# US 2010/0168533 A1 · System and a method for generating a quantitative measure reflecting the severity of a medical condition

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- Number: US 2010/0168533 A1
- Document ID: US20100168533A1
- Status: Published application
- Kind: A1
- Application number: 12/663,030
- Filed: 2008-06-09
- Published: 2010-07-01
- Earliest claimed: 2007-06-07
- Assignees: Mentis Cura EHF
- Inventors: Kristinn Johnsen; Steinn Gudmundsson
- Web page: https://patentgrep.com/patent/US20100168533A1

## CPC

- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR

## Abstract

This invention relates to a method and a system for generating a quantitative measure reflecting the severity of a medical condition. A receiver unit receives biosignal data collected from a population of patients having varying degrees of the medical condition. A processor uses the biosignal data for determining reference feature values for each respective patient within the population, where the determining being made in accordance to a pre-defined set of reference features. The processor then assigns each respective patient within the population of patients with a reference feature vector having as vector elements the reference feature values associated with the patient. The processor also uses the reference feature vectors of the patients as input in determining combinations of features describing the variance in the data, where the size of the combinations is an indicator for the severity of the medical condition. This invention further relates to a method and a system for using the quantitative measure for determining a success indicator for a probe compound by implementing the quantitative measure, where a receiver unit receives biosignal data collected from a test subject posterior to administering the probe compound to the test subject, and a processor determines an analogous feature vector as determined for the population of patients. Finally, the processor determines the scalar product between the feature vector determined for the test subject and the combinations of features describing the variance in the data. This scalar product is the success indicator telling how successful the probe compound is.

## Claims

### Claim 1

A system (100) for generating a quantitative measure reflecting the severity of a medical condition, comprising:
a receiver unit (102) for receiving biosignal data collected from a population of M patients, the population being selected such that the patients have varying degrees of a medical condition,
a processor (103) adapted to:
use the biosignal data as input for determining reference feature values for each respective patient within said population, the determining being made in accordance to a pre-defined set of reference features [f1, . . . , fN] and results in reference feature vectors F1 . . . M=[value(f1), value(f2), . . . , value(fN)], the reference feature vectors of the patients subsequently being organized into a M×N matrix A, and
transform the matrix A into uncorrelated linear combinations of the features x1·fs+x2·fp . . . xn·ft where indexes x1 . . . , xn describe the variance in the data and wherein the size of the indexes x1 . . . , xn indicate the severity of the medical condition.

### Claim 2 (depends on 1)

A system according to claim 1, wherein the medical condition is a neurological condition.

### Claim 3 (depends on 2)

A system according to claim 2, wherein the neurological condition is an Alzheimer's type (AD group).

### Claim 4 (depends on 2)

A system according to claim 2, wherein the neurological condition is selected from:
Alzheimer's disease,
multiple sclerosis,
mental conditions including depressive disorders,
bipolar disorder and schizophrenic disorders,
Parkinson's disease,
epilepsy, migraine,
Vascular Dementia (VaD),
Fronto-temporal dementia,
Lewy bodies dementia,
Creutzfeld-Jacob disease and
vCJD (“mad cow” disease), and
AD/HD (Attention Deficit/Hyperactive Disorder)

### Claim 5 (depends on 1)

A system according to claim 1, wherein the receiver is adapted to be coupled to an electroencephalographic (EEG) measuring device (106) and wherein the received data are electroencephalographic (EEG) data.

### Claim 6 (depends on 1)

A system according to claim 1, wherein the receiver is adapted to be coupled to at least one measuring device (106) selected from:
magnetic resonance imaging (MRI),
functional magnetic resonance imaging (FMRI),
magneto-encephalographic (MEG) measurements,
positron emission tomography (PET),
CAT scanning (Computed Axial Tomography),
single photon emission computerized tomography (SPECT),
a combination of one or more of said measuring devices
and wherein the biosignal data are the measuring data from one or more of said devices.

