A N2CNN-Based Anomaly Detection Method for Cardiovascular Data in Cyber-Physical System

作     者：Pal, Raju Tripathi, Ashish Kumar Pandey, Avinash Chandra Khan, Mohammad Ayoub Menon, Varun G. Mittal, Himanshu 

作者机构：JIIT Dept Comp Sci & Engn & Informat Technol Noida 201309 India Malaviya Natl Inst Technol Jaipur Dept Comp Sci & Engn Jaipur 302017 India IIITDM Dept Comp Sci Engn Jabalpur 482005 India Univ Bisha Coll Comp & Informat Technol Bisha 67714 Saudi Arabia SCMS Sch Engn & Technol Dept Comp Sci & Engn Ernakulam 682024 India 

出 版 物：《IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING》 (IEEE Trans. Netw. Sci. Eng.)

年 卷 期：2023年第10卷第5期

页      面：2617-2626页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0701[理学-数学] 

主　　题：Cyber-physical system remote health monitor-ing convolutional neural network elitist non-dominated sorting genetic algorithm anomaly detection 

摘      要：Internet-of-medical-things is the new means of monitoring patient health remotely. However, the real-time detection of anomalies in the patient data is a challenging task, especially on ECG-data. To ease the same, a novel method, NSGA-II based convolution neural network, is presented in this paper for efficient anomaly detection. In the proposed method, non-dominated sorting genetic algorithm-II is emto obtain optimal hyper-parameters of CNN by evaluating three objective functions namely, accuracy, precision, and recall. Further, the performance validation of the proposed method is conducted on two public datasets and compared against seven state-of-the-art methods. Experimental results affirm that the proposed method outperforms the considered methods with an accuracy of 94.83% and 94.96% on MIT-BIH arrhythmia dataset and INCART dataset, respectively. Therefore, it can be claimed that the proposed method is an efficient alternative for anomaly detection.