A new feature detection mechanism and its application in secured ECG transmission with noise masking.
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 would appear as regular ECG without encryption. Therefore, traditional decryption techniques including powerful brute force attack are useless against this obfuscation.
Sufi
Fahim
F
Science, Engineering and Technology Portfolio, RMIT University, Melbourne 3000, Australia. research@fahimsufi.com
Khalil
Ibrahim
I
D000465:Algorithms; D016494:Computer Security / Q000592:standards*; D003219:Confidentiality; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004562:Electrocardiography / Q000295:instrumentation* / Q000379:methods*; D006801:Humans; D020407:Internet; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted