A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy

Abstract

High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone, and with poor inter-reviewer reliability, an automatic HFOs detector is highly desirable in clinical practice. However, the existing approaches for HFOs detection may not be applicable for MEG signals with noisy background activity. Therefore, we employ the stacked sparse autoencoder (SSAE) and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first attempt to conduct HFOs detection in MEG using deep learning methods. After configuration optimization, our proposed SMO detector is outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. Furthermore, we have tested the performance consistency of our model using various validation schemes. The distribution of performance metrics demonstrates that our model can achieve steady performance.

Figures

Fig. 1:

Overview of SMO detector working as a CAD tool for HFOs detection in clinical practice

Fig. 2:

Examples of gold standard signals: (A) HFOs and (B) Normal signals. Original (first row) and down sample factor 10 (second row) signals for the training of our SMO detector are shown

Fig. 3:

Stacking of a SSAE with two AEs

Fig. 4:

Learning curves for the weights between input layer and first hidden layer. Different number of hidden nodes were tested separately using all gold-standard data

Fig. 5:

Performance (Accuracy, Sensitivity and Specificity) of SMO detector with various architectures. The rows are number of hidden nodes in each layer, and the columns are number of hidden layers. Best performance are marked with black boxes

Fig. 6:

Comparison of classification performance among three methods: logistic regression (LR), support vector machine (SVM) and SMO (SSAE). Mean and standard derivation of accuracy, sensitivity and specificity over 50 repeated classification experiments are shown

Fig. 7:

Boxplots of the performance metrics of the SMO detector with different repeated k-fold random subsampling validation. The box marks the first and third quartiles of data. The median value of the metrics is represented as a red band inside the box. The ends of the whiskers represent the data points within (2.7Standard derivation). The outliers are represented by ‘+’ markers