S
Shaun Purcell
Researcher at Brigham and Women's Hospital
Publications - 347
Citations - 151651
Shaun Purcell is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Genome-wide association study & Population. The author has an hindex of 120, co-authored 326 publications receiving 132973 citations. Previous affiliations of Shaun Purcell include Icahn School of Medicine at Mount Sinai & University of Saint Mary.
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Journal ArticleDOI
0033 Genome-wide association analysis of composite sleep scores in 413,904 individuals
Matthew Goodman,Jacqueline M. Lane,Hassan S. Dashti,Joo Yeon Chung,Tamar Sofer,Shaun Purcell,Xiaofeng Zhu,Martin K. Rutter,Susan Redline,Richa Saxena,Heming Wang +10 more
TL;DR: This article constructed an additive sleep score (SS-add) as a sum of up to five favorable self-reported sleep behaviors (sleep duration of 7-8 hours, early chronotype, few insomnia symptoms, no snoring, and no excessive daytime sleepiness) using the underlying sleep traits.
Journal ArticleDOI
0031 Whole genome sequence analyses for Excessive Daytime Sleepiness in the NHLBI TOPMed Program
Nuzulul Kurniansyah,Han Chen,Shaun Purcell,Richa Saxena,Xiaofeng Zhu,Brian E. Cade,Tamar Sofer,Susan Redline,Heming Wang +8 more
TL;DR: The first whole-genome sequence (WGS) analyses for Epworth Sleepiness Scale (ESS) in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program were performed in this paper .
Journal ArticleDOI
AI-Driven sleep staging from actigraphy and heart rate
TzuAn Song,Samadrita Roy Chowdhury,Masoud Malekzadeh,Stephanie L. Harrison,Susan Redline,Katie L. Stone,Richa Saxena,Shaun Purcell,Joyita Dutta +8 more
TL;DR: In this paper , a sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS) was proposed to predict the duration of each sleep stage with high accuracy.
Posted ContentDOI
A transformer model for predicting cognitive impairment from sleep
TL;DR: This work uses data from N = 1, 502 subjects from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort and presents a deep learning approach based on a transformer architecture to predict cognitive status from sleep electroencephalography (EEG) data, which is the first effort to use deep learning to predict Cognitive impairment from sleep metrics.
Journal ArticleDOI
Sleep Architecture, Obstructive Sleep Apnea, and Cognitive Function in Adults
Matthew P. Pase,Stephanie L. Harrison,Jeffrey R. Misialek,Christopher J. Klein,Marina G. Cavuoto,Andrée-Ann Baril,Stephanie Yiallourou,A.N. Bisson,Dibya Himali,Yue Leng,Qiong Yang,Sudha Seshadri,Alexa B Beiser,Rebecca F. Gottesman,Susan Redline,Oscar L. Lopez,Pamela L. Lutsey,Kristine Yaffe,Katie L. Stone,Shaun Purcell,Jayandra J. Himali +20 more
TL;DR: In this paper , the association of sleep architecture and obstructive sleep apnea measures with cognitive function among middle-aged to older adults in 5 cohorts was investigated in a cohort study.