Acceleration plethysmogram based biometric identification

This paper presents the feasibility study of Acceleration Plethysmogram (APG) based biometric identification system. APG signals are obtained from the second derivative of the Photoplethysmogram (PPG) signal. It has been reported from previous literature that APG signals contain more information as compared to the PPG signal. Thus, in this paper, the robustness and reliability of APG signal as a biometric recognition mechanism will be proven. APG signals of 10 subjects were acquired from the Multiparameter Intelligent Monitoring in Intensive Care II Waveform Database (MIMIC2WDB) which contains PPG signals with a sampling frequency of 125 Hz. The signals were later converted into an APG waveform. Then, discriminating features are extracted from the APG morphology. Finally, these APG samples were classified using commonly known classification techniques to identify individuals. Based on the experimentation results, APG signal when using Bayes Network gives an identification rate of 97.5 percentage as compared to PPG signal of 55 percentage for the same waveform. This outcome suggests the feasibility and robustness of APG signals as a biometric modality as compared to PPG signals

Photoplethysmogram Based Biometric Identification Incorporating Different Age and Gender Group

Biometric is the authentication and identification of a person by measuring or estimating their physiological characteristics. First generation biometric such as fingerprint, signature and voice have drawback and easily can be duplicated which lead to serious identity theft crime. Therefore, second generation of biometric was introduced by using bio-signal. This study evaluates the possibility of applying PPG as biometric identification system incorporating different age, gender group, and time variability. A total of 36 subjects were involved in this study consists of 18 males and 18 females for age difference and gender analysis. The PPG signals were taken in resting state by using pulse oximeter. The PPG signal was differentiated twice in order to form APG signal. These signals then undergo preprocessing and the segmentation process was done by using MATLAB. The highest peaks from the signal was used as reference point to determine the appropriate distance for one cycle of both signal. Then, the signals were classified by four commonly used classifiers which are Bayes Network, Naïve Bayes, Multilayer Perceptron, and Radial Basis Function. The outcome from this study suggested the accuracy up to 100% for different age group, 91.11% for female subjects and 95% for male subjects

Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS

This paper proposes a robust biometric identification system using compressed electrocardiogram (ECG) signal by varying physiological conditions. The ECG data were obtained by recording a total of 30 healthy subjects where they performed six regular daily activities repeatedly at a sampling frequency of 1000 Hz. Then, the QRS complexes are segmented by implementing Amplitude Based Technique (ABT) where it compares the amplitudes of ECG points to determine the R peak. The segmented QRS is then compressed for various levels by using Discrete Wavelet Transform (DWT) algorithms and first 3 Daubechies (db) wavelet are computed. Next, a Cardioid graph is generated. In order to verify the matching process, the classification is performed by using the Multilayer Perceptron (MLP) technique. The results show that by applying this method, the accuracy of the identification rate can be achieved as high as 96.4% even when the data file is compressed up to 73.3%. When the data file is compressed, the outcomes also demonstrate that the execution time is less compare to non-compressed data. Therefore, the biometric identification system can be implemented efficiently as there will be a lesser issue regarding the data storage, execution time and accuracy based on the outcome of the study

The development of human biometric identification using acceleration plethysmogram

This study explicates the practicability of using acceleration plethysmogram (APG) signal in biometric identification. The introduction of APG signal is initiated from the congenital of photoplethysmogram (PPG) signal since APG signal has been widely known as the second derivative of PPG signal. Previous researchers claimed that APG signal elucidates more information as compared to PPG signal. For this reason, the robustness and reliability of APG signal as biometric recognition is demonstrated. A total of 10 subjects obtained from MIMIC II WAFEFORM Database (MIMIC2WDB) which provides PPG signals with a 125 Hz sampling frequency are used as test samples. The signals are then differentiated twice to obtain the APG signals. Then, discriminative features are extracted from the APG morphology. Finally, these APG samples were classified using commonly known classification techniques to identify individuals. Based on the experimentation results, APG signal when using Multilayer Perceptron gives an identification rate of 98% as compared to PPG signal of 76% for the same waveform. This outcome suggests the feasibility and robustness of APG signals as a biometric modality as an alternative to current techniques

Time variability analysis of photoplethysmogram biometric identification system

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

Study of acceleration plethysmogram based biometric identification incorporating different time instances

