Format

Send to

Choose Destination
Nat Sci Sleep. 2017 Feb 16;9:11-29. doi: 10.2147/NSS.S130141. eCollection 2017.

Big data in sleep medicine: prospects and pitfalls in phenotyping.

Author information

1
Neurology Department, Massachusetts General Hospital; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
2
Neurology Department, Massachusetts General Hospital.

Abstract

Clinical polysomnography (PSG) databases are a rich resource in the era of "big data" analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea-hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine.

KEYWORDS:

correlation; plotting; polysomnography; sleep disorders; statistics; subjective symptoms

Conflict of interest statement

Disclosure Dr Matt T Bianchi has received funding from the Department of Neurology, Massachusetts General Hospital, the Center for Integration of Medicine and Innovative Technology, the Milton Family Foundation, the MGH-MIT Grand Challenge, and the American Sleep Medicine Foundation. He has a pending patent on a sleep wearable device, received research funding from MC10 Inc and Insomnisolv Inc, has a consulting agreement with McKesson Health and International Flavors and Fragrances, serves as a medical monitor for Pfizer, and has provided expert testimony in sleep medicine. Dr M Brandon Westover receives funding from NIH-NINDS (1K23NS090900), the Rappaport Foundation, and the Andrew David Heitman Neuroendovascular Research Fund. The authors report no other conflicts of interest in this work.

Supplemental Content

Full text links

Icon for Dove Medical Press Icon for PubMed Central
Loading ...
Support Center