The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). With the application of machine learning the subject specific ECG signal can be differentiated. However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed.
References
[1]
Woodard Jr., J.D. (2001) Biometrics: Facing up to Terrorism. RAND Arroyo Center, Santa Monica.
[2]
Silva, H., Lourenço, A., Canento, F., Fred, A.L. and Raposo, N. (2013) ECG Biometrics: Principles and Applications. BIOSIGNALS 6th International Conference on Bio-Inspired Systems and Signal Processing, Barcelona, 11-14 February 2013, 215-220.
[3]
Israel, S.A. and Irvine, J.M. (2012) Heartbeat Biometrics: A Sensing System Perspective. International Journal of Cognitive Biometrics, 1, 39-65. https://doi.org/10.1504/IJCB.2012.046514
[4]
Hurst, J.W. (1998) Naming of the Waves in the ECG, with a Brief Account of Their Genesis. Circulation, 98, 1937-1942. https://doi.org/10.1161/01.CIR.98.18.1937
[5]
Barold, S.S. (2003) Willem Einthoven and the Birth of Clinical Electrocardiography a Hundred Years Ago. Cardiac Electrophysiology Review, 7, 99-104. https://doi.org/10.1023/A:1023667812925
[6]
Patel, S., Park, H., Bonato, P., Chan, L. and Rodgers, M. (2012) A Review of Wearable Sensors and Systems with Application in Rehabilitation. Journal of Neuroengineering and Rehabilitation, 9, 1-17. https://doi.org/10.1186/1743-0003-9-21
[7]
Unar, J.A., Seng, W.C. and Abbasi, A. (2014) A Review of Biometric Technology along with Trends and Prospects. Pattern Recognition, 47, 2673-2688. https://doi.org/10.1016/j.patcog.2014.01.016
[8]
Pinto, J.R., Cardoso, J.S. and Lourenço, A. (2018) Evolution, Current Challenges, and Future Possibilities in ECG Biometrics. IEEE Access, 6, 34746-34776. https://doi.org/10.1109/ACCESS.2018.2849870
[9]
Seeley, R.R., Stephens, T.D. and Tate, P. (2003) Cardiovascular System: The Heart. In: Anatomy & Physiology, 6th Edition, McGraw-Hill, Boston, 667-709.
[10]
LeMoyne, R. and Mastroianni, T. (2021) Biometrics of ECG Signal through Temporal Organization with Support vector Machine. 9th IEEE International Conference on e-Health and Bioengineering (EHB), Iasi, 18-19 November 2021, 1-4. https://doi.org/10.1109/EHB52898.2021.9657724
[11]
LeMoyne, R. and Mastroianni, T. (2021) Preliminary Biometrics of ECG Signal Based on Temporal Organization through the Implementation of a Multilayer Perceptron Neural Network. Journal of Biomedical Science and Engineering, 14, 435-441. https://doi.org/10.4236/jbise.2021.1412037
[12]
LeMoyne, R. and Mastroianni, T. (2021) Waikato Environment for Knowledge Analysis (WEKA) a Perspective Consideration of Multiple Machine Learning Classification Algorithms and Applications. In: LeMoyne, R. and Mastroianni, T., Eds., Applied Software Development with Python & Machine Learning by Wearable & Wireless Systems for Movement Disorder Treatment via Deep Brain Stimulation, World Scientific Publishing, Singapore, 137-180. https://doi.org/10.1142/9789811235962_0006
[13]
LeMoyne, R. and Mastroianni, T. (2020) Machine Learning Classification for Network Centric Therapy Utilizing the Multilayer Perceptron Neural Network. In: Multilayer Perceptrons: Theory and Applications, Nova Science Publishers, Hauppauge, 39-76.
