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LeMoyne, R., Mastroianni, T., Whiting, D. and Tomycz, N. (2019) Assessment of Machine Learning Classification Strategies for the Differentiation of Deep Brain Stimulation “On” and “Off” Status for Parkinson’s Disease Using a Smartphone as a Wearable and Wireless Inertial Sensor for Quantified Feedback. In: LeMoyne, R., Mastroianni, T., Whiting, D. and Tomycz, N., Eds., Wearable and Wireless Systems for Healthcare II, Springer, Singapore, 113-126.
https://doi.org/10.1007/978-981-13-5808-1_9

has been cited by the following article:

• TITLE: Preliminary Biometrics of ECG Signal Based on Temporal Organization through the Implementation of a Multilayer Perceptron Neural Network

  AUTHORS: Robert LeMoyne, Timothy Mastroianni

  KEYWORDS: ECG Signal, Biometrics, Multilayer Perceptron Neural Network, Machine Learning, Signal Analysis

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.14 No.12, December 23, 2021

  ABSTRACT: The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.