Development of a Matlab software for analysis of heart rate variability

Carvalho, João L. A.; Rocha, Adson Ferreira da; Nascimento, Francisco Assis de Oliveira; Neto, João Souza; Junqueira, Luiz Fernando

doi:10.1109/icosp.2002.1180076

Cited by 73 publications

(52 citation statements)

References 4 publications

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“…Most of the nonlinear features applied in HRV research were implemented in the framework. Nonlinear, phase space Sequential trend analysis [12] Nonlinear, phase space Detrended fluctuation analysis (DFA) α 1 , α 2 [25] Nonlinear, fractal estimates Hurst exponent [6] Nonlinear, fractal estimates Higuchi's fractal dimension [11] Nonlinear, fractal estimates kmax Approximate entropy (ApEn) [26] Nonlinear, entropy Embedding dimension, radius, maximum (Y/N) Sample entropy (SampEn) [27] Nonlinear, entropy Embedding dimension, radius, maximum (Y/N) Multiscale sample entropy [28] Nonlinear, entropy Scale, embedding dimension, radius, maximum (Y/N) Rényi entropy [21] Nonlinear, entropy Order Spectral entropy [8] Nonlinear, entropy Frequency band selection Multiscale asymmetry index [29] Nonlinear, other Scale…”

Section: Feature Calculationmentioning

confidence: 99%

“…The implementation of HRV toolkit named ECGLab by authors [12] allows biomedical engineers to explore and analyze cardiac rhythm in Matlab environment. The first version of the software contained routines for R peak detection out of the ECG record as well as for loading of already prepared ASCII files that contain RR intervals.…”

Section: Comparison To Similar Workmentioning

confidence: 99%

“…However, there are several frameworks and programs known to us at the researchers' disposal that implement some of the features used in the HRV analysis. These include: ECGLab [12], KARDIA [13], BioSig project [14], and HRV Analysis Software [15].…”

Section: Introductionmentioning

confidence: 99%

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HRVFrame: Java-Based Framework for Feature Extraction from Cardiac Rhythm

Jović

Bogunović

2011

Artificial Intelligence in Medicine

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Abstract. Heart rate variability (HRV) analysis can be successfully applied to automatic classification of cardiac rhythm abnormalities. This paper presents a novel Java-based computer framework for feature extraction from cardiac rhythms. The framework, simply called HRVFrame, implements more than 30 HRV linear time domain, frequency domain, time-frequency domain, and nonlinear features. Output of the framework in the form of .arff files enables easier medical knowledge discovery via platforms such as RapidMiner or Weka. The scope of the framework facilitates comparison of models for different disorders. The framework can be used for general cardiac disorder analysis as well as for specific cardiac rhythm analysis. Some of the features implemented in the framework can also be applied to other biomedical timeseries. The thorough approach to feature extraction pursued in this work is also encouraged for other types of biomedical time-series.

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Section: Feature Calculationmentioning

confidence: 99%

Section: Comparison To Similar Workmentioning

confidence: 99%

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HRVFrame: Java-Based Framework for Feature Extraction from Cardiac Rhythm

Jović

Bogunović

2011

Artificial Intelligence in Medicine

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“…estimation we must use order 12 in AR model for a sampling rate of 2Hz, and order 15 for a sampling rate of 4Hz (Carvalho J et al, 2003). Fig.…”

Section: The Autonomic Nervous System (Ans) Is a Breach Of The Periphmentioning

confidence: 99%

“…RR signal represents de heart period at discrete points, but this representation is no suitable for FFT analysis as these points are not equally spaced. In order to produce RR data suitable for classical spectral analysis, the RR signal must be interpolated and re-sampled at higher uniform rate, usually at 2 or 4 Hz (Carvalho J et al, 2003). …”

Section: The Autonomic Nervous System (Ans) Is a Breach Of The Periphmentioning

confidence: 99%