Automated Detection of High Frequency Oscillations in Intracranial EEG Using the Combination of Short-Time Energy and Convolutional Neural Networks

Abstract

High-frequency oscillations (HFOs) of 80∼500 Hz in the intracranial electroencephalogram (iEEG) recordings are considered as a reliable marker for epileptic location. However, a significant challenge to the clinical use of HFOs is due to the time-consuming procedure of visually identifying them. A new methodology is presented in this paper for the automated detection of HFOs based on their 2D time-frequency map employing the short-time energy (STE) estimation and the convolutional neural network (CNN) classification algorithm. The effectiveness and usefulness of the proposed method are evaluated using the clinical iEEG data acquired from five patients (28.4 ± 13.0 years) with medically intractable epilepsy. The proposed methodology presents the following significant advantages: 1) compared with the recently reported HFOs detector based on the CNN using only the 1D temporal EEG signal, the proposed method achieves a higher accuracy using the deep CNN classifier on 2D time-frequency map of HFOs, of which the evaluated sensitivity and false discovery rate (FDR) for identifying ripples are 88.16% and 12.58%, respectively, and the corresponding sensitivity and FDR are 93.37% and 8.1% for detecting fast ripples, respectively; 2) it is capable of automatically extracting the shared features of HFOs events of different patients and would be much robust, unlike other automated methodologies proposed in the literature where the characteristics of HFOs were extracted manually on the basis of researchers' knowledge, which, probably, is prone to observer bias; and 3) with the proposed STE estimation, all suspicious ripples and fast ripples could be initially found out and transformed into time-frequency map for subsequently CNN-based classification, rather than transforming and classifying the raw data, thus requiring a lower computational resource. In addition, the time occurrence of each transient event of the HFOs can be identified to be potentially useful for further seizure analysis. In conclusion, this automated detection of the HFOs combing the STE and the CNN could allow analyzing large amounts of data in a short time while assuring a relatively higher accuracy and, thus, would potentially serve to provide a clinically useful tool.INDEX TERMS Convolutional neural networks (CNNs), epilepsy, high frequency oscillations (HFOs), intracranial electroencephalograms (iEEG), short time energy (STE).

Double-Step Machine Learning Based Procedure for HFOs Detection and Classification

“…We used only energy features, because they do not need information regarding the whole duration of the signal [29] and have low computational cost [38] in view of a future real-time application. Moreover, the energy computation in 5 and 10 ms windows has been already validated by [39,40].…”

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

“…Zuo et al 33 proposed the CNN-based method for identifying the two kind of HFOs in ripple and fast-ripple separately and achieved average results with sensitivity (77.04% and 83.23% for ripples) and specificity (72.27% and 79.36% for fast ripples) compared to four traditional automated methods proposed in the RIPPLELAB toolbox 32 . The combination of short-time energy (STE) and CNN also used in recent study for identifying HFOs 60 . In their study, the performance of the system in terms of sensitivity and FDR are used to evaluate their system and compared with three related existing studies 32,36,57 .…”

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

“…Zuo et al 33 proposed the CNN-based method for identifying the two kind of HFOs in ripple and fast-ripple separately and achieved average results with sensitivity (77.04% and 83.23% for ripples) and specificity (72.27% and 79.36% for fast ripples) compared to four traditional automated methods proposed in the RIPPLELAB toolbox 32 . The combination of short-time energy (STE) and CNN also used in recent study for identifying HFOs 62 . In their study, the performance of the system in terms of sensitivity and FDR are used to evaluate their system and compared with three related existing studies 32,36,59 .…”