This study investigates the effectiveness of acceleration plethysmogram (APG) to be applied as a biometric identification system in different time instances. Currently, most of the study actively discusses on the ability of photoplethysmogram (PPG) for person identification. To the best of our knowledge, little has been said on related studies on APG signals. A total of 5 PPG signals were collected from a publicly available online repository, which is MIMIC II Waveform Database, version 3, part 3 for two different periods and then undergoes preprocessing using a low pass filter. After that, the signals were segmented and later differentiated to produce APG signals. Lastly, the APG signals were classified using four different types of classifiers, namely, Naïve Bayes, Bayes Network, Multilayer Perceptron (MLP) and Radial Basis Function (RBF). Based on the experimentation results, the accuracy for all classifiers increase when applying APG as a biometric modality of up to 11.72% as compared to PPG signals

Development of cardioid based graph ECG heart abnormalities classification technique

In this study, the development of Cardioid based graph electrocardiogram heart abnormalities classification technique is presented. ECG signals in this work were acquired from a public online database UCD Sleep Apnea database (UCDB) with sampling rate of 250 Hz. Each recording has 60 seconds of electrocardiogram signals. Unique features were extracted using the Pan Tompkins algorithm, later Cardioid based graph was formed as the result of the differentiation process. The various shapes of closed-loop created were then observed. From the Cardioid loop, we evaluated the area and standard deviation to differentiate between normal and abnormal heartbeats. As a result, the area and standard deviation values of abnormal heartbeat were twice the value of a normal heartbeat thus indicating the differences between two types of heart morphologies. In order to justify the results, the signal is then classified by using Bayes Network classifier. Classification outcomes suggests that the proposed technique gives heart abnormality identification with a classification accuracy of as low as 12.5% when normal and abnormal heartbeat are matched (two different conditions). Thus, the output of the study suggests the proof-of-concept of our proposed mechanisms to detect heart abnormalities and has the potential to act as an alternative to the current techniques

Sleep apnea detection using cardioid based grap

In this study, the development of Cardioid based graph electrocardiogram heart abnormalities classification technique is presented. ECG signals in this work were attained from a public online database UCD Sleep Apnea database (UCDB) with sampling rate of 250 Hz. Each recording has 60 seconds of electrocardiogram signals. Unique features were extracted using the Pan Tompkins algorithm, later Cardioid based graph was formed as the result of the differentiation process. The various shapes of closed-loop created were then observed. From the Cardioid loop, we evaluated the area and standard deviation to differentiate between normal and abnormal heartbeats. As a result, the area, standard deviation, and mean values of abnormal heartbeat were twice the value of a normal heartbeat thus indicating the differences between two types of heart morphologies. Thus, the output of the study suggests the proof-of-concept of our proposed mechanisms to detect heart abnormalities and has the potential to act as an alternative to the current techniques

Development of a photoplethysmogram based heart abnormality detection technique

In this study, the development of Cardioid based graph photoplethysmogram heart abnormality detection technique is presented. PPG signals in this work were collected from an online public repository called MIMIC II Waveform Database, Version 3 Part 1 with sampling rate of 250 Hz. Each recording has one minute of PPG signals. Distinctive features were extracted, and then the Cardioid based graph was plotted as the result of the differentiation of the signals. In addition, the different shapes of closed-loop created were then observed and assessed. From the Cardioid loop, the area and standard deviation were computed to distinguish between normal and abnormal heartbeats. Based on the results, these values for abnormal heartbeat were higher than the value of normal heartbeat thus signifying the differences between two categories of heart conditions. Therefore, the results of this study suggest the capability of the proposed mechanisms to determine heart abnormality and act as an alternative to the current detection system

Cardiac irregularity detection using photoplethysmogram signal

In this study, photoplethysmogram (PPG) based heart abnormality detection method was proposed. PPG signals utilized were obtained from MIMIC II Waveform Database, Version 3 Part 1 with sampling frequency of 250 Hz with the duration of 10 seconds each. The feature of the PPG signals were then extracted using MATLAB and the distances between successive minimum troughs as well as the area of Cardioid graphs of PPG signals were calculated and evaluated to differentiate the normal and abnormal PPG signal. Based on the experimentation results, distances between minimum trough and the area of the Cardioid graphs of abnormal PPG signals are larger than the normal segments. Therefore, the results show the proof-of-concept of the proposed heart abnormality detection technique and suggest a better alternative to the current techniques