A Continuous Change Detection Mechanism to Identify Anomalies in ECG Signals for WBAN-based Healthcare Environments

Farrukh Aslam Khan, Nur Al Hasan Haldar, Aftab Ali, Mohsin Iftikhar, Tanveer Zia, Albert Y. Zomaya

Research output: Contribution to journalArticle

13 Citations (Scopus)
21 Downloads (Pure)

Abstract

The developments and applications of wireless body area networks (WBANs) for healthcare and remote monitoring have brought a revolution in the medical research field. Numerous physiological sensors are integrated in a WBAN architecture in order to monitor any significant changes in normal health conditions. This monitored data are then wirelessly transferred to a centralized personal server (PS). However, this transferred information can be captured and altered by an adversary during communication between the physiological sensors and the PS. Another scenario where changes can occur in the physiological data is an emergency situation, when there is a sudden change in the physiological values, e.g., changes occur in electrocardiogram (ECG) values just before the occurrence of a heart attack. This paper presents a centralized approach for the detection of abnormalities, as well as intrusions, such as forgery, insertions, and modifications in the ECG data. A simplified Markov model-based detection mechanism is used to detect changes in the ECG data. The features are extracted from the ECG data to form a feature set, which is then divided into sequences. The probability of each sequence is calculated, and based on this probability, the system decides whether the change has occurred or not. Our experiments and analyses show that the proposed scheme has a high detection rate for 5% as well as 10% abnormalities in the data set. The proposed scheme also has a higher true negative rate with a significantly reduced running time for both 5% and 10% abnormalities. Similarly, the receiver operating characteristic (ROC) and ROC convex hull have very promising results.
Original languageEnglish
Pages (from-to)13531-13544
Number of pages13
JournalIEEE Access
Volume5
Publication statusPublished - 09 Jun 2017

    Fingerprint

Cite this