Human activity can be measured with actimetry sensors used by the subjects in several locations such as the wrists or legs. Actigraphy data is used in different contexts such as sports training or tele-medicine monitoring. In the diagnosis of sleep disorders, the actimetry sensor, which is basically a 3D axis accelerometer, is used by the patient in the non dominant wrist typically during an entire week. In this paper the actigraphy data is described by a weighted mixture of two distributions where the weight evolves along the day according to the patient circadian cycle. Thus, one of the distributions is mainly associated with the wakefulness state while the other is associated with the sleep state. Actigraphy data, acquired from 20 healthy patients and manually segmented by trained technicians, is used to characterize the acceleration magnitude during sleep and wakefulness states. Several mixture combinations are tested and statistically validated with conformity measures. It is shown that both distributions can co-exist at a certain time with varying importance along the circadian cycle.

• MeSH
• Actigraphy instrumentation   methods   MeSH
• Wakefulness MeSH
• Time Factors MeSH
• Circadian Rhythm MeSH
• Equipment Design MeSH
• Humans MeSH
• Monitoring, Physiologic instrumentation   methods   MeSH
• Computer Communication Networks MeSH
• Sleep MeSH
• Models, Statistical MeSH
• Telemedicine instrumentation   methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

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%.

• MeSH
• Actigraphy methods   MeSH
• Automation MeSH
• Electronic Data Processing methods   MeSH
• Bayes Theorem MeSH
• Wakefulness physiology   MeSH
• Humans MeSH
• Sleep Wake Disorders diagnosis   physiopathology   MeSH
• Reproducibility of Results MeSH
• Sleep physiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH