On the Accuracy of Heart Rate Variability Measures from Undersampled RR Interval Time Series

2016 ◽  
Author(s):  
Giorgio Quer ◽  
Amr Alasaad ◽  
Ramesh R. Rao
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Interval Time ◽  
Rr Interval ◽  
Interval Time Series

scholarly journals Does the Temporal Asymmetry of Short-Term Heart Rate Variability Change during Regular Walking? A Pilot Study of Healthy Young Subjects

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xinpei Wang ◽  
Chang Yan ◽  
Bo Shi ◽  
Changchun Liu ◽  
Chandan Karmakar ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Short Term ◽  
Area Index ◽  
Temporal Asymmetry ◽  
Interval Time ◽  
Rr Interval ◽  
Rate Asymmetry ◽  
Interval Time Series

The acceleration and deceleration patterns in heartbeat fluctuations distribute asymmetrically, which is known as heart rate asymmetry (HRA). It is hypothesized that HRA reflects the balancing regulation of the sympathetic and parasympathetic nervous systems. This study was designed to examine whether altered autonomic balance during exercise can lead to HRA changes. Sixteen healthy college students were enrolled, and each student undertook two 5-min ECG measurements: one in a resting seated position and another while walking on a treadmill at a regular speed of 5 km/h. The two measurements were conducted in a randomized order, and a 30-min rest was required between them. RR interval time series were extracted from the 5-min ECG data, and HRA (short-term) was estimated using four established metrics, that is, Porta’s index (PI), Guzik’s index (GI), slope index (SI), and area index (AI), from both raw RR interval time series and the time series after wavelet detrending that removes the low-frequency component of <~0.03 Hz. Our pilot data showed a reduced PI but unchanged GI, SI, and AI during walking compared to resting seated position based on the raw data. Based on the wavelet-detrended data, reduced PI, SI, and AI were observed while GI still showed no significant changes. The reduced PI during walking based on both raw and detrended data which suggests less short-term HRA may underline the belief that vagal tone is withdrawn during low-intensity exercise. GI may not be sensitive to short-term HRA. The reduced SI and AI based on detrended data suggest that they may capture both short- and long-term HRA features and that the expected change in short-term HRA is amplified after removing the trend that is supposed to link to long-term component. Further studies with more subjects and longer measurements are warranted to validate our observations and to examine these additional hypotheses.

Testing heart rate asymmetry in long, nonstationary 24 hour RR-interval time series

2019 ◽  
Vol 40 (10) ◽  
pp. 105001 ◽  
Author(s):  
J Piskorski ◽  
J Ellert ◽  
T Krauze ◽  
W Grabowski ◽  
A Wykretowicz ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Interval Time ◽  
Rr Interval ◽  
Rate Asymmetry ◽  
Heart Rate Asymmetry ◽  
Interval Time Series

scholarly journals On the Standardization of Approximate Entropy: Multidimensional Approximate Entropy Index Evaluated on Short-Term HRV Time Series

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Juan Bolea ◽  
Raquel Bailón ◽  
Esther Pueyo
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Healthy Subjects ◽  
Approximate Entropy ◽  
Pink Noise ◽  
Entropy Index ◽  
Rr Interval ◽  
Similarity Threshold ◽  
Interval Time Series ◽  
Night Period

Background. Nonlinear heart rate variability (HRV) indices have extended the description of autonomic nervous system (ANS) regulation of the heart. One of those indices is approximate entropy, ApEn, which has become a commonly used measure of the irregularity of a time series. To calculate ApEn, a priori definition of parameters like the threshold on similarity and the embedding dimension is required, which has been shown to be critical for interpretation of the results. Thus, searching for a parameter-free ApEn-based index could be advantageous for standardizing the use and interpretation of this widely applied entropy measurement. Methods. A novel entropy index called multidimensional approximate entropy, MApEnmax, is proposed based on summing the contribution of maximum approximate entropies over a wide range of embedding dimensions while selecting the similarity threshold leading to maximum ApEn value in each dimension. Synthetic RR interval time series with varying levels of stochasticity, generated by both MIX(P) processes and white/pink noise, were used to validate the properties of the proposed index. Aging and congestive heart failure (CHF) were characterized from RR interval time series of available databases. Results. In synthetic time series, MApEnmax values were proportional to the level of randomness; i.e., MApEnmax increased for higher values of P in generated MIX(P) processes and was larger for white than for pink noise. This result was a consequence of all maximum approximate entropy values being increased for higher levels of randomness in all considered embedding dimensions. This is in contrast to the results obtained for approximate entropies computed with a fixed similarity threshold, which presented inconsistent results for different embedding dimensions. Evaluation of the proposed index on available databases revealed that aging was associated with a notable reduction in MApEnmax values. On the other hand, MApEnmax evaluated during the night period was considerably larger in CHF patients than in healthy subjects. Conclusion. A novel parameter-free multidimensional approximate entropy index, MApEnmax, is proposed and tested over synthetic data to confirm its capacity to represent a range of randomness levels in HRV time series. MApEnmax values are reduced in elderly patients, which may correspond to the reported loss of ANS adaptability in this population segment. Increased MApEnmax values measured in CHF patients versus healthy subjects during the night period point to greater irregularity of heart rate dynamics caused by the disease.

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