Transparent authentication: Utilising heart rate for user authentication

There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices - principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficents. Ten statistical features are extracted from each level of the coffecient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1 st , 2 nd , 3 rd and 4 th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25% As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice

Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches

Detection of atrial fibrillation (AF) from a wrist watch photoplethysmogram (PPG) signal is important because the wrist watch form factor enables long term continuous monitoring of arrhythmia in an easy and non-invasive manner. We have developed a novel method not only to detect AF from a smart wrist watch PPG signal, but also to determine whether the recorded PPG signal is corrupted by motion artifacts or not. We detect motion and noise artifacts based on the accelerometer signal and variable frequency complex demodulation based time-frequency analysis of the PPG signal. After that, we use the root mean square of successive differences and sample entropy, calculated from the beat-to-beat intervals of the PPG signal, to distinguish AF from normal rhythm. We then use a premature atrial contraction detection algorithm to have more accurate AF identification and to reduce false alarms. Two separate datasets have been used in this study to test the efficacy of the proposed method, which shows a combined sensitivity, specificity and accuracy of 98.18%, 97.43% and 97.54% across the datasets

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Study protocol for Smartphone Monitoring for Atrial fibrillation in Real-Time in India (SMART-India): a community-based screening and referral programme

INTRODUCTION: Atrial fibrillation (AF), the world\u27s most common arrhythmia, often goes undetected and untreated in low-resource communities, including India, where AF epidemiology is undefined. AF is an important risk factor for stroke, which plagues an estimated 1.6 million Indians annually. As such, early detection of AF and management of high-risk patients is critically important to decrease stroke burden in individuals with AF. This study aims to describe the epidemiology of AF in Anand District, Gujarat, India, characterise the clinical profile of individuals who are diagnosed with AF and determine the performance of two mobile technologies for community-based AF screening. METHODS: This observational study builds on findings from a previous feasibility study and leverages two novel technologies as well as an existing community health programme to perform door-to-door AF screening for 2000 people from 60 villages of Anand District, Gujarat, India using local health workers. A single-lead ECG and a pulse-based application is used to screen each individual for AF three times over a period of 5 days. Participants with suspected arrhythmias are followed up by study cardiologist who makes final diagnoses. Participants diagnosed with AF are initiated on treatment based on current anticoagulation guidelines and clinical reasoning. ANALYTICAL PLAN: Age-stratified and sex-stratified prevalence of AF in the Anand District will be calculated for sample and estimated for Anand distribution using survey design weights. Sociodemographic and clinical factors associated with AF will be evaluated using multivariable regression methods. Performance of each mobile technology in detecting AF will be evaluated using a 12-lead ECG interpretation as the gold standard. ETHICS AND DISSEMINATION: This protocol was approved separately by the Institutional Review Board of University of Massachusetts Medical School and the Human Research Ethics Committee at Charutar Arogya Mandal. The findings of this study will be disseminated through peer-reviewed journals and scientific conferences

Implementation of The Variable Data Transmission System of Abnormal ECG with Activity State

The cause of death among modern people are the highest death rate from heart disease and monitoring and management of ECG signals to cope with these heart diseases is necessary. ECG is a bio-signal that is an important criterion for determining the presence or absence of cardiac activity states. Recently, an attempt has been made to analyze and compare biological signals and physical activity information for accurate analysis and diagnosis. However, in order to measure the ECG data for a long time, a storage space of several Mbytes and a wide bandwidth for wireless transmission are required. To solve this problem,  cost, time, and high-performance systems are additional required. The implemented system minimizes the amount of packets generated during wireless data transmission as well as abnormal heartbeat detection and activity information, and enables monitoring of heart activity status and activity information in real time through a smart phone. In order to evaluate the data packet transmission and restoration performance of the system implemented in this research, the MIT / BIH Arrhythmia Database 100 record was embedded in the system controller section and the packet was transmitted to the smartphone. In addition, ECG evaluation experiments were conducted according to the activity status during daily life. As a result of the performance evaluation, both experiments confirmed the data packet generated and signal restoration performance

Pregled utjecaja obrazovanja na usvajanje pametnih tehnologija za detekciju fibrilacije atrija

SUMMARY The main objective of this review was to investigate whether educational attainment has an impact on the occurrence of atrial fibrillation (AF) as well as the implementation of smart technology to detect this condition. Data on the relationship between education level and the occurrence of AF were collected, as well as data on smart devices for detecting AF. A lower level of education has been linked to an increased risk of AF. With this in mind, it is easy to explain the clear correlation between education level and AF, as well as the adoption of smart device detection and how it may improve illness prognosis. People with a higher level of education understand and embrace the notion of employing smart devices to detect and prevent AF; they also have decreased AF prevalence compared with those with a lower level of education.SAŽETAK Cilj je ovoga preglednog članka bio istražiti ima li obrazovanje utjecaja na pojavu fibrilacije atrija (AF, prema engl. atrial fibrillation), kao i implementaciju pametne tehnologije za otkrivanje ove bolesti. Prikupljeni su podatci o povezanosti razine obrazovanja i pojave AF-a, te o pametnim uređajima za njegovu detekciju. Niža razina obrazovanja povezana je s povećanim rizikom od AF-a. Imajući to na umu, jednostavno je razumjeti jasnu korelaciju između razine obrazovanja i AF-a, kao i usvajanje detekcije pametnim uređajima i kako to može poboljšati prognozu bolesti. Ljudi s višom razinom obrazovanja razumiju i prihvaćaju pojam uporabe pametnih uređaja za otkrivanje i sprječavanje AF-a; oni također imaju smanjenu prevalenciju AF-a od onih s nižim stupnjem obrazovanja

The Emerging Wearable Solutions in mHealth

The marriage of wearable sensors and smartphones have fashioned a foundation for mobile health technologies that enable healthcare to be unimpeded by geographical boundaries. Sweeping efforts are under way to develop a wide variety of smartphone-linked wearable biometric sensors and systems. This chapter reviews recent progress in the field of wearable technologies with a focus on key solutions for fall detection and prevention, Parkinson’s disease assessment and cardiac disease, blood pressure and blood glucose management. In particular, the smartphone-based systems, without any external wearables, are summarized and discussed