Payam Parsinejad
Papers
1
Total Citations
2
H-Index
1
About
Payam Parsinejad is a researcher whose work lies at the intersection of biomedical signal processing and non-invasive health monitoring. His primary research focus is on developing robust computational techniques for extracting clinically meaningful metrics from physiological signals, particularly heart rate variability (HRV) data. Parsinejad’s most notable contribution is the creation of a combined time-frequency technique that enables accurate extraction of the pNN50 metric—a key indicator of parasympathetic nervous system activity—from noisy heart rate measurements. This work, published in 2018, addresses a critical challenge in ambulatory and wearable monitoring, where signal artifacts often compromise data quality. By improving the reliability of pNN50 estimation, his method enhances the utility of HRV analysis for both clinical diagnostics and real-time health tracking. Though his citation count is currently modest, the precision and innovation of his approach mark him as a promising contributor to the field. Parsinejad’s research holds particular relevance for advancing remote patient monitoring and stress assessment technologies, offering a pathway toward more accurate, artifact-resilient physiological metrics in everyday health applications.
Research Focus
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Top Papers
- 1