A preliminary study on continuous authentication methods for photoplethysmographic biometrics

Recent studies in biometrics focus on one dimensional physiological signals commonly acquired in medical applications, like electrocardiogram (ECG), electroencephalograms (EEG), phonocardiogram (PCG), and photoplethysmogram (PPG). In this context, an important application is in continuous authentication scenarios since physiological signals are frequently captured for long time periods in order to monitor the health status of the patients

ECG biometric recognition : permanence analysis of QRS signals for 24 hours continuous authentication

Recent studies regard the use of ECG signals for biometric recognition exploiting the possibility of these signals to be frequently recorded for long time periods without any explicit actions performed by the users during the acquisitions. This aspect makes ECG signals particularly suitable for continuous authentication applications. In this context, researches have proved that the QRS complex is the most stable component of the ECG signal. In this paper, we perform a preliminary study on the persistency of QRS signals for continuous authentication systems. A recognition method based on multiple leads is proposed, and used to evaluate the persistency of the QRS complex in 24 hours Holter signals. This time interval can be considered as adequate for many possible applications in continuous authentication scenarios. The analysis is performed on a significantly large public Holter dataset and aims to search accurate matching and enrollment strategies for continuous authentication systems. At the best our knowledge, the results presented in this paper are based on the biggest set of ECG signals used to design continuous authentication applications in the literature. Results suggest that the QRS complex is stable only for a relatively small time period, and the performance of the proposed recognition method starts decreasing after two hours

Biometric identification using analysis of cardiac sound

Human Heart Sound is unique in nature. It helps to regulate the pumping blood to the rest of the organ system for proper function, so that that pumping blood abruptly passes through the heart chamber to create heart sounds which are sounds as LUB and DUB via closure of Bicuspid and Tricuspid valve. These sounds having two segments S1 belongs to first sound and S2 belongs to second sound. In my works, first we made data collection from our ten volunteer of the age group 20-40 during three months period using Digital Stethoscope. We are having 100 heart samples stored in database. Then feature extraction using LFBC (linear frequency band cepstral), feature extraction method includes STDFT for converting the time domain signal into frequency domain. Then magnitude was taken and rejecting the phase part which generally include noise interference. Next the filter bank is applied, which reject the unwanted high frequency components. After that Dimension compression technique was used. Using DCT (Discrete Cosine Transform) here logarithmic first 24 coefficient was taken. Then Spike removal is done for removing the artifacts of position of hand movement while taking heart sound. At last, cepstral means subtraction is done, which removes the artifacts, here position of stethoscope is not same at all the time, after this operation is done, cepstral coefficient as our feature vector. Then Classification is done, using BP-MLP-ANN where 50 numbers of heart sound signal as Training and 50 numbers of heart sound signal as Testing are applied. The identification results show 52 % of performance accuracy

Adaptive ECG biometric recognition : a study on re-enrollment methods for QRS signals

The diffusion of wearable and mobile devices for the acquisition and analysis of cardiac signals drastically increased the possible applicative scenarios of biometric systems based on electrocardiography (ECG). Moreover, such devices allow for comfortable and unconstrained acquisitions of ECG signals for relevant time spans of tens of hours, thus making these physiological signals particularly attractive biometric traits for continuous authentication applications. In this context, recent studies showed that the QRS complex is the most stable component of the ECG signal, but the accuracy of the authentication degrades over time, due to significant variations in the patterns for each individual. Adaptive techniques for automatic template update can therefore become enabling technologies for continuous authentication systems based on ECG characteristics

Analysis of phonocardiograph signal as a biometric application

Heart sound is distinctive in nature. Earlier work reported that, it can also contribute a lot to recognize a person by their heart sound. A novel technique is described in this thesis for the identification and verification of the person using energy based feature set and back propagation multilayer perceptron artificial neural network classifier (BP-MLP-ANN) is used in this thesis. PCG signal is invariable, unique, universal easy to accessible and unique in nature. Heart samples were collected through ten volunteers as ten data (i.e. heart sounds) per individuals. Before feature extraction, pre-processing involves extraction of cycles, alignment, and segmentation of primary heart sound S1 and S2. This Segmentation contributes to the features extraction based on energy taken 30 windows at a time. Classification was done, using BP-MLP-ANN. 69 % of total numbers of heart sound signal were used as Training and remaining 31 % of heart sound signal were used for Testing. The identification results show 63.3824 % of performance accuracy

No soldiers left behind: An IoT-based low-power military mobile health system design

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user\u27s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information