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Long-term electroencephalography (EEG) rec- ordings contain multiple types of artifacts (eye blink artifacts, chewing artifacts, electromyography (EMG) artifacts, etc.), that severely interfere with the EEG reading by clinicians and researchers. Existing research often focuses on specific artifact detection, which cannot handle the complex multitype artifact detection. Therefore, a multiple artifact detection method based on adaptive scalp region selection and classifier fusion is proposed in this article. The main contributions of the article are as follows: 1) according to the characteristics of different scalp area generated by artifacts, adaptive brain region screening is realized. 2) ReliefF and mRMR are fused to realize two-stage feature evaluation and reduce the feature redundancy. 3) Multimodel cascade fusion can bring stronger robustness and better generalization. The data used in the study are collected from real patients suffering from benign epilepsy with centro-temporal spikes (BECT) at the Children’s Hospital of Zhejiang University School of Medicine (CHZU). Compared with the state-of-the-art methods, the proposed method shows the best performance in the detection of multitype artifacts, and achieves the F1-score of 87%, precision of 87%, recall of 88%, and accuracy of 93% on the CHZU dataset.

Simultaneous childhood epilepsy syndrome classification (ESC) and seizure detection (SD) are both significant in epilepsy analysis. Current research mainly focuses on a single task, mostly on SD. In this article, a novel dual-stream multitask network (DSMN) exploiting multichannel scalp electroencephalograms (EEGs) is developed to simultaneously perform ESC-Task and SD-Task, in short as DSMN-ESS. The close correlation between ESC-Task and SD-Task is explored to achieve better performance. To improve the performance, an information-sharing gate module is designed in DSMN to enable both tasks to fully obtain useful information. Meanwhile, a channel weight update module is developed to well-extract the internal spatial relationship between multichannel EEGs. Furthermore, an area-under-the-curve (AUC)-based loss is proposed to address the data imbalance issue in epilepsy analysis. Studies on EEG data recorded 49 patients from the Children’s Hospital, Zhejiang University School of Medicine (CHZU), are carried out to show the effectiveness of DSMN-ESS. The results show that DSMN-ESS can achieve the highest AUC, 99.95% and 99.78% in ESC-Task and SD-Task, respectively, which are superior to several state-of-the-art (SOTA) methods.