Metrological characterization and signal processing of a wearable sensor for the measurement of heart rate variability

The aim of using atypicality is to extract small, rare, unusual and interesting pieces out of big data. This complements statistics about typical data to give insight into data. In order to find such “interesting” parts of data, universal approaches are required, since it is not known in advance what we are looking for. We therefore base the atypicality criterion on codelength. In a prior paper we developed the methodology for discrete-valued data, and the current paper extends this to real-valued data. This is done by using minimum description length (MDL). We develop the information-theoretic methodology for a number of “universal” signal processing models, and finally apply them to recorded hydrophone data and heart rate variability (HRV) signal.

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A nonlinear signal processing approach to model heart rate variability

Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, 2004. ◽

10.1109/isspit.2004.1433689 ◽

2005 ◽

Cited By ~ 2

Author(s):

N.J. Dabanloo ◽

D.C. McLernon ◽

A. Ayatollabi ◽

V.J. Majd

Keyword(s):

Heart Rate ◽

Signal Processing ◽

Heart Rate Variability ◽

Nonlinear Signal Processing ◽

Processing Approach ◽

Nonlinear Signal

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A Hybrid Signal Processing of RR Intervals from QTc Variation Searching Arrhythmia and Improving Heart Rate Variability Assessment in Acute Large Artery Ischemic Stroke

Journal of Medical Engineering ◽

10.1155/2016/9823026 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6

Author(s):

S. Rangsungnoen ◽

P. Chanbenjapipu ◽

N. Mathuradavong ◽

K. Suwanprasert

Keyword(s):

Heart Rate ◽

Signal Processing ◽

Heart Rate Variability ◽

Acute Stroke ◽

Sample Entropy ◽

Scaling Exponent ◽

Large Artery ◽

Qrs Detection ◽

Poincaré Plot ◽

Mean Heart Rate

Sudden death caused by abnormal QTc and atrial fibrillation (AF) has been reported in stroke. Heart rate variability (HRV) is reduced with missing beats of RRI during arrhythmic episode and abnormal QTc variation during acute stroke. In this study, we develop a hybrid signal processing by Pan Tompkins QRS detection and Kalman filter estimator for meaningful missing beats and searching AF with prolonged QTc. We use this hybrid model to investigate RRIs of Lead II ECG in thirty acute stroke patients with long QTc and AF (LQTc-AF) and normal QTc without AF (NQTc-nonAF) and then assess them by HRV. In LQTc-AF Kalman, higher mean heart rate with lower mean RRIs compared to NQTc-nonAF Kalman was characterized. LQTc-AF Kalman showed significant increase in SDNN, HF, SD2, SD2/SD1, and sample entropy. SDNN and HF associated with high RMSSD, pNN50, and SD1 reflect predominant parasympathetic drive for sympathovagal balance in LQTc-AF Kalman. Greater SD2, SD2/SD1, and sample entropy indicate more scatter of Poincaré plot. Compared with conventional Labchart, fractal scaling exponent of α1 (DFA) is higher in LQTc-AF Kalman. Remarkable complexity with parasympathetic drive in LQTc-AF Kalman suggests an influence of missing beats during stroke.

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