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Clarkson Doctoral Candidate Earns Top-place Finish At International Conference
Heart rate, heart rate variability (HRV), and sleep state are some of the common physiological parameters researchers use when studying infants. Although HRV can be easily derived from infant electrocardiograms (ECG), sleep state scoring is a time consuming task using many physiological signals.
Now Clarkson Ph.D. candidate Aaron T. Lewicke, working with Clarkson Associate Professor Stephanie Schuckers, Clarkson Assistant Professor Edward Sazonov, and Michael Corwin from the pediatrics department at Boston Medical Center may have found a technique to reliably determine sleep and wake in infants using only the ECG. Lewicke presented a paper titled “Reliable Determination of Sleep Versus Wake From Heart Rate Variability Using Neural Networks” at the International Joint Conference on Neural Networks (IJCNN) in Montreal, Canada, recently and received the award for the best Student Poster Paper.
Lewicke’s paper details a simplified method for determining sleep state scores from heart rate variability determined by infant electrocardiograms (ECG).
“At present determining sleep scores takes a great deal of time and requires looking at many physiological signals,” explains Lewicke. “We will test our technique simultaneously with ECG and polysomnograph (PSG) determined sleep scores from the Collaborative Home Infant Monitoring Evaluation (CHIME) study. If successful, our simplified technique will offer many advantages, including high accuracy of results, low intrusiveness, and increased reliability. These are highly desirable testing attributes, especially in highly sensitive groups of patients, such as infants.”
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