- Title
- A count data model for heart rate variability forecasting and premature ventricular contraction detection
- Creator
- Allami, Ragheed; Stranieri, Andrew; Balasubramanian, Venki; Jelinek, Herbert
- Date
- 2017
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165240
- Identifier
- vital:13196
- Identifier
-
https://doi.org/10.1007/s11760-017-1103-x
- Identifier
- ISBN:1863-1703
- Abstract
- Heart rate variability (HRV) measures including the standard deviation of inter-beat variations (SDNN) require at least 5 min of ECG recordings to accurately measure HRV. In this paper, we predict, using counts data derived from a 3-min ECG recording, the 5-min SDNN and also detect premature ventricular contraction (PVC) beats with a high degree of accuracy. The approach uses counts data combined with a Poisson-generated function that requires minimal computational resources and is well suited to remote patient monitoring with wearable sensors that have limited power, storage and processing capacity. The ease of use and accuracy of the algorithm provide opportunity for accurate assessment of HRV and reduce the time taken to review patients in real time. The PVC beat detection is implemented using the same count data model together with knowledge-based rules derived from clinical knowledge.
- Publisher
- Springer-Verlag
- Relation
- Signal Image and Video Processing Vol. 11, no. 8 (2017), p. 1427-1435
- Rights
- Copyright © Springer-Verlag London 2017
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0906 Electrical and Electronic Engineering; Heart rate variability forecasting; RR interval; SDNN; Premature ventricular contraction
- Full Text
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