('True positive Rate') plt.title('ROC curve') plt.legend(loc='best') plt.show()
Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 64) 11456 _________________________________________________________________ dropout_1 (Dropout) (None, 64) 0 _________________________________________________________________ dense_2 (Dense) (None, 64) 4160 _________________________________________________________________ dropout_2 (Dropout) (None, 64) 0 _________________________________________________________________ dense_3 (Dense) (None, 1) 65 ================================================================= Total params: 15,681 Trainable params: 15,681 Non-trainable params: 0 _________________________________________________________________ Train on 7360 samples, validate on 1840 samples Epoch 1/100 - 0s - loss: 1.9573 - acc: 0.7432 - val_loss: 1.6758 - val_acc: 0.9098 Epoch 2/100 - 0s - loss: 1.5837 - acc: 0.8760 - val_loss: 1.3641 - val_acc: 0.9332 Epoch 3/100 - 0s - loss: 1.2899 - acc: 0.9201 - val_loss: 1.1060 - val_acc: 0.9424 Epoch 4/100 - 0s - loss: 1.0525 - acc: 0.9404 - val_loss: 0.9179 - val_acc: 0.9446 Epoch 5/100 - 0s - loss: 0.8831 - acc: 0.9466 - val_loss: 0.7754 - val_acc: 0.9484 Epoch 6/100 - 0s - loss: 0.7291 - acc: 0.9552 - val_loss: 0.6513 - val_acc: 0.9538 Epoch 7/100 - 0s - loss: 0.6149 - acc: 0.9572 - val_loss: 0.5541 - val_acc: 0.9495 Epoch 8/100 - 0s - loss: 0.5232 - acc: 0.9558 - val_loss: 0.4717 - val_acc: 0.9484 Epoch 9/100 - 0s - loss: 0.4443 - acc: 0.9595 - val_loss: 0.4118 - val_acc: 0.9489 Epoch 10/100 - 0s - loss: 0.3921 - acc: 0.9590 - val_loss: 0.3667 - val_acc: 0.9554 Epoch 11/100 - 0s - loss: 0.3579 - acc: 0.9553 - val_loss: 0.3348 - val_acc: 0.9565 Epoch 12/100 - 0s - loss: 0.3302 - acc: 0.9572 - val_loss: 0.3209 - val_acc: 0.9473 Epoch 13/100 - 0s - loss: 0.3154 - acc: 0.9546 - val_loss: 0.2988 - val_acc: 0.9560 Epoch 14/100 - 0s - loss: 0.2956 - acc: 0.9596 - val_loss: 0.2899 - val_acc: 0.9500 Epoch 15/100 - 0s - loss: 0.2907 - acc: 0.9565 - val_loss: 0.2786 - val_acc: 0.9500 Epoch 16/100 - 0s - loss: 0.2794 - acc: 0.9607 - val_loss: 0.2665 - val_acc: 0.9560 Epoch 17/100 - 0s - loss: 0.2712 - acc: 0.9588 - val_loss: 0.2636 - val_acc: 0.9598 Epoch 18/100 - 0s - loss: 0.2665 - acc: 0.9603 - val_loss: 0.2532 - val_acc: 0.9533 Epoch 19/100 - 0s - loss: 0.2659 - acc: 0.9569 - val_loss: 0.2473 - val_acc: 0.9538 Epoch 20/100 - 0s - loss: 0.2569 - acc: 0.9591 - val_loss: 0.2451 - val_acc: 0.9614 Epoch 21/100 - 0s - loss: 0.2464 - acc: 0.9614 - val_loss: 0.2402 - val_acc: 0.9625 Epoch 22/100 - 0s - loss: 0.2470 - acc: 0.9598 - val_loss: 0.2453 - val_acc: 0.9538 Epoch 23/100 - 0s - loss: 0.2498 - acc: 0.9601 - val_loss: 0.2408 - val_acc: 0.9538 Epoch 24/100 - 0s - loss: 0.2433 - acc: 0.9587 - val_loss: 0.2421 - val_acc: 0.9505 Epoch 25/100 - 0s - loss: 0.2406 - acc: 0.9613 - val_loss: 0.2307 - val_acc: 0.9538 Epoch 26/100 - 0s - loss: 0.2372 - acc: 0.9601 - val_loss: 0.2301 - val_acc: 0.9538 Epoch 27/100 - 0s - loss: 0.2294 - acc: 0.9615 - val_loss: 0.2287 - val_acc: 0.9598 Epoch 28/100 - 0s - loss: 0.2349 - acc: 0.9613 - val_loss: 0.2255 - val_acc: 0.9571 Epoch 29/100 - 0s - loss: 0.2326 - acc: 0.9579 - val_loss: 0.2206 - val_acc: 0.9554 Epoch 30/100 - 0s - loss: 0.2257 - acc: 0.9614 - val_loss: 0.2180 - val_acc: 0.9571 Epoch 31/100 - 0s - loss: 0.2258 - acc: 0.9618 - val_loss: 0.2200 - val_acc: 0.9609 Epoch 32/100 - 0s - loss: 0.2236 - acc: 0.9611 - val_loss: 0.2213 - val_acc: 0.9538 Epoch 33/100 - 0s - loss: 0.2201 - acc: 0.9622 - val_loss: 0.2112 - val_acc: 0.9587 Epoch 34/100 - 0s - loss: 0.2253 - acc: 0.9617 - val_loss: 0.2159 - val_acc: 0.9549 Epoch 35/100 - 0s - loss: 0.2207 - acc: 0.9629 - val_loss: 0.2114 - val_acc: 0.9598 Epoch 36/100 - 0s - loss: 0.2228 - acc: 0.9606 - val_loss: 0.2136 - val_acc: 0.9592 Epoch 37/100 - 0s - loss: 0.2163 - acc: 0.9617 - val_loss: 0.2098 - val_acc: 0.9620 Epoch 38/100 - 0s - loss: 0.2167 - acc: 0.9621 - val_loss: 0.2179 - val_acc: 0.9560 Epoch 39/100 - 0s - loss: 0.2137 - acc: 0.9611 - val_loss: 0.2120 - val_acc: 0.9576 Epoch 40/100 - 0s - loss: 0.2093 - acc: 0.9636 - val_loss: 0.2003 - val_acc: 0.9658 Epoch 41/100 - 0s - loss: 0.2155 - acc: 0.9621 - val_loss: 0.2016 - val_acc: 0.9625 Epoch 42/100 - 0s - loss: 0.2076 - acc: 0.9652 - val_loss: 0.1994 - val_acc: 0.9598 Epoch 43/100 - 0s - loss: 0.2128 - acc: 0.9626 - val_loss: 0.2053 - val_acc: 0.9587 Epoch 44/100 - 0s - loss: 0.2071 - acc: 0.9643 - val_loss: 0.1974 - val_acc: 0.9630 Epoch 45/100 - 0s - loss: 0.2078 - acc: 0.9637 - val_loss: 0.2047 - val_acc: 0.9592 Epoch 46/100 - 0s - loss: 0.2130 - acc: 0.9615 - val_loss: 0.2089 - val_acc: 0.9538 Epoch 47/100 - 0s - loss: 0.2113 - acc: 0.9617 - val_loss: 0.2007 - val_acc: 0.9582 Epoch 48/100 - 0s - loss: 0.2072 - acc: 0.9656 - val_loss: 0.2026 - val_acc: 0.9538 Epoch 49/100 - 0s - loss: 0.2055 - acc: 0.9636 - val_loss: 0.2013 - val_acc: 0.9565 Epoch 50/100 - 0s - loss: 0.2089 - acc: 0.9610 - val_loss: 0.1974 - val_acc: 0.9582 Epoch 51/100 - 0s - loss: 0.2033 - acc: 0.9632 - val_loss: 0.1946 - val_acc: 0.9587 Epoch 52/100 - 0s - loss: 0.2075 - acc: 0.9626 - val_loss: 0.1995 - val_acc: 0.9625 Epoch 53/100 - 0s - loss: 0.2030 - acc: 0.9635 - val_loss: 0.1948 - val_acc: 0.9603 Epoch 54/100 - 0s - loss: 0.2038 - acc: 0.9641 - val_loss: 0.1939 - val_acc: 0.9679 Epoch 55/100 - 0s - loss: 0.2048 - acc: 0.9636 - val_loss: 0.1950 - val_acc: 0.9592 Epoch 56/100 - 0s - loss: 0.2037 - acc: 0.9637 - val_loss: 0.1917 - val_acc: 0.9636 Epoch 57/100 - 0s - loss: 0.2014 - acc: 0.9647 - val_loss: 0.1909 - val_acc: 0.9620 Epoch 58/100 - 0s - loss: 0.1979 - acc: 0.9651 - val_loss: 0.1896 - val_acc: 0.9614 Epoch 59/100 - 0s - loss: 0.2068 - acc: 0.9629 - val_loss: 0.1909 - val_acc: 0.9609 Epoch 60/100 - 0s - loss: 0.1990 - acc: 0.9633 - val_loss: 0.1908 - val_acc: 0.9614 Epoch 61/100 - 0s - loss: 0.1921 - acc: 0.9666 - val_loss: 0.1904 - val_acc: 0.9620 Epoch 62/100 - 0s - loss: 0.2018 - acc: 0.9629 - val_loss: 0.1896 - val_acc: 0.9614 Epoch 63/100 - 0s - loss: 0.2041 - acc: 0.9620 - val_loss: 0.1917 - val_acc: 0.9625 Epoch 64/100 - 0s - loss: 0.2000 - acc: 0.9652 - val_loss: 0.1891 - val_acc: 0.9620 Epoch 65/100 - 0s - loss: 0.1967 - acc: 0.9656 - val_loss: 0.1916 - val_acc: 0.9609 Epoch 66/100 - 0s - loss: 0.1961 - acc: 0.9639 - val_loss: 0.1854 - val_acc: 0.9641 