Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images

Abstract

We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detection could benefit from convolutional neural networks because their visual recognition ability is comparable to that of humans. We explored image-based seizure detection by applying convolutional neural networks to long-term EEG that included epileptic seizures. After filtering, EEG data were divided into short segments based on a given time window and converted into plot EEG images, each of which was classified by convolutional neural networks as ‘seizure’ or ‘non-seizure’. These resultant labels were then used to design a clinically practical index for seizure detection. The best true positive rate was obtained using a 1-s time window. The median true positive rate of convolutional neural networks labelling by seconds was 74%, which was higher than that of commercially available seizure detection software (20% by BESA and 31% by Persyst). For practical use, the median of detected seizure rate by minutes was 100% by convolutional neural networks, which was higher than the 73.3% by BESA and 81.7% by Persyst. The false alarm of convolutional neural networks' seizure detection was issued at 0.2 per hour, which appears acceptable for clinical practice. Moreover, we demonstrated that seizure detection improved when training was performed using EEG patterns similar to those of testing data, suggesting that adding a variety of seizure patterns to the training dataset will improve our method. Thus, artificial visual recognition by convolutional neural networks allows for seizure detection, which otherwise currently relies on skillful visual inspection by expert epileptologists during clinical diagnosis.

Artificial Intelligence and Computational Approaches for Epilepsy

“…Regarding the seizure detection task, many studies have shown that the models trained via traditional machine learning algorithms, 55 – 60 especially SVM and k-NN, deep learning algorithms 61 – 65 using specific features in time and/or frequency domain based on scalp EEG, or iEEG recordings can successfully detect seizures with multiple types. In particular, Emami and colleagues have proposed a CNN-based seizure detection model.…”

“…In particular, Emami and colleagues have proposed a CNN-based seizure detection model. 61 The model learned the EEG (scalp EEG) characteristics in seizure state and non-seizure state automatically, without additional manual feature extraction procedures, through the supervised learning framework, and was able to detect seizure onset at an average positive rate of 74% when the entire time series EEG was sequentially input by 1 second (100% for input by 1 minute). They also have demonstrated that performance of seizure detection depends on the similarity of seizure onset pattern between training data and test data, i.e., new data with a different onset pattern that those of trained data could not be detected well.…”

Automatische Erkennung von epilepsietypischen Potenzialen und Anfällen im EEG

“…Die Klassifikationskriterien reichen von einfachen Schwellwertverfahren bis zu komplexen Machine-Learning-Methoden wie Support-Vektor-Maschinen, Random-Forest-Verfahren (Entscheidungsbäume) und künstlichen neuronalen Netzwerken [34,37]. Zuletzt kamen auch Deep-Learning-Algorithmen zur Anwendung [38,39]. Die Algorithmen werden zunächst an einem Trainingsdatensatz trainiert und dann an einem unabhängigen prospektiven Datensatz validiert [30,37].…”

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

“…A majority voting strategy has been adopted to fuse the decisions of the ensemble of CNNs, reaching an average accuracy of 96.1%. In [27], the authors exploited the attitude of the CNNs to process images, rather than 0-D temporal sequences. For this purpose, the raw EEG multichannels data have been firstly filtered, then segmented using a prefixed time window, and finally converted into a series of EEG-plot images.…”