Wellness analysis system

Patent metadata

Field	Value
Publication number	US-11289199-B2
Application number	US-201113009505-A
Country	US
Kind code	B2
Filing date	Jan 19, 2011
Priority date	Jan 19, 2010
Publication date	Mar 29, 2022
Grant date	Mar 29, 2022

Abstract

A wellness analyzer is in communications with sensors that generate real-time physiological data from a patient. The wellness analyzer is also in communications with databases that provide non-real-time information relevant to a medical-related assessment of the patient. In a diagnostic mode, a monitor layer inputs the sensor data and adjunct layers input the database information. Adjunct layer logic blocks process the database information so as to output supplemental information to the monitor. Monitor logic blocks process the sensor data and the supplemental information so as to generate a wellness output. In a simulation mode, a simulator generates at least one parameter and the monitor generates a predictive wellness output accordingly.

First claim

What is claimed is:

1. A wellness analyzer comprising:
a plurality of sensors that generate real-time physiological data from a plurality of sites on a patient;
a plurality of databases that provide non-real-time information relevant to a medical-related assessment;
a wellness monitor that generates a wellness output in a diagnostic mode, the wellness monitor including one or more processors, wherein the one or more processors receive sensor data and the non-real-time information, process the non-real-time information and generate supplemental information, and process the sensor data and the supplemental information and generate the wellness output, wherein the one or more processors generate a plurality of parameters as outputs based on the sensor data received as input, wherein the wellness output is based at least in part upon one or more derived features of the plurality of parameters, the one or more derived features including at least one of a slope, a trend, a variability, a pattern, or a waveform morphology of the sensor data, the one or more derived features further including relationships derived between at least two of the plurality of parameters for the monitored patient, wherein the derived relationship comprises patient responses to variations in at least one of the plurality of parameters; and
a simulator configured to:
generate and vary at least one simulated parameter output for the patient according to at least one probability distribution, and
generate one or more other non-simulated parameter outputs as dependent parameters, the dependent parameters depending from the at least one simulated parameter based on the derived relationships and in response to the at least one simulated parameter output,

wherein the one or more processors process the at least one simulated parameter output and the dependent parameters and generate a predictive wellness output when the wellness monitor is in a simulation mode.

2. The wellness analyzer according to claim 1 ,
wherein the one or more processors perform a signal extraction function that extracts physiological signals from the sensor data; and
wherein the one or more processors perform a signal analysis function that derives the parameters from the physiological signals and generates the plurality of parameters as outputs.

3. The wellness analyzer according to claim 2 wherein the one or more processors process the output parameters and generate a plurality of system status outputs,
wherein the one or more processors perform a feature extractor function that extracts one or more features of the parameters, the one or more features comprising at least one of a slope, a trend, a variability, a pattern, or a waveform morphology, and
wherein the one or more processors perform a feature analyzer function that derives the system statuses from the one or more extracted features,
each system status output indicating the physiological condition of a biological system including any of a patient's circulatory, respiratory, neurological, gastrointestinal, urinary, immune, musculoskeletal, endocrine and reproductive systems.

4. The wellness analyzer according to claim 3 wherein the one or more processors process the output system statuses and generate the wellness output.

5. The wellness analyzer according to claim 4 wherein the one or more processors perform a feature extraction function on the parameters so as to identify at least one of a parameter level, a parameter trend, a parameter pattern, or a parameter statistic as one or more extracted features, the wellness monitor further comprising:
a memory that stores the one or more extracted features when the wellness monitor is in the diagnostic mode,
wherein the one or more processors perform a feature playback function that recalls the one or more extracted features from the memory when the wellness monitor is in the simulation mode so as to generate the one or more other parameter outputs as the dependent parameters in response to the at least one simulated parameter output according to the one or more derived features of the patient from the diagnostic mode.

6. The wellness analyzer according to claim 5 further comprising
a diagnostic knowledge base memory that stores diagnostic codes; and
a predictive knowledge base memory that stores predictive codes, the diagnostic knowledge base memory and the predictive knowledge base memory in communications with the one or more processors,
wherein the one or more processors perform an expert system function that compares the output system statuses with the stored diagnostic codes so as to generate the wellness output when the wellness monitor is in the diagnostic mode; and
wherein the one or more processors perform an expert system function that compares the output system statuses with the stored predictive codes and generates the predictive wellness output when the wellness monitor is in the simulation mode.

7. A wellness analysis method performed by a physiological monitor comprising:
outputting a wellness output in a diagnostic mode by performing at least the steps of:
receiving physiological sensor data from a patient at a physiological monitor;
deriving using one or more processors a plurality of physiological parameters based upon the received sensor data;
outputting for display on a display of the physiological monitor a plurality of physiological system statuses based at least in part upon one or more derived features of the parameters, the one or more derived features comprising at least one of a slope, a trend, a variability, a pattern, or a waveform morphology, the one or more derived features further including relationships derived between at least two of the plurality of parameters for the monitored patient, wherein the derived relationship comprises patient responses to variations in at least one of the plurality of parameters; and
generating using the one or more processors a wellness output for display on the display of the physiological monitor based upon the system statuses;

outputting a predictive wellness output in a simulation mode by performing at least the steps of:
simulating and varying using the one or more processors an independent parameter for the patient, the independent parameter being one of the plurality of physiological parameters;
playing back one or more other parameters of the plurality of the physiological parameters as dependent parameters using the one or more processors, the dependent parameters depending from the independent parameter based on the derived relationships and in response to the independent parameter; and
determining using the one or more processors a predictive wellness output for display on the display of the physiological monitor in response to the simulated and the dependent parameters.

8. The wellness analysis method according to claim 7 wherein generating comprises:
extracting the one or more derived features from the parameters;
analyzing the one or more derived features so as to determine the system statuses and
storing the one or more derived features in a memory.

9. The wellness analysis method according to claim 8 wherein extracting comprises:
routing the parameters to selected feature extractor functions; and
performing the selected feature extractor functions on the parameters;
the functions including at least a level, a trend, and a statistical calculation.

10. The wellness analysis method according to claim 9 wherein analyzing comprises reading a plurality of system status codes from a look-up table according to the extracted features.

11. The wellness analysis method according to claim 10 wherein generating the wellness output comprises:

Assignees

• Al Ali Ammar
• Masimo Corp

Inventors

• Al-Ali Ammar

Classifications

• G16H50/50 Primary

  for simulation or modelling of medical disorders · CPC title

• G16H50/20 Primary

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title