A new nonlinear model for generating RR tachograms

McLernon, D.C.; Dabanloo, Nader Jafarnia; Ayatollahi, Ahmad; Majd, Vahid Johari; Zhang, H.

doi:10.1109/cic.2004.1442979

Cited by 9 publications

(9 citation statements)

References 3 publications

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“…This method has been chosen because by means of changing input of IPFM model, we can produce synthetic HRV signal which is similar to the natural signal (the one taken from human body), and we can also model several types of diseases that show their effects on the signal. Considering that this model is capable of producing several types of HRV signals it can be concluded that it is a comprehensive model for synthesizing this type of signal (with comparison to results of references [1] [2] [10]). So this study can be considered as an applied study in terms of its type and method.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the input S 1 produces VLF component in HRV signal. As we know, the VLF is related to peripheral vascular and mechanisms of temperature regulation that have been produced by the sympathetic nervous system [10]. Input P 1 and S 2 respectively can determine the effectiveness of parasympathetic and sympathetic mechanisms that are related to the pressure sensor (baroreceptory) that appear in the LF (or Mayer) [2].…”

Section: S Smentioning

confidence: 99%

“…ANS has two subsystems, sympathetic nerves and parasympathetic nerves. The HR may be increased by sympathetic activity or decreased by parasympathetic activity [10]. In this study, a mathematical model was proposed to generate artificial HRV signals.…”

Section: Ipfm Model Used In Proposed Modelmentioning

confidence: 99%

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New Modeling for Generation of Normal and Abnormal Heart Rate Variability Signals

Safdarian¹

2014

JBiSE

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Section: Discussionmentioning

confidence: 99%

Section: S Smentioning

confidence: 99%

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New Modeling for Generation of Normal and Abnormal Heart Rate Variability Signals

Safdarian¹

2014

JBiSE

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“…The Autonomic Nerve System (ANS) is responsible for short-term regulation of the blood pressure [3]. The ANS is a part of the Central Nervous System (CNS).…”

Section: Heart Rate Variability Abd Autonomic Nervous Systemmentioning

confidence: 99%

Emotion Recognition based on Utilizing Occurrence Sequence of Heart Rate's Phase Space Points

Dabanloo

Attarodi

Moharreri

et al. 2012

Biomedical Engineering / 765: Telehealth / 766: Assistive Technologies

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In this paper, we have used the color stimuli to define the valence level of subjects by analyzing their heart rate variability (HRV). For this analysis, we have used the novel features, Global Occurrence Matrix (GOM) and Co-Occurrence Matrix (COM), and try to distinguish two groups of positive and negative emotions. The results show that cold colors are associated with high valence level and hot colors are in relation with low valence level.

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“…Heart Rate Variability (HRV) is the variation in the time series of RR intervals [1] and is an indicator of heart's condition [2]. It has been proved that nonlinear analysis of HRV provides more valuable information for the physiological interpretation of heart rate fluctuations compared to linear HRV measures [3].…”

Section: Introductionmentioning

confidence: 99%

New Feature Set for Better Representation of Dynamic of RR Intervals in Poincare Plot ?

Moharreri

Dabanloo

Rezaei

et al. 2017

Computing in Cardiology Conference (CinC)

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Traditional features extracted from Poincare plot (e.g., SD1 and SD2) IntroductionHeart Rate Variability (HRV) is the variation in the time series of RR intervals [1] and is an indicator of heart's condition [2]. It has been proved that nonlinear analysis of HRV provides more valuable information for the physiological interpretation of heart rate fluctuations compared to linear HRV measures [3]. One of the useful nonlinear analysis of HRV is Poincare plot, plot of each RR interval against next RR interval [4]. Woo et al. were the first group who used Poincare plot for evaluating the differences between healthy subjects and heart failure patients [5]. Poincare plot is a geometrical representation of RR time series to demonstrate patterns of heart rate dynamics resulting from nonlinear processes [6]. Tulppo et al. [7] fitted an ellipse to the point distribution in the Poincare plot and defined two standard descriptors (SD1 and SD2) for quantification of the Poincare plot geometry. These standard descriptors represent the minor axis and the major axis of the ellipse and guide the visual inspection of the distribution. Brennan et al. proved that although Poincare plot is a nonlinear representation of RR intervals, but SD1 and SD2 cannot describe these nonlinear behavior and are linear and statistic parameters [8]. Furthermore, these descriptors ignore temporal information and only quantify point distribution [9].In addition to geometric features that was proposed by our team previously [10-13], a new feature set is proposed in this article to capture the temporal information in a Poincare plot by considering the angle between consecutive points, the direction of the trajectory, and the position of points in relation to the line of identity. These features were used to create ADP Map (angle, direction, and position map) for a new representation of RR points.Performance of proposed feature set was evaluated in distinguishing four cardiac condition including normal sinus rhythm (NSR), acute myocardial infarction (MI), congestive heart failure (CHF), and atrial fibrillation (AF). 2.Method and Data MethodFor extraction of the new feature set and creating new map, quantification of temporal information/dynamics of the points in Poincare plot were the focus of this article. For capturing dynamic information, three features were calculated between every three consecutive points in Poincare plot: angle, direction of the trajectory, and the location of middle point in relation to the line of identity. For visualization purpose, these features were used to create a 3D map (ADP map).

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A new nonlinear model for generating RR tachograms

Cited by 9 publications

References 3 publications

New Modeling for Generation of Normal and Abnormal Heart Rate Variability Signals

New Modeling for Generation of Normal and Abnormal Heart Rate Variability Signals

Emotion Recognition based on Utilizing Occurrence Sequence of Heart Rate's Phase Space Points

New Feature Set for Better Representation of Dynamic of RR Intervals in Poincare Plot ?

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