An efficient method for Secure ECG Feature Based Cryptographic Key Generation
1Premkumar, Assistant Professor, ECE, Saveetha School of Engineering, SIMATS, Chennai, India.
2J.Mohan, Associate Professor, ECE, Saveetha School of Engineering, SIMATS, Chennai, India.
Manuscript received on September 15, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 159-164 | Volume-8 Issue-12, October 2019. | Retrieval Number: L35061081219/2019©BEIESP | DOI: 10.35940/ijitee.L3506.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A novel method to generate ECG feature oriented cryptographic keys is proposed. Due to the advantage of the uniqueness and randomness properties of ECG’s main feature, this feature is achieved. As the production of key depends on four reference- free ECG main features, Low-latency property is obtained. These features are obtained in short time. This process is referred as (SEF)-based cryptographic key production. The SEF has the following features like: 1) identifying the appearance time of ECG’s fiducial values by means of Daubechies wavelet transform to calculate ECG’s main features conversely; 2) A dynamic method is used to denote the best quantity of bits that can be obtained from the main ECG feature, which consists of PR, RR, PP, QT, and ST time periods; 3) Generating cryptographic keys by the ECG features extracted in the method mentioned above and 4) Making the SEF method as strong with cryptographically secure pseudo-random number generators. Fibonacci linear feedback shift register and recent encryption traditional algorithms are executed as the pseudorandom number generator to improve the safety stage of the produced cryptographic keys. This method is executed to 239 subjects’ ECG signals consisting of normal sinus rhythm, arrhythmia, atrial brillation, and myocardial infraction. Normal ECG rhythms have slightly better randomness when compare with the abnormal.The output results proves that the SEF method is faster than the present existing key production methods. It produces higher security level when compared to existing methods.
Keywords: Cryptographic Key Generation, Electrocardiogram, Bio-electrical Signal, Body Area Network
Scope of the Article: Area Networks