Wide Machine Learning Algorithms Evaluation Applied to ECG Authentication and Gender Recognition

ECG signals have been widely studied for knowing heart behavior and following cardiac abnormalities. Last years have emerged new applications where ECG has being used in cryptography and biometrics. The purpose in this paper center around perform two independent experiments taking advantage of the ECG properties. The first experiment is about person authentication and the second experiment covers gender recognition. Both tests are performed extracting the same features and evaluating the classification accuracy with several machine learning algorithms sensing the ECG signals in different body positions. ECG signal contains properties like liveness detection, ubiquity, diffculty of being copied, continuity, and reclaims the mandatory user presence. These properties makes ECG study having the potential of being embedded for smartphone applications in the Internet of Things era. The best accuracy score is over the 98% for ECG authentication and 94% for gender recognition; as the best of our knowledge there is no ECG gender recognition with the algorithms studied in this paper.

[1]  Joseph A. O'Sullivan,et al.  ECG biometrics: A robust short-time frequency analysis , 2010, 2010 IEEE International Workshop on Information Forensics and Security.

[2]  Sarineh Keshishzadeh,et al.  Single lead Electrocardiogram feature extraction for the human verification , 2015, 2015 5th International Conference on Computer and Knowledge Engineering (ICCKE).

[3]  Ibrahim Khalil,et al.  Data mining in mobile ECG based biometric identification , 2014, J. Netw. Comput. Appl..

[4]  Ning Zhang,et al.  A survey on touch dynamics authentication in mobile devices , 2016, Comput. Secur..

[5]  Daniela Moctezuma,et al.  Features combination for gender recognition on Twitter users , 2016, 2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC).

[6]  Margit Antal,et al.  Gender recognition from mobile biometric data , 2016, 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI).

[7]  Ke Wu,et al.  Automatic recognition of gender by voice , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[8]  L. Biel,et al.  ECG analysis: a new approach in human identification , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[9]  Lei Zhang,et al.  Learning a lightweight deep convolutional network for joint age and gender recognition , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[10]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[11]  A. Uchiyama,et al.  Development of an ECG identification system , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Ana L. N. Fred,et al.  Check Your Biosignals Here: A new dataset for off-the-person ECG biometrics , 2014, Comput. Methods Programs Biomed..

[13]  J. Manyika,et al.  Disruptive technologies: Advances that will transform life, business, and the global economy , 2013 .

[14]  Juan E. Tapiador,et al.  Human Identification Using Compressed ECG Signals , 2015, Journal of Medical Systems.

[15]  Arturo José Méndez Penín,et al.  A comparison of three QRS detection algorithms over a public database , 2013 .

[16]  G. Bortolan,et al.  Personal Verification/Identification via Analysis of the Peripheral ECG Leads: Influence of the Personal Health Status on the Accuracy , 2015, BioMed research international.

[17]  Yun Fu,et al.  Gender recognition from body , 2008, ACM Multimedia.

[18]  Heart attack symptoms in women. , 2012, The Journal of the Oklahoma State Medical Association.

[19]  Abdulmotaleb El-Saddik,et al.  ECG Authentication for Mobile Devices , 2016, IEEE Transactions on Instrumentation and Measurement.

[20]  Dhouha Rezgui Non-member,et al.  ECG biometric recognition using SVM-based approach , 2016 .

[21]  J. Koseeyaporn,et al.  Human Identification System Based ECG Signal , 2005, TENCON 2005 - 2005 IEEE Region 10 Conference.

[22]  Semih Ergin,et al.  ECG based biometric authentication using ensemble of features , 2014, 2014 9th Iberian Conference on Information Systems and Technologies (CISTI).

[23]  Maurizio Talamo,et al.  Movement based biometric authentication with smartphones , 2015, 2015 International Carnahan Conference on Security Technology (ICCST).

[24]  Vivek Kanhangad,et al.  Investigating gender recognition in smartphones using accelerometer and gyroscope sensor readings , 2016, 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).

[25]  Khairul Azami Sidek,et al.  Development of an Electrocardiogram Based Biometric Identification System: A Case Study in the University , 2016 .