One-dimensional, nonlinear determinism characterizes  heart rate pattern during paced respiration

The classification of HRV signal through the geometry of Poincaré′ plot and its quantification is one of the popular techniques in nonlinear analysis. The quantification is generally done by fitting an 'ellipse' on the constructed Poincaré plot of HRV signal. However, most of the meditative and pre-meditative states cannot be distinguished from Poincaré′ plot. In this paper we introduce a new notion called modified angle map by which it is possible to classify the meditative and pre-meditative states significantly.

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“…al. [8] is considered. This is done by transforming the points in Poincare′ plot into polar coordinates.…”

Section: B Poincare′ Plot Of the Modified Angle Map And Its Quantifimentioning

confidence: 99%

A new technique for the classification of pre-meditative and meditative states

Dey¹,

Mukherjee²,

Palit

et al. 2011

2011 International Conference on Communication and Industrial Application

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“…In [4], a non linear approach has been proposed in order to analyze R-R interval time series. These authors have noticed that depending of the patient, the clustering of the heartbeat could be either at long or short R-R intervals.…”

Section: Simulations With the Arch Signalmentioning

confidence: 99%

“…The second simulation concerns the ability to produce arch signals observed in real cases. As in [4], we simulate the @ R signal when the condition (7) is not fulfilled. In Fig.…”

Section: Simulations With the Arch Signalmentioning

confidence: 99%

“…The aim of this communication is to compare these two models regarding their ability to mimic the RSA when a real or a synthetic respiration signal is used as the input of the model. It has been shown that the repetitive pattern of the RSA can exhibit different shapes [4] depending on the subjects and experimental conditions. These shapes could be compared to arch or inverted arch and will be designated consequently.…”

Section: Introductionmentioning

confidence: 99%

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Some considerations on the IPFM model for the heart rate variability analysis

Meste

Blain²,

Bermon³

2003

Computers in Cardiology, 2003

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The IntroductionIt is commonly admitted that the IPFM model is a possible model for the generation of the R waves time occurrences. The use of the outputs ¢ ¥ £ s for the frequency analysis of the heart rate variability has been studied in detail in several papers. The latest [1], [2] gives some recommendation in using these outputs in order to reduce or cancel the distortion of the spectrum. The problem of the choice for the model input is not usually addressed and is simplified using a single or a sum of cosines but not a real physiological signal, the respiration for instance. Since the Respiratory Sinus Arrhytmia (RSA) is one of the most important component of the heart rate variability it is straightforward that effort should be made in taking the respiration as an input of the model. In the last CinC we have proposed [3] a model for the generation of the R waves:(1)is an external modulation. It will be shown in the following paper that similarly to the IPFM model a time occurrences generator, called PFM model, can be deduced from the expression (1). The condition for the correct use of the heart period signal defined aswill be also given. The aim of this communication is to compare these two models regarding their ability to mimic the RSA when a real or a synthetic respiration signal is used as the input of the model. It has been shown that the repetitive pattern of the RSA can exhibit different shapes [4] depending on the subjects and experimental conditions. These shapes could be compared to arch or inverted arch and will be designated consequently. Since this shape analysis is a step forward compared to classical frequency analysis we will check whether the PFM or the IPFM can exhibit these patterns under some artificial experimental conditions. We will close this communication presenting a real case. This case compares the outputs obtained from the two models, using the recorded respiration volume as the input, to the measured heart period signal from the real ECG signal. Models definitions and propertiesStarting from the R-waves generator defined by (1) and assuming that the maximum of with. The proposed solution of this equation is:This is an approximated solution in the simple case that the modulating function is a single cosine. It can be shown that it is an acceptable solution when the condition:is verified. From (3) the heart period signal can be calculated byand is finally:We can state that if the model proposed in (1) synthesize a real ECG (R-waves), the deduced @ R S

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“…The law of motion of low dimensional systems can be reconstructed by circle maps ('next angle maps') [2].…”

Section: Introductionmentioning

confidence: 99%

Online cardiac arrhythmia classification by means of circle maps analysis implemented on an intelligent miniaturized sensor

Schiek

Schlösser

Schnitzer

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The intermittent occurrence of cardiac arrhythmias like e.g. atrial fibrillation hampers their diagnosis and hence the treatment. Since persons suffering from atrial fibrillation are known to have a remarkable increased risk of stroke the diagnosis of atrial fibrillation is a matter of great importance. Easy and comfortable to use long term ECG recording systems capable of online arrhythmia classification might help to solve this problem. We developed an intelligent, miniaturized, and wireless networking sensor which allows lossless local data recordings up to 4 GB. With its outer dimensions of 20mm per rim and less than 15g of weight including the Lithium-Ion battery our modular designed sensor node is thoroughly capable of up to eight channel recordings with 8 kHz sample rate each and provides sufficient computational power for online digital signal processing. For online arrhythmia classification we will record one ECG channel and 3-axis accelerometer data with 512 Hz each, the later being used for activity classification based artifact identification. We adapted our recently developed circle maps analysis of short term heart rate variation to run on this miniaturized intelligent sensor powered by the Texas Instruments MSP430 microcontroller derivate F1611. With this configuration we started to evaluate the cardiac arrhythmia classification in long term ECG recordings.

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One-dimensional, nonlinear determinism characterizes heart rate pattern during paced respiration

Cited by 24 publications

References 15 publications

A new technique for the classification of pre-meditative and meditative states

A new technique for the classification of pre-meditative and meditative states

Some considerations on the IPFM model for the heart rate variability analysis

Online cardiac arrhythmia classification by means of circle maps analysis implemented on an intelligent miniaturized sensor

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