NDLI: A New Feature Detection Mechanism and Its Application in Secured ECG Transmission with Noise Masking

Content Provider	Springer Nature Link
Author	Sufi, Fahim Khalil, Ibrahim
Copyright Year	2008
Abstract	With cardiovascular disease as the number one killer of modern era, Electrocardiogram (ECG) is collected, stored and transmitted in greater frequency than ever before. However, in reality, ECG is rarely transmitted and stored in a secured manner. Recent research shows that eavesdropper can reveal the identity and cardiovascular condition from an intercepted ECG. Therefore, ECG data must be anonymized before transmission over the network and also stored as such in medical repositories. To achieve this, first of all, this paper presents a new ECG feature detection mechanism, which was compared against existing cross correlation (CC) based template matching algorithms. Two types of CC methods were used for comparison. Compared to the CC based approaches, which had 40% and 53% misclassification rates, the proposed detection algorithm did not perform any single misclassification. Secondly, a new ECG obfuscation method was designed and implemented on 15 subjects using added noises corresponding to each of the ECG features. This obfuscated ECG can be freely distributed over the internet without the necessity of encryption, since the original features needed to identify personal information of the patient remain concealed. Only authorized personnel possessing a secret key will be able to reconstruct the original ECG from the obfuscated ECG. Distribution of the key is extremely efficient and fast due to small size (only 0.04–0.09% of the original ECG file). Moreover, if the obfuscated ECG reaches to the wrong hand (hacker), it would appear as regular ECG without encryption. Therefore, traditional decryption techniques including powerful brute force attack are useless against this obfuscation.
Starting Page	121
Ending Page	132
Page Count	12
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	33
Issue Number	2
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2008-06-21
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	ECG obfuscation ECG feature detection HIPAA Securing ECG data ECG encryption Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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