Deep Arm/Ear-ECG Image Learning for Highly Wearable Biometric Human Identification

Berry, M. J., and G. Linoff. Data mining techniques: for marketing, sales, and customer support. Hoboken: Wiley, 1997.

Bojarski M., D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, and J. Zhang. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 , 2016

Coutinho, D. P., H. Silva, H. Gamboa, A. Fred, and M. Figueiredo. Novel fiducial and non-fiducial approaches to electrocardiogram-based biometric systems. IET Biom. 2:64–75, 2013.

Fernández-Alemán, J. L., I. C. Señor, P. Á. O. Lozoya, and A. Toval. Security and privacy in electronic health records: a systematic literature review. J. Biomed. Inform. 46:541–562, 2013.

Gravina, R., P. Alinia, H. Ghasemzadeh, and G. Fortino. Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Inf. Fusion 35:68–80, 2017.

Heart function, Sinus rhythm & Common cardiac arrhythmias. http://www.nottingham.ac.uk/nursing/practice/resources/cardiology/function/normal_duration.php .

Hejazi M., S. Al-Haddad, S. J. Hashim, A. F. A. Aziz, and Y. P. Singh. Feature level fusion for biometric verification with two-lead ECG signals. In: Signal processing & its applications (CSPA), 2016 IEEE 12th International Colloquium on IEEE, 2016, p. 54–59.

Israel, S. A., J. M. Irvine, A. Cheng, M. D. Wiederhold, and B. K. Wiederhold. ECG to identify individuals. Pattern Recognit. 38:133–142, 2005.

Kennel, M. B., R. Brown, and H. D. Abarbanel. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45:3403, 1992.

Keras: Deep learning library for theano and tensorFlow. https://keras.io/

LeCun, Y., Y. Bengio, and G. Hinton. Deep learning. Nature 521:436–444, 2015.

Lourenço, A., H. Silva, and A. Fred. Unveiling the biometric potential of finger-based ECG signals. Comput. Intell. Neurosci. 2011:5, 2011.

Montano, N., A. Porta, C. Cogliati, G. Costantino, E. Tobaldini, K. R. Casali, and F. Iellamo. Heart rate variability explored in the frequency domain: a tool to investigate the link between heart and behavior. Neurosci. Biobehav. Rev. 33:71–80, 2009.

Nurminen, H., T. Ardeshiri, R. Piche, and F. Gustafsson. Robust inference for state-space models with skewed measurement noise. IEEE Signal Process. Lett. 22:1898–1902, 2015.

Nvidia. CUDA parallel computing platform

Riera A., S. Dunne, I. Cester, and G. Ruffini. STARFAST: a wireless wearable EEG/ECG biometric system based on the ENOBIO sensor. In: Proceedings of the International Workshop on Wearable Micro and Nanosystems for Personalised Health, 2008

Semmlow, J. L., and B. Griffel. Biosignal and Medical Image Processing. Boca Raton: CRC Press, 2014.

Shen, T.-W. D., W. J. Tompkins, and Y. H. Hu. Implementation of a one-lead ECG human identification system on a normal population. J. Eng. Comput. Innov. 2:12–21, 2010.

Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15:1929–1958, 2014.

Tang, X., and L. Shu. Classification of electrocardiogram signals with RS and quantum neural networks. Int. J. Multimed. Ubiquitous Eng. 9:363–372, 2014.

Ting C.-M., and S.-H. Salleh. ECG based personal identification using extended Kalman filter. In: 10th International Conference on Information Science, Signal Processing and their Applications, 2010, pp. 774–777

Wichard J. D., M. J. Ogorzałek, and C. Merkwirth. CNN in drug design—recent developments. In: Circuits and Systems (ISCAS), 2015 IEEE International Symposium on IEEE, 2015, pp. 405–408

Yan, J., and L. Lu. Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis. Signal Process. 98:74–87, 2014.

Yao J., and Y. Wan. A wavelet method for biometric identification using wearable ECG sensors. In: Medical Devices and Biosensors, 2008. ISSS-MDBS 2008. 5th International Summer School and Symposium on IEEE, 2008, pp. 297–300

Zhang Q., C. Zahed, V. Nathan, D. A. Hall, and R. Jafari. An ECG dataset representing real-world signal characteristics for wearable computers. In: Biomedical Circuits and Systems Conference (BioCAS), IEEE, 2015, pp. 1–4

Zhang Q., D. Zhou, and X. Zeng. A novel framework for motion-tolerant instantaneous heart rate estimation by phase-domain multi-view dynamic time warping. In: IEEE Transactions on Biomedical Engineering Preprint, 2016

Zhang, Q., D. Zhou, and X. Zeng. A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals. Physiol. Meas. 37:1945, 2016.

Zhang, Q., D. Zhou, and X. Zeng. Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomed. Eng. Online 16:23, 2017.

Zhang, Q., D. Zhou, and X. Zeng. A machine learning-empowered system for long-term motion-tolerant wearable monitoring of blood pressure and heart rate with ear-ECG/PPG. IEEE Access 5:10547–10561, 2017.