Smartphone Homecare Monitoring of Hearts

Szu, Harold; Hsu, Charles C.; Moon, Gyu; Landa, Joseph S.; Nakajima, Hiroshi; Hata, Yoshinobu

doi:10.1155/2013/983515

Cited by 3 publications

(1 citation statement)

References 6 publications

(4 reference statements)

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“…The widespread usage of mobile phones with the advanced skills of Information and Communication Technology, a variety of clinical domains has been emerging in terms of telehealth [1]- [2]. Among these aspects of healthcare awareness, mobile telecardiology systems are developed to monitor heart conditions especially from the high-risk cardiac patients in the patient-centric manner [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Digital Filters for Mobile Healthcare Applications

Kim¹,

Park²,

Lee³

et al. 2014

IJEEE

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Digital filtering process is often necessary to suppress the noisy characteristics embedded in biomedical signals such as ECG (Electrocardiogram), EEG (Electroencephalogram), and EMG (Electromyogram). As the availability and usage levels of Information and Communication Technology (ICT) increases, the applications of ubiquitous healthcare are expected to be accelerated in the form of pervasive healthcare utilizing the mobile devices for the next decade. One of these aspects, ambulatory ECG signals can be transmitted to a mobile device to offer telecardiology healthcare delivery. However the signal is vulnerable to base-line drift and power-line source fluctuations. With this aim, the digital filtering algorithms are implemented with an android platform smartphone to eliminate the noisy components especially in the ECG signal transmitted wirelessly from the wearable patch-style heart activity monitoring system. Graphical User Interface (GUI) is designed and implemented in an Android OS smartphone with programming multi-thread-Java modules to realize FIR (Finite Impulse Response Filter) and IIR (Infinite Impulse Response Filter). With applying our implemented digital filters built in a mobile device, we can conclude the fact that the artifacts in the ambulatory ECG signal due to base-line wandering and power-line instability can be efficiently reduced.  Index Terms-electrocardiogram, smartphone, finite impulse response filter, infinite impulse response filter, graphic user interface, mobile healthcare Kyeong-Seop Kim received the B.S. and M.S.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of Digital Filters for Mobile Healthcare Applications

Kim¹,

Park²,

Lee³

et al. 2014

IJEEE

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Authentication, privacy, security can exploit brainwave by biomarker

Jenkins

Sweet

Sweet³

et al. 2014

SPIE Proceedings

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We seek to augment the current Common Access Control (CAC) card and Personal Identification Number (PIN) verification systems with an additional layer of classified access biometrics. Among proven devices such as fingerprint readers and cameras that can sense the human eye's iris pattern, we introduced a number of users to a sequence of 'grandmother images', or emotionally evoked stimuliresponse images from other users, as well as one of their own, for the purpose of authentication. We performed testing and evaluation of the Authenticity Privacy and Security (APS) brainwave biometrics, similar to the internal organ of the human eye's iris which cannot easily be altered. 'Aha' recognition through stimulus-response habituation can serve as a biomarker, similar to keystroke dynamics analysis for inter and intra key fluctuation time of a memorized PIN number (FIST). Using a non-tethered Electroencephalogram (EEG) wireless smartphone/pc monitor interface, we explore the appropriate stimuli-response biomarker present in DTAB low frequency group waves. Prior to login, the user is shown a series of images on a computer display. They have been primed to click their mouse when the image is presented. DTAB waves are collected with a wireless EEG and are sent via Smartphone to a cloud based processing infrastructure. There, we measure fluctuations in DTAB waves from a wireless, non-tethered, single node EEG device between the Personal Graphic Image Number (PGIN) stimulus image and the response time from an individual's mental performance baseline. We hope Higher Order Statistics (HOS) in typing and PGIN can reveal intrinsic personal traits/habits and increase EEG specificity through stimulus-response neuro-biomarkers. Towards that goal, we describe an infrastructure that supports distributed verification for webbased EEG authentication. The performance of machine learning on the relative Power Spectral Density EEG data may uncover features required for subsequent access to web or media content. Our approach provides a scalable framework wrapped into a robust Neuro-Informatics toolkit, viable for use in the Biomedical and mental health communities, as well as numerous consumer applications.

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The Use of Mobile ECG Monitoring Devices in Embolic Stroke Associated With Atrial Fibrillation

Maniar

Donohoe

Troudt

Advances in Medical Education, Research, and Ethics

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Stroke prevention is a primary aim in clinical neurology. Atrial fibrillation (AF), the most common cardiac arrhythmia affecting 35 million people worldwide, carries a 5x risk of devastating stroke largely preventable with anticoagulation. The AliveCor Kardia™ mobile application for iPhones and Kardia Band™, a wristband for the Apple Watch, are approved by the Food and Drug Administration (FDA). These clinically validated mobile electrocardiograms (ECGs) utilize an artificial intelligence algorithm to provide instant analysis for detecting AF. The preliminary experience using this novel technology at a safety net, teaching hospital was very positive. AliveCor's™ was easy to use by neurologists, residents, medical students and nursing personnel and, in combination with pulse palpation, consistently detected AF that was confirmed by a cardiologist with standard ECG. With this new technology, the identification of AF that previously went unrecognized and often asymptomatic has prompted significant changes in patient therapy (additional cardiology evaluation and administration of anticonvulsants). AliveCor™ improved the ability to identify AF in elderly patients with and without a prior history of stroke. Although detection of paroxysmal, persistent and chronic AF was achieved, stratification of the risk versus benefit of anticoagulation therapy in individual patients became more difficult. The anticipated epidemic of AF will be easier to detect with technological advances like AliveCor™, which provides accurate analysis compared to current anticoagulation risk population-based assessment tools. One assessment tool, the CHA2DS2-VASc, depicts a predictive value for stroke in patients with AF with a P value equal to 0.58, making it merely significant. More precise physiologic biomarkers of thrombus formation using blood or cardiac imaging are needed to segregate subgroups of this expanding population of patients found to be in AF.

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Smartphone Homecare Monitoring of Hearts

Cited by 3 publications

References 6 publications

Design and Implementation of Digital Filters for Mobile Healthcare Applications

Design and Implementation of Digital Filters for Mobile Healthcare Applications

Authentication, privacy, security can exploit brainwave by biomarker

The Use of Mobile ECG Monitoring Devices in Embolic Stroke Associated With Atrial Fibrillation

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