### Claim 7 (depends on 1)

A system according to claim 1, wherein said pre-defined set of reference features is selected from:
the absolute delta power,
the absolute theta power,
the absolute alpha power,
the absolute beta power,
the absolute gamma power,
the relative delta power,
the relative theta power,
the relative alpha power,
the relative beta power,
the relative gamma power,
the total power,
the peak frequency,
the median frequency,
the spectral entropy,
the DFA scaling exponent (alpha band oscillations),
the DFA scaling exponent (beta band oscillations) and
the total entropy.

### Claim 8 (depends on 1)

A system according to claim 1, wherein determining said combinations of features describing said variance in data comprises means for employing principal component analyses (PCA).

### Claim 9

A method of generating a quantitative measure reflecting the severity of a medical condition, comprising:
receiving biosignal data (201) collected from a population of patients having varying degrees of the medical condition,
using the biosignal data (202) as input for determining reference feature values for each respective patient within said population, the determining being made in accordance to a pre-defined set of reference features [f1, . . . , fN] and results in reference feature vectors F1 . . . M=[value(f1), value(f2), . . . , value(fN)], the reference feature vectors of the patients subsequently being organized into a M×N matrix A,
transforming the matrix A into uncorrelated linear combinations of the features x1·fs+x2·fp . . . xn·ft where indexes x1 . . . , xn describe the variance in the data and wherein the size of the indexes x1 . . . , xn indicate the severity of the medical condition.

### Claim 10 (depends on 9)

A method according to claim 9, further comprising performing a correlation related measure on the combinations of features describing the variance in the data by comparing said combinations of features describing the variance in the data with an existing measure.

### Claim 11 (depends on 9)

A method according to claim 9, wherein the existing measure is mini mental state examination (MMSE) measure.

### Claim 12 (depends on 10)

A method according to claim 10, wherein performing a correlation related measure comprises:
repetitively, removing parts from said combinations of features describing the variance in the data or changing the combination of the features describing the variance in the data, and subsequently
determining the correlation between the out-coming combinations of features describing the variance in the data and the existing measure,
wherein those removed parts that do not contribute to the correlation or lower the correlation are excluded from the combinations of features describing the variance in the data.

### Claim 13 (depends on 9)

A method according to claim 9, wherein the step of determining combinations of features describing the variance in the data is done using principal component analyses (PCA) and wherein the combinations of features describing the variance in the data is the resulting PCA vector.

### Claim 14 (depends on 9)

A computer program product for instructing a processing unit to execute the method step of claim 9 when the product is run on a computer.

### Claim 15 (depends on 1)

A success monitoring system (300) for determining a success indicator for at least one probe compound by implementing the quantitative measure determined by the system according to claim 1, comprising:
a receiver unit (302) for receiving biosignal data collected from a test subject posterior to administering said at least one probe compound to the test subject,
a processor (303) adapted to:
determine an analogous feature vector F1 . . . M=[test_subj(f1), test_subj(f2), . . . , test_subj(fN)] for the test subject as determined for said population of M patients, and
determine the scalar product between the feature vector F1 . . . M=[test_subj(f1), test_subj(f2), . . . , test_subj(fN)] determined for the test subject and said combinations of features x1·fs+x2·fp . . . xn·ft describing the variance in the data, the scalar product being the success indicator.

### Claim 16 (depends on 9)

A method of using the quantitative measure reflecting the severity of a medical condition as claimed in claim 9 in determining a success indicator for at least one probe, comprising:
receiving biosignal data (401) collected from a test subject posterior to administering said at least one probe compound to the test subject,
determining an analogous feature vector F1 . . . M=[test_subj(f1), test_subj(f2), . . . , test_subj(fN)] for the test subject (402) as determined for said population of M patients, and
determining the scalar product between the feature vector F1 . . . M=[test_subj(f1), test_subj(f2), . . . , test_subj(fN)] determined for the test subject and said combinations of features x1·fs+x2·fp . . . xn·ft (403) describing the variance in the data, the scalar product being the success indicator.

### Claim 17 (depends on 16)

A computer program product for instructing a processing unit to execute the method step of claim 16 when the product is run on a computer.