PhysioNet: an NIH research resource for complex signals

Costa, Marisa da; Moody, G.B.; Henry, Isaac; Goldberger, Ary L.

doi:10.1016/j.jelectrocard.2003.09.038

Cited by 35 publications

(14 citation statements)

References 19 publications

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“…The RR interval time series were extracted from ECG records using an automatic QRS detector, WQRS available in the PhysioNet's library [17], based on nonlinearly scaled ECG curve length feature [18]. Two scientists independently reviewed and corrected the QRS detection and manually labelled the normal beats obtaining the so called series of normal to normal (NN) beat intervals.…”

Section: Methodsmentioning

confidence: 99%

Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination

Melillo

Bracale

Pecchia

2011

BioMedical Engineering OnLine

267

172

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BackgroundThis study investigates the variations of Heart Rate Variability (HRV) due to a real-life stressor and proposes a classifier based on nonlinear features of HRV for automatic stress detection.Methods42 students volunteered to participate to the study about HRV and stress. For each student, two recordings were performed: one during an on-going university examination, assumed as a real-life stressor, and one after holidays. Nonlinear analysis of HRV was performed by using Poincaré Plot, Approximate Entropy, Correlation dimension, Detrended Fluctuation Analysis, Recurrence Plot. For statistical comparison, we adopted the Wilcoxon Signed Rank test and for development of a classifier we adopted the Linear Discriminant Analysis (LDA).ResultsAlmost all HRV features measuring heart rate complexity were significantly decreased in the stress session. LDA generated a simple classifier based on the two Poincaré Plot parameters and Approximate Entropy, which enables stress detection with a total classification accuracy, a sensitivity and a specificity rate of 90%, 86%, and 95% respectively.ConclusionsThe results of the current study suggest that nonlinear HRV analysis using short term ECG recording could be effective in automatically detecting real-life stress condition, such as a university examination.

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Section: Methodsmentioning

confidence: 99%

Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination

Melillo

Bracale

Pecchia

2011

BioMedical Engineering OnLine

267

172

View full text Add to dashboard Cite

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“…Two QRS onsets detected within 200 ms of each other were assumed to represent the same QRS complex and were treated as such. Automated T wave offsets were determined by using the publicly available ECGPUWAVE 15. For each set of 3 simultaneous leads (I, II, and III, for example), 2 were chosen for analysis by ECGPUWAVE.…”

Section: Methodsmentioning

confidence: 99%

QT dynamics early after exercise as a predictor of mortality

2010

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“…The AF database consists of 25 ECG recordings (10 h in duration) of patients with Paroxysmal AF, whereas the NSR database consists of 18 long-term ECG recordings from healthy subjects who had no significant arrhythmias [39][40][41]. Figure 1 illustrates a typical tracing of RR interval time series for a PAF patient from the MIT-BIH AFDB Database.…”

Section: Datamentioning

confidence: 99%

“…Therefore, here we present a study based on a public ECG database on PhysioNet (http://physionet.org) [39][40][41]. We aim to develop a computerized AF detector based on quantifying the dissimilarity of AF and normal RR-interval time series using an information-based approach.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Paroxysmal Atrial Fibrillation Using an Information-Based Similarity Approach

Cui

Chang

Yang

et al. 2017

Entropy

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Atrial fibrillation (AF) is an abnormal rhythm of the heart, which can increase heart-related complications. Paroxysmal AF episodes occur intermittently with varying duration. Human-based diagnosis of paroxysmal AF with a longer-term electrocardiogram recording is time-consuming. Here we present a fully automated ensemble model for AF episode detection based on RR-interval time series, applying a novel approach of information-based similarity analysis and ensemble scheme. By mapping RR-interval time series to binary symbolic sequences and comparing the rank-frequency patterns of m-bit words, the dissimilarity between AF and normal sinus rhythms (NSR) were quantified. To achieve high detection specificity and sensitivity, and low variance, a weighted variation of bagging with multiple AF and NSR templates was applied. By performing dissimilarity comparisons between unknown RR-interval time series and multiple templates, paroxysmal AF episodes were detected. Based on our results, optimal AF detection parameters are symbolic word length m = 9 and observation window n = 150, achieving 97.04% sensitivity, 97.96% specificity, and 97.78% overall accuracy. Sensitivity, specificity, and overall accuracy vary little despite changes in m and n parameters. This study provides quantitative information to enhance the categorization of AF and normal cardiac rhythms.

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PhysioNet: an NIH research resource for complex signals

Cited by 35 publications

References 19 publications

Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination

Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination

QT dynamics early after exercise as a predictor of mortality

Automated Detection of Paroxysmal Atrial Fibrillation Using an Information-Based Similarity Approach

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