5,198 research outputs found
How random is your heart beat?
We measure the content of random uncorrelated noise in heart rate variability
using a general method of noise level estimation using a coarse grained
entropy. We show that usually - except for atrial fibrillation - the level of
such noise is within 5 - 15% of the variance of the data and that the
variability due to the linearly correlated processes is dominant in all cases
analysed but atrial fibrillation. The nonlinear deterministic content of heart
rate variability remains significant and may not be ignored.Comment: see http://urbanowicz.org.p
Nonlinear trend removal should be carefully performed in heart rate variability analysis
Background : In Heart rate variability analysis, the rate-rate time
series suffer often from aperiodic non-stationarity, presence of ectopic beats
etc. It would be hard to extract helpful information from the original signals.
10 Problem : Trend removal methods are commonly practiced to reduce
the influence of the low frequency and aperiodic non-stationary in RR data.
This can unfortunately affect the signal and make the analysis on detrended
data less appropriate. Objective : Investigate the detrending effect
(linear \& nonlinear) in temporal / nonliear analysis of heart rate variability
of long-term RR data (in normal sinus rhythm, atrial fibrillation, 15
congestive heart failure and ventricular premature arrhythmia conditions).
Methods : Temporal method : standard measure SDNN; Nonlinear methods
: multi-scale Fractal Dimension (FD), Detrended Fluctuation Analysis (DFA) \&
Sample Entropy (Sam-pEn) analysis. Results : The linear detrending
affects little the global characteristics of the RR data, either 20 in temporal
analysis or in nonlinear complexity analysis. After linear detrending, the
SDNNs are just slightly shifted and all distributions are well preserved. The
cross-scale complexity remained almost the same as the ones for original RR
data or correlated. Nonlinear detrending changed not only the SDNNs
distribution, but also the order among different types of RR data. After this
processing, the SDNN became indistinguishable be-25 tween SDNN for normal sinus
rhythm and ventricular premature beats. Different RR data has different
complexity signature. Nonlinear detrending made the all RR data to be similar ,
in terms of complexity. It is thus impossible to distinguish them. The FD
showed that nonlinearly detrended RR data has a dimension close to 2, the
exponent from DFA is close to zero and SampEn is larger than 1.5 -- these
complexity values are very close to those for 30 random signal.
Conclusions : Pre-processing by linear detrending can be performed on RR data,
which has little influence on the corresponding analysis. Nonlinear detrending
could be harmful and it is not advisable to use this type of pre-processing.
Exceptions do exist, but only combined with other appropriate techniques to
avoid complete change of the signal's intrinsic dynamics. 35 Keywords
heart rate variability linear / nonlinear detrending
complexity analysis mul-tiscale analysis detrended
fluctuation analysis fractal dimension sample entropy
Using skewness and the first-digit phenomenon to identify dynamical transitions in cardiac models
Disruptions in the normal rhythmic functioning of the heart, termed as
arrhythmia, often result from qualitative changes in the excitation dynamics of
the organ. The transitions between different types of arrhythmia are
accompanied by alterations in the spatiotemporal pattern of electrical activity
that can be measured by observing the time-intervals between successive
excitations of different regions of the cardiac tissue. Using biophysically
detailed models of cardiac activity we show that the distribution of these
time-intervals exhibit a systematic change in their skewness during such
dynamical transitions. Further, the leading digits of the normalized intervals
appear to fit Benford's law better at these transition points. This raises the
possibility of using these observations to design a clinical indicator for
identifying changes in the nature of arrhythmia. More importantly, our results
reveal an intriguing relation between the changing skewness of a distribution
and its agreement with Benford's law, both of which have been independently
proposed earlier as indicators of regime shift in dynamical systems.Comment: 11 pages, 6 figures; incorporating changes as in the published
versio
Non-Linear Heart Rate Variability and Risk Stratification in Cardiovascular Disease
Traditional time and frequency domain heart rate variability (HRV) have cardiac patients at risk of mortality post-myocardial infarction. More recently, non linear HRV has been applied to risk stratification of cardiac patients. In this review we describe studies of non linear HRV and outcome in cardiac patients. We have included studies that used the three most common non-linear indices: power law slope, the short term fractal scaling exponent and measures based on Poincaré plots. We suggest that a combination of traditional and non-linear HRV may be optimal for risk stratification. Considerations in using non linear HRV in a clinical setting are described
User-initialized active contour segmentation and golden-angle real-time cardiovascular magnetic resonance enable accurate assessment of LV function in patients with sinus rhythm and arrhythmias.
BackgroundData obtained during arrhythmia is retained in real-time cardiovascular magnetic resonance (rt-CMR), but there is limited and inconsistent evidence to show that rt-CMR can accurately assess beat-to-beat variation in left ventricular (LV) function or during an arrhythmia.MethodsMulti-slice, short axis cine and real-time golden-angle radial CMR data was collected in 22 clinical patients (18 in sinus rhythm and 4 patients with arrhythmia). A user-initialized active contour segmentation (ACS) software was validated via comparison to manual segmentation on clinically accepted software. For each image in the 2D acquisitions, slice volume was calculated and global LV volumes were estimated via summation across the LV using multiple slices. Real-time imaging data was reconstructed using different image exposure times and frame rates to evaluate the effect of temporal resolution on measured function in each slice via ACS. Finally, global volumetric function of ectopic and non-ectopic beats was measured using ACS in patients with arrhythmias.ResultsACS provides global LV volume measurements that are not significantly different from manual quantification of retrospectively gated cine images in sinus rhythm patients. With an exposure time of 95.2Â ms and a frame rate ofâ>â89 frames per second, golden-angle real-time imaging accurately captures hemodynamic function over a range of patient heart rates. In four patients with frequent ectopic contractions, initial quantification of the impact of ectopic beats on hemodynamic function was demonstrated.ConclusionUser-initialized active contours and golden-angle real-time radial CMR can be used to determine time-varying LV function in patients. These methods will be very useful for the assessment of LV function in patients with frequent arrhythmias
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
The electrocardiogram (ECG) is one of the most extensively employed signals
used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG
signals can capture the heart's rhythmic irregularities, commonly known as
arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of
patients' acute and chronic heart conditions. In this study, we propose a
two-dimensional (2-D) convolutional neural network (CNN) model for the
classification of ECG signals into eight classes; namely, normal beat,
premature ventricular contraction beat, paced beat, right bundle branch block
beat, left bundle branch block beat, atrial premature contraction beat,
ventricular flutter wave beat, and ventricular escape beat. The one-dimensional
ECG time series signals are transformed into 2-D spectrograms through
short-time Fourier transform. The 2-D CNN model consisting of four
convolutional layers and four pooling layers is designed for extracting robust
features from the input spectrograms. Our proposed methodology is evaluated on
a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art
average classification accuracy of 99.11\%, which is better than those of
recently reported results in classifying similar types of arrhythmias. The
performance is significant in other indices as well, including sensitivity and
specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote
Sensing MDPI Journa
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