Epoch 67/100 - 0s - loss: 0.1969 - acc: 0.9648 - val_loss: 0.1887 - val_acc: 0.9592 Epoch 68/100 - 0s - loss: 0.1990 - acc: 0.9630 - val_loss: 0.1874 - val_acc: 0.9636 Epoch 69/100 - 0s - loss: 0.1923 - acc: 0.9662 - val_loss: 0.1893 - val_acc: 0.9614 Epoch 70/100 - 0s - loss: 0.1925 - acc: 0.9645 - val_loss: 0.1853 - val_acc: 0.9641 Epoch 71/100 - 0s - loss: 0.1948 - acc: 0.9622 - val_loss: 0.1905 - val_acc: 0.9592 Epoch 72/100 - 0s - loss: 0.1994 - acc: 0.9628 - val_loss: 0.1852 - val_acc: 0.9641 Epoch 73/100 - 0s - loss: 0.1953 - acc: 0.9651 - val_loss: 0.1834 - val_acc: 0.9641 Epoch 74/100 - 0s - loss: 0.1888 - acc: 0.9670 - val_loss: 0.1816 - val_acc: 0.9620 Epoch 75/100 - 0s - loss: 0.1933 - acc: 0.9659 - val_loss: 0.1860 - val_acc: 0.9620 Epoch 76/100 - 0s - loss: 0.1917 - acc: 0.9635 - val_loss: 0.1828 - val_acc: 0.9625 Epoch 77/100 - 0s - loss: 0.1907 - acc: 0.9677 - val_loss: 0.1828 - val_acc: 0.9603 Epoch 78/100 - 0s - loss: 0.1990 - acc: 0.9637 - val_loss: 0.1805 - val_acc: 0.9652 Epoch 79/100 - 0s - loss: 0.1934 - acc: 0.9652 - val_loss: 0.1864 - val_acc: 0.9614 Epoch 80/100 - 0s - loss: 0.1870 - acc: 0.9667 - val_loss: 0.1808 - val_acc: 0.9674 Epoch 81/100 - 0s - loss: 0.1901 - acc: 0.9660 - val_loss: 0.1825 - val_acc: 0.9625 Epoch 82/100 - 0s - loss: 0.1880 - acc: 0.9649 - val_loss: 0.1871 - val_acc: 0.9663 Epoch 83/100 - 0s - loss: 0.1901 - acc: 0.9677 - val_loss: 0.1808 - val_acc: 0.9620 Epoch 84/100 - 0s - loss: 0.1941 - acc: 0.9620 - val_loss: 0.1853 - val_acc: 0.9647 Epoch 85/100 - 0s - loss: 0.1867 - acc: 0.9674 - val_loss: 0.1825 - val_acc: 0.9620 Epoch 86/100 - 0s - loss: 0.1940 - acc: 0.9651 - val_loss: 0.1877 - val_acc: 0.9576 Epoch 87/100 - 0s - loss: 0.1913 - acc: 0.9633 - val_loss: 0.1817 - val_acc: 0.9620 Epoch 88/100 - 0s - loss: 0.1940 - acc: 0.9649 - val_loss: 0.1834 - val_acc: 0.9636 Epoch 89/100 - 0s - loss: 0.1886 - acc: 0.9656 - val_loss: 0.1844 - val_acc: 0.9625 Epoch 90/100 - 0s - loss: 0.1835 - acc: 0.9677 - val_loss: 0.1899 - val_acc: 0.9641 Epoch 91/100 - 0s - loss: 0.1884 - acc: 0.9674 - val_loss: 0.1894 - val_acc: 0.9587 Epoch 92/100 - 0s - loss: 0.1855 - acc: 0.9675 - val_loss: 0.1894 - val_acc: 0.9582 Epoch 93/100 - 0s - loss: 0.1864 - acc: 0.9655 - val_loss: 0.1808 - val_acc: 0.9641 Epoch 94/100 - 0s - loss: 0.1878 - acc: 0.9671 - val_loss: 0.1865 - val_acc: 0.9609 Epoch 95/100 - 0s - loss: 0.1901 - acc: 0.9662 - val_loss: 0.1859 - val_acc: 0.9641 Epoch 96/100 - 0s - loss: 0.1836 - acc: 0.9670 - val_loss: 0.1823 - val_acc: 0.9647 Epoch 97/100 - 0s - loss: 0.1876 - acc: 0.9664 - val_loss: 0.1799 - val_acc: 0.9668 Epoch 98/100 - 0s - loss: 0.1854 - acc: 0.9675 - val_loss: 0.1912 - val_acc: 0.9565 Epoch 99/100 - 0s - loss: 0.1881 - acc: 0.9673 - val_loss: 0.1801 - val_acc: 0.9668 Epoch 100/100 - 0s - loss: 0.1821 - acc: 0.9674 - val_loss: 0.1758 - val_acc: 0.9701
Contents and codes are arranged in:
[1] : Study on EEG synchronous analysis and epileptic seizure prediction method
[2]: http://dy.163.com/v2/article/detail/EEC68EH5054281P3.html
Reference resources
Classification of epilepsy with keras - Python case
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