Informatics Publishing Limited

Nur Izzati Mohammed Nadzri ¹, Mariana Sulaimi ¹, Lina Fadhilah Umadi ¹, Khairul Azami Sidek ¹

Affiliations
1 Department of Electrical and Computer Engineering, Faculty of Engineering, International Islamic University Malaysia, 53100 Kuala Lumpur, MY

Abstract

This paper presents a photoplethysmogram (PPG) based biometric recognition technique for twins. PPG devices have been widely used due to its advantages such as non-invasive, low cost and small in size which makes it a convenient analytical tool. To the best of our knowledge, little has been set pertaining to biometric recognition for twins using PPG signal. A total of six subjects from three couple of twins were used for experimentation purposes. The signals were processed using a low pass filter to remove unwanted noise. Then, multiple cycle of PPG waveforms were extracted and later, Naive Bayes (NB) and Radial Basis Function (RBF) network classifiers are used to categorize the subjects using the discriminant features. Based on the experimentation results, classification accuracies of 97% and 94% were achieved when using Naive Bayes and RBF network respectively which suggests the capability of our proposed system to identify individuals regardless whether the persons is a twin or not. The outcome also provides complimentary mechanism to detect a person besides using the current existing methods.

Keywords

Naive Bayes, Photoplethysmogram (PPG), Radial Basis Function (RBF) Network, Systolic and Diastolic.

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Siti Nurfarah Ain Mohd Azam ¹, Khairul Azami Sidek ¹

Affiliations
1 International Islamic University of Malaysia, Department of Electrical and Computer Engineering, Faculty of Engineering, International Islamic University of Malaysia, 53100 Jalan Gombak, Kuala Lumpur, MY

Abstract

This paper presents the time variability analysis of photoplethysmogram biometric identification system. There have been few researches discussing about the effectiveness of PPG signal as a biometric identification system in different time instances. PPG signals of 5 subjects were obtained from MIMIC II Waveform Database, version 3, part 3 with a sampling frequency of 125 Hz. The signals were pre-process using low pass Butterworth filter. Then, discriminating features were extracted from the PPG waveform in varying time instances (different days). Finally, this PPG samples were classified using commonly known classification techniques for person identification. Based on experimentation results, PPG signals when using LMT and FT gives identification rates of 96% for both classifiers. For sensitivity and specificity test, both LMT and FT give the accuracy of 0.96 and 0.01. The precision test gives the result of 0.962 for LMT and 0.964 for FT. Thus, this outcome suggests the feasibility and robustness of PPG signals as a biometric modality in different time instances.

Keywords

Biometric, FT, LMT, PPG Signal, Person Identification, Time Variability.

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Nuri Izzati Zainal ¹, Khairul Azami Sidek ¹

Affiliations
1 Department of Electrical and Computer Engineering, International Islamic University Malaysia, 53100 Kuala Lumpur, MY

Abstract

This paper presents a study of Photoplethysmography (PPG) for Heart Rate Variability (HRV) measurement. PPG is a non-invasive device which is used to measure the blood volume changes in the veins. Using optical based method, the PPG signal gives such reliable information regarding to the person’s body condition. One of the information that could be extracted from PPG signals is the measurement of HRV. In this research, PPG data acquisition is done in real time where it is directly processed using Graphical User Interface (GUI) based application. The main purpose of this research is to design a prototype for acquiring real time PPG signal for HRV measurement. Raw PPG data is extracted from HRM2511e and filtered using Easy Pulse circuit. Then, a software prototype is built for feature extraction and classification. The result is based on 6 different subjects. Each subject will provide PPG signal and HRV measurement in two different physical conditions which are sitting and standing.

Keywords

Data Acquisition, Easy Pulse, GUI, Heart Rate Variability, PPG.

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