Evaluation of PPG Biometrics for Authentication in different states

Amongst all medical biometric traits, Photoplethysmograph (PPG) is the easiest to acquire. PPG records the blood volume change with just combination of Light Emitting Diode and Photodiode from any part of the body. With IoT and smart homes' penetration, PPG recording can easily be integrated with other vital wearable devices. PPG represents peculiarity of hemodynamics and cardiovascular system for each individual. This paper presents non-fiducial method for PPG based biometric authentication. Being a physiological signal, PPG signal alters with physical/mental stress and time. For robustness, these variations cannot be ignored. While, most of the previous works focused only on single session, this paper demonstrates extensive performance evaluation of PPG biometrics against single session data, different emotions, physical exercise and time-lapse using Continuous Wavelet Transform (CWT) and Direct Linear Discriminant Analysis (DLDA). When evaluated on different states and datasets, equal error rate (EER) of $0.5\%$-$6\%$ was achieved for $45$-$60$s average training time. Our CWT/DLDA based technique outperformed all other dimensionality reduction techniques and previous work.Comment: Accepted at 11th IAPR/IEEE International Conference on Biometrics, 2018. 6 pages, 6 figure

Predict Daily Life Stress based on Heart Rate Variability

Department of Human Factors EngineeringThe purpose of this study is to investigate the feasibility of predicting a daily mental stress level from analyzing Heart Rate Variability (HRV) by using a Photoplethysmography (PPG) sensor which is integrated in the wristband-type wearable device. In this experiment, each participant was asked to measure their own PPG signals for 30 seconds, three times a day (at noon, 6 P.M, and 10 minutes before going to sleep) for a week. And 10 minutes before going to sleep, all participants were asked to self-evaluate their own daily mental stress level using Perceived Stress Scale (PSS). The recorded signals were transmitted and stored at each participant???s smartphone via Bluetooth Low Energy (BLE) communication by own-made mobile application. The preprocessing procedure was used to remove PPG signal artifacts in order to make better performance for detecting each pulse peak point at PPG signal. In this preprocessing, three- level-bandpass filtering which consisted three different pass band range bandpass filters was used. In this study, frequency domain HRV analysis feature that the ratio of low-frequency (0.04Hz ~ 0.15Hz) to high-frequency (0.15Hz ~ 0.4Hz) power value was used. In frequency domain analysis, autoregressive (AR) model was used, because this model has higher resolution than that of Fast Fourier Transform (FFT). The accuracy of this prediction was 86.35% on average of all participants. Prediction result was calculated from the leave-one-out validation. The IoT home appliances are arranged according to the result of this prediction algorithm. This arrangement is offering optimized user???s relaxation. Also, this algorithm can help acute stress disorder patients to concentrate on getting treatment.clos

A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure

Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patients’ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations

Validity of telemetric-derived measures of heart rate variability: a systematic review

Heart rate variability (HRV) is a widely accepted indirect measure of autonomic function with widespread application across many settings. Although traditionally measured from the 'gold standard' criterion electrocardiography (ECG), the development of wireless telemetric heart rate monitors (HRMs) extends the scope of the HRV measurement. However, the validity of telemetric-derived data against the criterion ECG data is unclear. Thus, the purpose of this study was twofold: (a) to systematically review the validity of telemetric HRM devices to detect inter-beat intervals and aberrant beats; and (b) to determine the accuracy of HRV parameters computed from HRM-derived inter-beat interval time series data against criterion ECG-derived data in healthy adults aged 19 to 62 yrs. A systematic review of research evidence was conducted. Four electronic databases were accessed to obtain relevant articles (PubMed, EMBASE, MEDLINE and SPORTDiscus. Articles published in English between 1996 and 2016 were eligible for inclusion. Outcome measures included temporal and power spectral indices (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). The review confirmed that modern HRMs (Polar® V800™ and Polar® RS800CX™) accurately detected inter-beat interval time-series data. The HRV parameters computed from the HRM-derived time series data were interchangeable with the ECG-derived data. The accuracy of the automatic in-built manufacturer error detection and the HRV algorithms were not established. Notwithstanding acknowledged limitations (a single reviewer, language bias, and the restricted selection of HRV parameters), we conclude that the modern Polar® HRMs offer a valid useful alternative to the ECG for the acquisition of inter-beat interval time series data, and the HRV parameters computed from Polar® HRM-derived inter-beat interval time series data accurately reflect ECG-derived HRV metrics, when inter-beat interval data are processed and analyzed using identical protocols, validated algorithms and software, particularly under controlled and stable conditions

Evaluation of the Linear Relationship Between Pulse Arrival Time and Blood Pressure in ICU Patients: Potential and Limitations

A variety of techniques based on the indirect measurement of blood pressure (BP) by Pulse Transit Time (PTT) have been explored over the past few years. Such an approach has the potential in providing continuous and non-invasive beat to beat blood pressure without the use of a cuff. Pulse Arrival Time (PAT) which includes the cardiac pre-ejection period has been proposed as a surrogate of PTT, however, the balance between its questioned accuracy and measurement simplicity has yet to be established. The present work assessed the degree of linear relationship between PAT and blood pressure on 96 h of continuous electrocardiography and invasive radial blood pressure waveforms in a group of 11 young ICU patients. Participants were selected according to strict exclusion criteria including no use of vasoactive medications and presence of clinical conditions associated with cardiovascular diseases. The average range of variation for diastolic BP was 60 to 79 mmHg while systolic BP varied between 123 and 158 mmHg in the study database. The overall Pearson correlation coefficient for systolic and diastolic blood pressure was −0.5 and −0.42, respectively, while the mean absolute error was 3.9 and 7.6 mmHg. It was concluded that the utilization of PAT for the continuous non-invasive blood pressure estimation is rather limited according to the experimental setup, nonetheless the correlation coefficient performed better when the range of variation of blood pressure was high over periods of 30 min suggesting that PAT has the potential to be used as indicator of changes relating to hypertensive or hypotensive episodes

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 159

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1976

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

ERS statement on standardisation of cardiopulmonary exercise testing in chronic lung diseases

The objective of this document was to standardise published cardiopulmonary exercise testing (CPET) protocols for improved interpretation in clinical settings and multicentre research projects. This document: 1) summarises the protocols and procedures used in published studies focusing on incremental CPET in chronic lung conditions; 2) presents standard incremental protocols for CPET on a stationary cycle ergometer and a treadmill; and 3) provides patients’ perspectives on CPET obtained through an online survey supported by the European Lung Foundation. We systematically reviewed published studies obtained from EMBASE, Medline, Scopus, Web of Science and the Cochrane Library from inception to January 2017. Of 7914 identified studies, 595 studies with 26 523 subjects were included. The literature supports a test protocol with a resting phase lasting at least 3 min, a 3-min unloaded phase, and an 8- to 12-min incremental phase with work rate increased linearly at least every minute, followed by a recovery phase of at least 2–3 min. Patients responding to the survey (n=295) perceived CPET as highly beneficial for their diagnostic assessment and informed the Task Force consensus. Future research should focus on the individualised estimation of optimal work rate increments across different lung diseases, and the collection of robust normative data.The document facilitates standardisation of conducting, reporting and interpreting cardiopulmonary exercise tests in chronic lung diseases for comparison of reference data, multi-centre studies and assessment of interventional efficacy. http://bit.ly/31SXeB