A comparison of the noise sensitivity of nine QRS detection algorithms

Friesen, G.M.; Jannett, T.C.; Jadallah, M.A.; Yates, S.L.; Quint, Stephen R.; Nagle, H. Troy

doi:10.1109/10.43620

Cited by 917 publications

(451 citation statements)

References 20 publications

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“…The acquired pulse signal was used to derive the instantaneous heart rate. 12 Instantaneous heart rate is the cyclic fluctuation in the time delay between each heart beat (heart rate increases slightly during inspiration and reduces during expiration), whereas pulse rate is simply the number of heart beats per minute. Three simultaneous recordings of pulse signals and ocular surface topography data were collected for each of the subjects.…”

Section: Data Acquisitionmentioning

confidence: 99%

Dynamics of ocular surface topography

Zhu

Collins

Iskander

2006

Eye

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Purpose To investigate fluctuations in the ocular surface, we used high-speed videokeratoscopy (50 Hz) to measure the dynamics of the ocular surface topography. Methods Ocular surface height difference maps were computed to illustrate the changes in the tear film in the inter-blink interval. Topography data were used to derive the ocular surface wavefront aberrations up to the fourth radial order of the Zernike polynomial expansion. We examined the ocular surface dynamics and temporal changes in the ocular surface wavefront aberrations in the inter-blink interval. Results During the first 0.5 s following a blink, the ocular surface height at the upper edge of the topography map increased by about 2 lm. Temporal changes occurred for some ocular surface wavefront aberrations and appeared to be related to changes in the distribution of tear film. Conclusion In the clinical measurement of ocular surface topography using videokeratoscopy or optics of the eye using wavefront sensors, care should be taken to avoid the initial tear film build-up phase following a blink to achieve more consistent results.

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Section: Data Acquisitionmentioning

confidence: 99%

Dynamics of ocular surface topography

Zhu

Collins

Iskander

2006

Eye

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“…These sources were characterised by Clifford (2006) and Friesen et al (1990) for contact ECG data. These authors have characterised these noise sources effects on data exploitation.…”

Section: Signal Processing 221 Noise Removalmentioning

confidence: 99%

Heartbeat biometrics: a sensing system perspective

Israel¹,

Irvine²

2012

IJCB

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This paper reviews the emerging research into exploitation of heartbeat data as a biometric for human identification. A variety of methods have been proposed for acquiring heartbeat signatures and a range of processing methods has been examined. We approach the biometric identification and verification problem by characterising the three major factors affecting performance: individual variants, environmental variants, and sensor variants. The ability to collect and process the signal, exploit the data for individual identification or verification, and disseminate the information depends on all three of these factors. Within each component, we have identified the relevant research. Where possible, we have tied these research papers to practical examples using high resolution ECG data. The research indicates that the heartbeat contains rich information about the individual, their level of anxiety, and the cardiac state.Keywords: biometrics; heartbeat; ECG; sensing system; cardiovascular function; human identification. areas of interest include novel biometrics, signal and image processing, quantification and modelling of data quality, and cognitive modeling. He was a Principal Investigator for DARPA's Human Identification at a Distance Program (HumanID). He serves on planning committees for IEEE and SPIE, and has served on several advisory panels for the Department of Defence and the Department of Energy. He has authored over a hundred journal and conference papers and holds a PhD in Mathematical Statistics from Yale University.

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“…23 All recordings were visually checked and R-wave triggering was manually corrected if necessary by one of the authors (B. F.). All NN intervals during the 5-min recording period were accepted for computing ventricular heart rate variability.…”

Section: Analysis Of Heart Rate Variability During Atrial Fibrillationmentioning

confidence: 99%

Irregularity of the ventricular rhythm during atrial fibrillation: Effect of slow atrioventricular nodal pathway ablation

Frey

Kreiner

Liebisch

et al. 1999

Clinical Cardiology

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SummaryBackground: The contribution of dual atrioventricular (AV) nodal pathway physiology to the irregularity of the ventricular rhythm during atrial fibrillation has not been clarified.Hypothesis: This study was performed to assess the effects of slow AV nodal pathway ablation on the irregularity of the ventricular rhythm during atrial fibrillation.Methods: Irregularity of the ventricularrhythm was quantified using analysis of heart rate variability. In 20 patients with AV nodal reentrant tachycardia, absolute heart rate variability during atrial fibrillation was quantified before and after slow AV nodal pathway ablation by the standard deviation of all NN intervals (SDNN). Relative heart rate variability was determined by computing the coefficient of variation, SDNN normalized for the standard deviation of the mean ventricular cycle length (MVCL-AF).Results: The slope of the regression between MVCL-AF and SDNN was significantly more gradual after slow pathway

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A comparison of the noise sensitivity of nine QRS detection algorithms

Cited by 917 publications

References 20 publications

Dynamics of ocular surface topography

Dynamics of ocular surface topography

Heartbeat biometrics: a sensing system perspective

Irregularity of the ventricular rhythm during atrial fibrillation: Effect of slow atrioventricular nodal pathway ablation

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