Key result
An intrusion cancellation approach evaluated using Matlab provided promising accurate detection of false alert rates for medical anomaly detection based on ECG and EMG analysis.
Why the study?
Wireless Body Sensor Networks remain vulnerable to multiple attacks, necessitating intrusion detection to help physicians make accurate medical decisions.
Does an intrusion cancellation scheme improve the accuracy of medical anomaly detection based on ECG and EMG analysis in WBSNs?
Does an intrusion cancellation scheme improve the accuracy of medical anomaly detection based on ECG and EMG analysis in WBSNs?
An intrusion cancellation scheme for Wireless Body Sensor Networks may improve the accuracy of medical anomaly detection based on ECG and EMG signals.
Lately, there has been a lot of interest in how to ensure secure communication in Wireless Body Sensor Networks (WBSN). To accomplish this finish, applied researchers have become increasingly identified their own detection techniques. However, WBSN still vulnerable for many attacks, for instance: Jamming, Sybil attacks, Sinkhole, Denial of service (DOS), etc. Therefore, intrusion detection is officially compulsory process that helps physicians to get accurate medical decisions. The present work proposes an intrusion cancellation to add efficiency to medical anomaly detection based on Electrocardiogram (ECG) and Electromyogram (EMG) analysis. The efficiency and accuracy of the proposed approach are evaluated using the Matlab tool. Hence, our scheme may be considered as a promising accurate detection of false alert rates.
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Mohamed et al. (2019) studied Medical anomaly detection. Intrusion cancellation approach was evaluated on Efficiency and accuracy of anomaly detection. An intrusion cancellation approach evaluated using Matlab provided promising accurate detection of false alert rates for medical anomaly detection based on ECG and EMG analysis.
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