[14]
Ribeiro, M.T., Singh, S. and Guestrin, C. (2016) “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 13-17 August 2016, 1135-1144. https://doi.org/10.1145/2939672.2939778
[15]
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I.H. (2009) The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter, 11, 10-18. https://doi.org/10.1145/1656274.1656278
[16]
Witten, I.H., Frank, E. and Hall, M.A. (2011) Data Mining: Practical Machine Learning Tools and Techniques. 3rd Edition, Morgan Kaufmann Publishers, Burlington. https://doi.org/10.1016/C2009-0-19715-5
Goldberger, A.L., Amaral, L.A., Glass, L., et al. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101, e215-e220. https://doi.org/10.1161/01.CIR.101.23.e215
[20]
Cortes, C. and Vapnik, V. (1995) Support-Vector Networks. Machine Learning, 20, 273-297. https://doi.org/10.1007/BF00994018
[21]
Vapnik, V.N. (1999) The Nature of Statistical Learning Theory. 2nd Edition, Springer-Verlag, New York. https://doi.org/10.1007/978-1-4757-3264-1
[22]
Witten, I.H., Frank, E. and Hall, M.A. (2011) Implementations: Real Machine Learning Schemes. In: Witten, I.H., Frank, E. and Hall, M.A., Eds., Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann Publishers, Burlington, 191-304. https://doi.org/10.1016/B978-0-12-374856-0.00006-7
[23]
Russell, S.J. and Norvig, P. (2010) Learning from Examples. In: Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall, Upper Saddle River, 693-767.
[24]
Bryson, A.E. and Ho, Y.C. (1969) Applied Optimal Control: Optimization, Estimation, and Control. Blaisdell Publishing, Waltham.
[25]
Russell, S.J. and Norvig, P. (2010) Introduction. In: Russell, S. and Norvig, P., Eds., Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall, Upper Saddle River, 1-33.
[26]
LeMoyne, R. and Mastroianni, T. (2021) Machine Learning Classification of Essential Tremor Using a Reach and Grasp Task with Deep Brain Stimulation System Set to “On” and “Off” Status. In: LeMoyne, R. and Mastroianni, T., Eds., Applied Software Development with Python & Machine Learning by Wearable & Wireless Systems for Movement Disorder Treatment via Deep Brain Stimulation, World Scientific Publishing, Singapore, 181-205. https://doi.org/10.1142/9789811235962_0007
[27]
Quinlan, J.R. (1993) Introduction. In: C4.5 Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, 1-16.
[28]
Quinlan, J.R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo.
Wu, X., Kumar, V., Quinlan, J.R., et al. (2008) Top 10 Algorithms in Data Mining. Knowledge and Information Systems, 14, 1-37. https://doi.org/10.1007/s10115-007-0114-2
[31]
Harrington, P. (2012) Splitting Datasets One Feature at a Time: Decision Trees. In: Harrington, P., Ed., Machine Learning in Action, Manning Publications, Shelter Island, 37-60.
[32]
LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539
[33]
LeMoyne, R. and Mastroianni, T. (2021) Global Algorithm Development. In: LeMoyne, R. and Mastroianni, T., Eds., Applied Software Development with Python & Machine Learning by Wearable & Wireless Systems for Movement Disorder Treatment via Deep Brain Stimulation, World Scientific Publishing, Singapore, 63-86. https://doi.org/10.1142/9789811235962_0003
[34]
LeMoyne, R. and Mastroianni, T. (2021) Incremental Software Development Using Python. In: LeMoyne, R. and Mastroianni, T., Eds., Applied Software Development with Python & Machine Learning by Wearable & Wireless Systems for Movement Disorder Treatment via Deep Brain Stimulation, World Scientific Publishing, Singapore, 87-106. https://doi.org/10.1142/9789811235962_0004
[35]
LeMoyne, R. and Mastroianni, T. (2021) Automation of Feature Set Extraction Using Python. In: LeMoyne, R. and Mastroianni, T., Eds., Applied Software Development with Python & Machine Learning by Wearable & Wireless Systems for Movement Disorder Treatment via Deep Brain Stimulation, World Scientific Publishing, Singapore, 107-135. https://doi.org/10.1142/9789811235962_0005