Monitoring system for monitoring a patient and detecting delirium of the patient

1. A monitoring system for automated monitoring of a patient and detecting delirium of the patient, comprising:

a monitoring unit for obtaining image data of the patient over time,

an image analysis unit for detecting motion events of the patient from the obtained image data,

an evaluation unit for classifying the detected motion events into delirium-typical motion events and non-delirium-typical motion events, wherein said evaluation unit is configured to determine high and/or low movement counts of detected motion events, and

a delirium determination unit for determining a delirium score from the duration of high or low movement counts and/or the duration, intensity, type, location and/or occurrence of delirium-typical motion events, said delirium score indicating the likelihood and/or strength of delirium of the patient.

2. The monitoring system as claimed in claim 1 ,

wherein said delirium determination unit is configured to indicate a higher likelihood and/or higher strength of delirium of the patient the longer, more intense and/or more often delirium-typical motion events occur.

3. The monitoring system as claimed in claim 1 ,

wherein said delirium determination unit is configured to determine a delirium type indicator from the duration of high or low movement counts and/or from the duration, intensity, type, location and/or occurrence of delirium-typical motion events, said delirium type indicator indicating the type of delirium including hyperactive delirium, hypoactive delirium and mixed hyperactive and hypoactive delirium.

4. The monitoring system as claimed in claim 3 ,

wherein said delirium determination unit is configured to determine the delirium type indicator to indicate hyperactive delirium if the accumulated duration of hyperactive periods in a first time interval exceeds a first duration threshold and to determine the delirium type indicator to indicate hypoactive delirium if the accumulated duration of hypoactive periods in said first time interval exceeds a second duration threshold.

5. The monitoring system as claimed in claim 1 ,

wherein said delirium determination unit is configured to determine the delirium score to indicate non-delirium if there is a substantial difference of movement counts between day and night time and/or if the movement counts in a predetermined time interval are between a third and fourth movement count threshold.

6. The monitoring system as claimed in claim 1 ,

wherein said monitoring unit is configured to continuously or at regular intervals obtain image data of the patient, in particular to obtain video data of the patient by use of a video camera.

7. The monitoring system as claimed in claim 1 ,

further comprising an information determination unit for determining additional information about the patient from said image data and/or said detected motion events, said additional information including one or more of a mean activity of the patient in a predetermined time interval, detected motion patterns, motion events of predetermined parts of the patient's body and changes and/or trends of the patient's activity.

8. The monitoring system as claimed in claim 1 ,

wherein said delirium determination unit is configured to determine said delirium score by additionally using additional physiological information about the patient, in particular Electroencephalography (EEG) data and/or skin conductance data of the patient.

9. The monitoring system as claimed in claim 8 ,

wherein said delirium determination unit is configured to compare said additional physiological information about the patient with reference physiological information indicative for delirium, in particular to compare Electroencephalography (EEG) data and/or skin conductance data of the patient with typical patterns of Electroencephalography (EEG) data and/or skin conductance data indicative for delirium.

10. The monitoring system as claimed in claim 9 ,

further comprising a storage unit for storing said reference physiological information indicative for delirium, in particular typical patterns of Electroencephalography (EEG) data and/or skin conductance data indicative for delirium.

11. The monitoring system as claimed in claim 8 ,

further comprising a physiological data acquisition unit for acquiring additional physiological information about the patient, in particular Electroencephalography (EEG) data and/or skin conductance data of the patient.

12. A method for monitoring of a patient, comprising:

obtaining image data of the patient over time,

detecting motion events of the patient from the obtained image data,

determining high and/or low movement counts of detected motion events,

classifying the detected motion events into delirium-typical motion events and non-delirium-typical motion events, and

determining a delirium score from the duration of high or low movement counts and/or the duration, intensity, type, location and/or occurrence of delirium-typical motion events, said delirium score indicating the likelihood and/or strength of delirium of the patient.

13. At least one processor for use in an automated monitoring system for monitoring a patient, said at least one processor being programmed to:

detect motion events of the patient from image data obtained over time from the patient,

classify the detected motion events into delirium-typical motion events and non-delirium-typical motion events and for determining high and/or low movement counts of detected motion events, and

determine a delirium score from the duration of high or low movement counts and/or the duration, intensity, type, location and/or occurrence of delirium-typical motion events, said delirium score indicating the likelihood and/or strength of delirium of the patient.

14. A processing method for use in an automated monitoring system for monitoring a patient, said processing method comprising:

detecting motion events of the patient from image data obtained over time from the patient,

classifying the detected motion events into delirium-typical motion events and non-delirium-typical motion events and for determining high and/or low movement counts of detected motion events, and

determining a delirium score from the duration of high or low movement counts and/or the duration, intensity, type, location and/or occurrence of delirium-typical motion events, said delirium score indicating the likelihood and/or strength of delirium of the patient.

15. A non-transitory storage medium storing instructions executable by a computer to carry out the steps of the processing method as claimed in claim 14 when said computer program is carried out on the computer.

16. The at least one processor of claim 13 , further programmed to:

obtain the image data of the patient over time from a camera.

17. The at least one processor of claim 13 , further programmed to:

determine, from the image data and/or the detected motion events, at least one of a mean activity of the patient in a predetermined time interval, detected motion patterns, motion events of predetermined parts of the patient's body and changes and/or trends of the patient's activity.

18. The at least one processor of claim 13 , further programmed to:

determine the delirium score by additionally using at least one of acquired Electroencephalography (EEG) data and skin conductance data of the patient.

19. The at least one processor of claim 13 , further programmed to:

compare the additional physiological information about the patient with at least one of Electroencephalography (EEG) data and skin conductance data of the patient with typical patterns of a corresponding one of Electroencephalography (EEG) data and skin conductance data indicative for delirium.

20. The at least one processor of claim 19 , further programmed to at least one of:

store, in a storage, the typical patterns of Electroencephalography (EEG) data and/or skin conductance data indicative for delirium; and

acquire, with a physiological data acquirer, Electroencephalography (EEG) data and/or skin conductance data of the patient.