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Automatic annotation of actigraphy data for sleep disorders diagnosis purposes

A. Domingues, O. Adamec, T. Paiva, J. M. Sanches

. 2010 ; 2010 () : 5081-5084.

Language English Country United States

Document type Journal Article, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc12026625

The diagnosis of Sleep disorders, highly prevalent in the western countries, typically involves sophisticated procedures and equipments that are intrusive to the patient. Wrist actigraphy, on the contrary, is a non-invasive and low cost solution to gather data which can provide valuable information in the diagnosis of these disorders. The acquired data may be used to infer the Sleep/Wakefulness (SW) state of the patient during the circadian cycle and detect abnormal behavioral patterns associated with these disorders. In this paper a classifier based on Autoregressive (AR) model coefficients, among other features, is proposed to estimate the SW state. The real data, acquired from 23 healthy subjects during fourteen days each, was segmented by expert medical personal with the help of complementary information such as light intensity and Sleep e-Diary information. Monte Carlo tests with a Leave-One-Out Cross Validation (LOOCV) strategy were used to assess the performance of the classifier which achieves an accuracy of 96%.

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$a The diagnosis of Sleep disorders, highly prevalent in the western countries, typically involves sophisticated procedures and equipments that are intrusive to the patient. Wrist actigraphy, on the contrary, is a non-invasive and low cost solution to gather data which can provide valuable information in the diagnosis of these disorders. The acquired data may be used to infer the Sleep/Wakefulness (SW) state of the patient during the circadian cycle and detect abnormal behavioral patterns associated with these disorders. In this paper a classifier based on Autoregressive (AR) model coefficients, among other features, is proposed to estimate the SW state. The real data, acquired from 23 healthy subjects during fourteen days each, was segmented by expert medical personal with the help of complementary information such as light intensity and Sleep e-Diary information. Monte Carlo tests with a Leave-One-Out Cross Validation (LOOCV) strategy were used to assess the performance of the classifier which achieves an accuracy of 96%.
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