Robust interbeat interval and heart rate variability estimation method from various morphological features using wearable sensors

Ayca Aygun, Hassan Ghasemzadeh, Roozbeh Jafari

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We introduce a novel approach for robust estimation of physiological parameters such as interbeat interval (IBI) and heart rate variability (HRV) from cardiac signals captured with wearable sensors in the presence of motion artifacts. Motion artifact due to physical exercise is known as a major source of noise that contributes to a significant decline in the performance of IBI and HRV estimation techniques for cardiac monitoring in free-living environments. Therefore, developing robust estimation algorithms is essential for utilization of wearable sensors in daily life situations. The proposed approach includes two algorithmic components. First, we propose a combinatorial technique to select characteristic points that define heartbeats in noisy signals in time domain. The heartbeat detection problem is defined as a shortest path search problem on a direct acyclic graph that leverages morphological features of the cardiac signals by taking advantage of the time-continuity of heartbeats - each heartbeat ends with the starting point of the next heartbeat. The graph is constructed with vertices and edges representing candidate morphological features and IBIs, respectively. Second, we propose a fusion technique to combine physiological parameters estimated from different morphological features using the shortest path algorithm to obtain more accurate IBI/HRV estimations. We evaluate our techniques on motion-corrupted photoplethysmogram and electrocardiogram signals. Our results indicate that the estimated IBIs are highly correlated with the ground truth (r = 0.89) and detected HRV parameters indicate high correlation with the true HRV parameters. Furthermore, our findings demonstrate that the developed fusion technique, which utilizes different morphological features, achieves a correlation coefficient that is at least 3% higher than that obtained using single physiological characteristic.

Original languageEnglish (US)
Article number8943951
Pages (from-to)2238-2250
Number of pages13
JournalIEEE Journal of Biomedical and Health Informatics
Volume24
Issue number8
DOIs
StatePublished - Aug 2020
Externally publishedYes

Keywords

  • Heart rate
  • heart rate variability (HRV)
  • interbeat interval (IBI)
  • motion artifacts
  • physiological signal processing
  • wearable sensors

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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