METHOD AND SYSTEM FOR SLEEP/WAKE CONDITION ESTIMATION

• US 20110034811A1
• Filed: 04/09/2009
• Published: 02/10/2011
• Est. Priority Date: 04/16/2008
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips N.V.
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Kremer, Frederique, Naujokat, Elke, Devot, Sandrine Magali Laure, Aubert, Xavier Louis Marie Antoine
• Application Number:
  US12/937,392
• Publication Number:
  US 20110034811A1
• Time in Patent Office: Days
• US Class Current: 600/484
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/6804 Garments; Clothes
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Insomnia is a prevalent sleep disturbance in the general population. As standard diagnostic method for assessing the nature and the severity of the sleep problem, a so-called sleep log or sleep diary, i.e. a questionnaire usually on paper, is used in most cases. The main drawback of this diagnostic tool is that its accuracy is affected by subjective bias of the patient, e.g. for patients it is often difficult to remember sleep and wake periods during the night correctly. The present invention proposes an automatic sleep log that uses vital body signs as input signals for assessing sleep and wake periods during the night. By using objective data, the diagnosis will be more accurate. Furthermore, this system can also be used to sleep restriction therapy, a non-pharmacological method to treat insomnia. Also in this application it can contribute to patients correctly applying this method and thus leading to a better medical outcome.

Claims

1. Arrangement for the identification of sleep and wake conditions of a subject, comprising

at least one sensor, which is integrated in the sleeping environment of the subject, the sensor is provided to generate subject related signals, and

calculation means, provided

to receive the sensor signals,

to extract at least one feature from these sensor signals,

to classify the extracted feature, and

to provide a probability indication whether the subject is awake or asleep.

2. Arrangement according to claim 1, wherein the at least one sensor is an ECG sensor, and/or a bed foil sensor sensing signals, which are related to the subject'"'"'s body movements.

3. The arrangement according to claim 2, wherein the extracting of at least one feature from at least one measured signal derived from the sensor measuring the ECG signal and the bed foil sensor, which senses the body movements includes the cardio-respiratory condition and the coherent power spectrum calculated from both the ECG signal and bed foil sensor respiratory signal.

4. The arrangement according to claim 1, wherein the sensor is an ECG sensor and an ECG signal provided by the ECG sensor undergoes a pre-processing, wherein the preprocessing includes a R-peak detection, ectopic beat removal, linear interpolation and resampling to predefined frequency, and resulting in a RR-interval series.

5. The arrangement according to claim 4, wherein the RR-interval series is further processed using heart rate variability standards in the frequency and in the time domain.

6. The arrangement according to claim 1, wherein a power spectrum is calculated over a predefined first time duration, centred on a second predefined time duration epoch of interest, using a detrending model.

7. The arrangement according to claim 6, wherein the power spectrum is divided into a low-frequency band and a high frequency band and wherein the power spectrum is normalised.

8. according to claim 2, wherein an RR-interval and a breath interval series are combined by estimating the squared coherence function over predefined first time duration centred on a second predefined time duration epoch.

9. The arrangement according to claim 1, wherein the at least one feature form the components of a feature vector for classifying.

10. according to claim 9, wherein the feature classification is based on a standard pattern recognition approach with supervised learning, and wherein the classifier includes

Bayesian linear or quadratic discriminant classifier,

support vector machine,

k-Nearest-Neighbour (kNN) method,

Neural Network,

Hidden Markov Model (HMM), and

wherein the parameters of the classifier are trained on a large database of representative data.

11. A Method for the identification of sleep and wake conditions of a subject comprising the following steps:

placing a sensor integrated in a sleeping environment, which is provided to generate subject related signals,

receiving the sensor signals,

extracting at least one feature from these sensor signals,

classifying the extracted feature, and

providing a probability indication whether the subject is awake or asleep.

12. Method according to claim 11, wherein the at least one sensor signal in the sleeping environment is obtained from a ECG sensor and/or bed foil sensor or any sensor providing a sensing of the cardio-respiratory status of the subject.

13. Method according to claim 11, wherein the classification is based on a data training vector set and the data may be fed back into the classifier, and additionally training data may be provided from or compared to questionnaire data.

14. Method according to claim 11, wherein the steps sensing and of the calculation including reception, extraction, and classifying can run automatically, and can be scheduled repeatedly, parallel or serially

15. A computer readable medium having a program code embodied therein which when executed causes a processor to perform the method of claim 11.

16. (canceled)