Evaluation of the beat-to-beat detection accuracy of PulseOn wearable optical heart rate monitor

Parák, Jakub; Tarniceriu, Adrian; Renevey, Philippe; Bertschi, Mattia; Delgado-Gonzalo, Ricard; Korhonen, Ilkka

doi:10.1109/embc.2015.7320273

Cited by 102 publications

(71 citation statements)

References 13 publications

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“…PPGbased systems measure the variation of blood volume over time using LEDs and photodiodes. Although less precise, PPGs are the chosen technique by all wrist-based cardiac monitoring sensors [9]. In our case, we focus on the analysis of the Heart Rate Variability (HRV) [10], that is, the analysis of the variation in the time intervals between heartbeats (a.k.a.…”

Section: Figmentioning

confidence: 99%

Secure Stream Processing for Medical Data

Segarra

Muntané

Lemay

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Medical data belongs to whom it produces it. In an increasing manner, this data is usually processed in unauthorized third-party clouds that should never have the opportunity to access it. Moreover, recent data protection regulations (e.g., GDPR) pave the way towards the development of privacy-preserving processing techniques. In this paper, we present a proof of concept of a streaming IoT architecture that securely processes cardiac data in the cloud combining trusted hardware and Spark. The additional security guarantees come with no changes to the application's code in the server. We tested the system with a database containing ECGs from wearable devices comprised of 8 healthy males performing a standardized range of in-lab physical activities (e.g., run, walk, bike). We show that, when compared with standard SPARK STREAMING, the addition of privacy comes at the cost of doubling the execution time.

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Section: Figmentioning

confidence: 99%

Secure Stream Processing for Medical Data

Segarra

Muntané

Lemay

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Self Cite

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“…In healthcare applications, wearable sensors can be categorised based on the measured entity into subtypes that include biopotential sensors [1,2], optical sensors [3,4], stretch and pressure sensors [5,6], chemical sensors [7,8], and Inertial Measurement Units (IMUs) [9,10,11,12]. Commercialisation of devices using the different subtypes of sensors has been gradually increasing.…”

Section: Introductionmentioning

confidence: 99%

Activity Recognition Using Complex Network Analysis

Jalloul¹,

Porée²,

Viardot³

et al. 2018

IEEE J. Biomed. Health Inform.

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In this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a Random Forest (RF) classifier, and when considering a monitoring system composed of only two modules positioned at the Neck and Thigh of the subject's body.

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“…Inexpensive consumer devices such as heart rate monitors and other wearable technology may have the potential to accurately detect irregular RR intervals19 20 and advancements in modern technology have enabled the implementation of relatively complex algorithms using readily available consumer devices including smartphones 1421 Systems employing wearable devices could be used for prolonged RR interval monitoring and may thus have potential for AF screening in the community.…”

Section: Introductionmentioning

confidence: 99%

“…The authors have secured funding from the School of Primary Care Research (SPCR) within the National Institute of Health Research (NIHR) in the UK to investigate the algorithm (using RR intervals sent via bluetooth from a Polar H7 heart rate monitor27 and also in an offline setting using RR intervals recorded by the Firstbeat Bodyguard 2 device20). Our trial is due to begin recruitment in September 2016.…”

Section: Introductionmentioning

confidence: 99%

Screening for Atrial Fibrillation using Economical and accurate TechnologY (SAFETY)—a pilot study

et al. 2017

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IntroductionAtrial fibrillation (AF) is a cause of stroke and a marker of atherosclerosis and of all patients with stroke, around 17% have AF. The screening and treatment of AF could prevent about 12% of all strokes. Several relatively low-cost devices with good accuracy now exist which can detect AF including WatchBP and AliveCor. However, they can only measure the ECG or pulse over short time periods. Inexpensive devices such as heart rate monitors, which are widely available, can measure heart rate for prolonged periods and may have potential in screening for AF. This study aims to determine the accuracy of AliveCor and WatchBP along with a bespoke algorithm using a heart rate monitor belt (Polar H7) and a wearable RR interval recorder (Firstbeat Bodyguard 2) for detecting AF during a single screening visit in primary care patients.Methods/analysisA multicentre case–control diagnostic study comparing the four different devices for the detection of AF with a reference standard consisting of a 12-lead ECG in GP surgeries across Hampshire, UK. We aim to recruit 92 participants with AF and 329 without AF aged 65 years and over. We will ask participants to rate comfort and overall impression for each device. We will collect qualitative data from participants capturing their experience of using wearable devices in order to evaluate acceptability. We will collect data from GPs to determine their views on AF screening.Ethics and disseminationThis protocol was approved by the London—City & East Research Ethics Committee in June 2016. The findings of the trial will be disseminated through peer-reviewed journals, national and international conference presentations and the Atrial Fibrillation Association, UK.Trial registration numberISRCTN17495003, Pre-results.

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Evaluation of the beat-to-beat detection accuracy of PulseOn wearable optical heart rate monitor

Cited by 102 publications

References 13 publications

Secure Stream Processing for Medical Data

Secure Stream Processing for Medical Data

Activity Recognition Using Complex Network Analysis

Screening for Atrial Fibrillation using Economical and accurate TechnologY (SAFETY)—a pilot study

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