SeizSClas: An Efficient and Secure Internet-of-Things-Based EEG Classifier

【Author】 Singh, Rishav; Ahmed, Tanveer; Singh, Amit Kumar; Chanak, Prasenjit; Singh, Sanjay Kumar

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】The Internet of Things (IoT) is one of the fastest growing areas of research. Considering the IoT and healthcare simultaneously, classifying brain signals using smart IoT sensors is one of the standing nontrivial problems of literature. The issue is further exacerbated by noise in brain signals, and there is no efficient solution for classifying brain signals as seizorous or nonseizorous, yet. Moreover, research has mostly ignored the security and privacy aspect of this problem. Therefore, in this article, we try to bridge this gap and present a secure privacy-preserving technique for brain signal classification. We first transform a brain signal into an image. Subsequently, we apply transfer learning to solve the classification problem. To do that, we use the pretrained VGG-19 as a base model. In addition, we discuss a scheme to store images in a blockchain so as to make the overall architecture privacy aware. By conducting comprehensive numerical simulations on a supercomputer and using the famous TUH Abnormal EEG data set, we show the efficacy of the proposed work. The work presented here not only makes the storage of patient data secure and private but also outperforms all existing techniques in terms of classification accuracy.

【Keywords】Blockchain; Internet of Things; Electroencephalography; Privacy; Brain modeling; Sensors; Security; Blockchain; deep learning; Internet of Things (IoT); security; transfer learning

【发表时间】2021 44666

【收录时间】2022-01-02

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