Automatic Actigraphy Data Annotation for Sleep Disorders Diagnosis at IEEE Conference
Explore how automatic actigraphy data annotation can aid in diagnosing sleep disorders, presented at the IEEE Engineering in Medicine and Biology Society conference. Learn about the motivations, challenges, and potential benefits of using actigraphy for monitoring sleep health.
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32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes Alexandre Domingues1, Ondrej Adamec1,2, Teresa Paiva3and J. Miguel Sanches1 1 Institute for Systems and Robotics, Instituto Superior T cnico, Lisbon, Portugal 2Faculty of Electrical Engineering and Computer Science, VSB TU Ostrava, Czech Republic 3Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Structure Motivation Actigraphy Data sets Data classification Results Conclusions 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Motivation Sleep disorders Affect a significative percentage of young and adult population. Can be related with diabetes, obesity, depression and cardiovascular diseases. Diagnosis involves complex and intrusive procedures. Many individuals never seek medical care. 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Motivation Diagnosis Standard procedure: Polysomnography Several physiological signals are acquired (EEG, EOG, EMG, Oxymetry,etc ) Data is acquired in a controled and reliable environment 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Motivation Diagnosis PSG - Difficulties Performed in medical facilities The exam is highly intrusive. Long term monitoring is not feasible Data processing is done offline and is a time consuming process. Faster, low cost and Portable methods are needed! 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Actigraphy Non invasive and portable sensor. Low cost solution to gather valuable information Allows for long term monitoring. Automatic determination of the Sleep / Wakefulness state 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Data sets Collected from 23 healthy subjects for a period of 14 days. Segmented by trained technicians with the help of light information and a sleep diary. Data segments grouped into two large sleep and wakefulness arrays. 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Statistical properties extraction 11 1p Two large arrays of Sleep/Wakefulness data 21 2p Time 31 3p W overlapping windows w1 wp , , , , C C w w p order autoregressive model ( = parameters) s w s w s w 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Bayes classifier Clouds of parameters of each class described by a Gaussian distribution New set of data Classification into wakefulness or sleep state Each class (ws, ww) described by a pair of features: w s , , Bayes classifier C , C s w = / ) f f ( | ( | ) p p s w 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Results Performance of the estimator Leave-one-out Cross-validation: Accuracy 96% Sensitivity 98% Specificity 74% 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Results Distribution of the coefficients obtained with a 2nd order autoregressive model 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Results Bayes factor for 2 days/nights 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Results Detection of a wakening episode 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
Conclusions Classification procedure with real actigraphy data. Based on autoregressive (AR) models Bayes classifier with 96% accuracy. Future work More physiological data, e.g., light, position, oximetry, and temperature. Step toward an alternative method in the diagnosis of some sleep disorders involving long term monitoring Light Polysonmography (LPSG) 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Thank You 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Automatic Annotation of Actigraphy Data for Sleep Disorders Diagnosis Purposes
