One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG
Wang, X., Wang, X., Liu, W., Chang, Z., Kärkkäinen, T., & Cong, F. (2021). One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG. Neurocomputing, 459, 212-222. https://doi.org/10.1016/j.neucom.2021.06.048
© 2021 the Authors
Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-sec sliding windows. Then, the 2-sec interictal and ictal segments were classified by the stacked 1D-CNN model. During model training, a RS-DA strategy was applied to solve the problem of sample imbalance, and the patient-specific model was trained with event-based K-fold (K is the number of seizures per patient) cross validation for detecting all seizures of each patient. Finally, we evaluated the performances of the proposed approach in the two levels: the segment-based level and the event-based level. The proposed method was tested on two long-term EEG datasets: the CHB-MIT sEEG dataset and the SWEC-ETHZ iEEG dataset. For the CHB-MIT sEEG dataset, we achieved 88.14 sensitivity, 99.62 specificity and 99.54 accuracy in the segment-based level. From the perspective of the event-based level, 99.31 sensitivity, 0.2/h false detection rate (FDR) and mean 8.1-sec latency were achieved. For the SWEC-ETHZ iEEG dataset, in the segment-based level, 90.09 sensitivity, 99.81 specificity and 99.73 accuracy were obtained. In the event-based level, 97.52 sensitivity, 0.07/h FDR and mean 13.2-sec latency were attained. From these results, we can see that our method can effectively use both sEEG and iEEG data to detect epileptic seizures, and this may provide a reference for the clinical application of seizure onset detection. ...
Publication in research information system
MetadataShow full item record
Additional information about fundingThis work was supported by National Natural Science Foundation of China (Grant No. 91748105), National Foundation in China (No. JCKY2019110B009 & 2020-JCJQ-JJ-252), the scholarship from China Scholarship Council (No. 201806060166) and the Fundamental Research Funds for the Central Universities [DUT20LAB303 & DUT20LAB308] in Dalian University of Technology in China.
Showing items with similar title or keywords.
Terziyan, Vagan; Malyk, Diana; Golovianko, Mariia; Branytskyi, Vladyslav (Elsevier, 2022)Convolutional Neural Network is one of the famous members of the deep learning family of neural network architectures, which is used for many purposes, including image classification. In spite of the wide adoption, such ...
Li, Fan; Tang, Hong; Shang, Shang; Mathiak, Klaus; Cong, Fengyu (MDPI, 2020)Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, ...
Pölönen, Ilkka; Annala, Leevi; Rahkonen, Samuli; Nevalainen, Olli; Honkavaara, Eija; Tuominen, Sakari; Viljanen, Niko; Hakala, Teemu (IEEE, 2019)In this study we apply 3D convolutional neural network (CNN) for tree species identification. Study includes the three most common Finnish tree species. Study uses a relatively large high-resolution spectral data set, ...
Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks Nezami, Somayeh; Khoramshahi, Ehsan; Nevalainen, Olli; Pölönen, Ilkka; Honkavaara, Eija (MDPI AG, 2020)Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include ...
Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks Hakala, Taina; Pölönen, Ilkka; Honkavaara, Eija; Näsi, Roope; Hakala, Teemu; Lindfors, Antti (Springer, 2020)In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. ...