Health Data Dynamics, Its Sources and Linkage with Genetic/Molecular Tests

• US 20110190598A1
• Filed: 01/30/2011
• Published: 08/04/2011
• Est. Priority Date: 01/31/2010
• Status: Active Grant

• Current Assignee: Vladimir Shusterman
• Original Assignee: Vladimir Shusterman
• Inventors: Shusterman, Vladimir
• Granted Patent: US 8,706,464 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 70/60 relating to pathologies

Abstract

Method and system for the analysis and source localization of the dynamical patterns in medical and health data, and linking such dynamical patterns with the individual'"'"'s genetic and/or molecular data. The invention makes use of optimally positioned sensors (sensor arrays) providing input data for signal processing, time-series analysis, pattern recognition and mathematical modeling to facilitate dynamical tracking of systemic arterial pressure without a pressure cuff, local vascular activity, electrocardiographic (ECG), respiratory, physical, muscular, gastrointestinal and neural activity, temperature and other physiological/health data. The invention also facilitates separation of local signals (such as local aneurisms or local vascular activity) from non-local, central or systemic patterns (e.g. systemic blood pressure). In addition, the invention improves identification of dynamical patterns associated with a specific genotype/disorder for screening, personalized risk assessment, diagnosis and treatment control. The system can be implemented in a specialized processor, such as an ambulatory blood pressure monitor, Electrocardiograph, Holter monitor located outside subject'"'"'s body or implanted inside the body, mobile/cell phone or Smart Phone/Personal Digital Assistant, computer or computer network (the Internet), including wireless or mobile network. The system can be also linked to the electronic health/medical records and other databases.

Claims

1. A method for linking dynamical patterns in health data with genetic variants to improve at least one risk stratification, treatment control, diagnosis and disease management comprising:

analyzing said dynamic patterns in data and their relationship to the genetic variants in at least one general population, demographic group, clinical group and patient group;

determining at least one typical characteristic of said dynamical pattern of health data associated with said different variants; and

adjusting at least one analysis, processing, filtering and pattern-recognition method to achieve separation of said dynamical patterns associated with said different genetic variants.

2. A method as set forth in claim 1, in which said determining at least one typical characteristic of said dynamical pattern of health data associated with different genetic variants is achieved by determining probability of having said genetic variant in the presence and absence of said typical characteristic using at least one probability function selected from Bayesian probability, Markov model and hidden Markov model.

3. A method as set forth in claim 1 in which said study group is selected from at least one general population, demographic group and patient group.

4. A method as set forth in claim 1 in which said typical characteristic of said dynamical patterns is selected from a numerical value, logical value, Boolean parameter, threshold, array of values, matrix of values, model parameter, filter cutoff value, pattern, function and mathematical functional.

5. A method as set forth in claim 1, in which said health data are selected from at least one blood pressure, cardiac output, vascular activity, electrocardiographic, respiratory, muscular, physical, neural activity and sleep-study data.

6. A method useful for at least one analysis calibration, screening, diagnosis, monitoring, risk assessment and healthcare information management comprising:

collecting health data over a period of at least several milliseconds at the time of application of at least one stimulus and during stimulus-free period;

determining at least one typical characteristic differentiating dynamical patterns associated with said stimulus versus those in the absence of said stimulus using at least one point-by-point comparison, point-by-point subtraction, template matching, signal averaging and root-mean-square error, mathematical decomposition and pattern recognition; and

calibrating the analysis using an individual'"'"'s typical characteristic to said stimulus as a reference.

7. A method as set forth in claim 6 in which said stimuli are selected to modify at least one arterial pressure, baroreflex, vagus-nerve reflex, sympathetic reflex, central nervous system activity, autonomic nervous system activity, respiration, physical activity, body position and sleep patterns.

8. A method as set forth in claim 6 in which said typical characteristic is selected from a numerical value, logical value, Boolean, threshold, model parameter, filter cutoff value, pattern, function and mathematical functional.

9. A method useful in monitoring of at least one blood pressure, cardiac output and vascular activity comprising:

analyzing at least one signal related to a blood pressure wave in at least one major blood vessel over at least several milliseconds using at least one sensor placed in the vicinity of said major blood vessel to determine at least one pressure wave characteristic;

determining at least one feature, pattern, threshold, parameter, filter cutoff value, and function of said pressure wave characteristic linked to systemic blood pressure; and

adjusting at least one analysis, processing, filtering and pattern-recognition method, cutoff value, threshold, and parameter to achieve separation of said dynamical patterns associated with systemic blood pressure from local changes and noises.

10. A method as set forth in claim 9 in which said major blood vessel is selected from the aorta, ascending aorta, thoracic aorta, abdominal aorta, pulmonary artery, hepatic portal vein and kidney arteries.

11. A system useful for monitoring at least one blood pressure, cardiac output and vascular activity comprising:

analyzing at least one signal related to a blood pressure wave in at least one major blood vessel over at least several milliseconds using at least one sensor placed in the vicinity of said major blood vessel to determine at least one pressure wave characteristic;

determining at least one feature, pattern, threshold, parameter, filter cutoff value, and function of said pressure wave characteristic linked to systemic blood pressure; and

adjusting at least one analysis, processing, filtering and pattern-recognition method, cutoff value, threshold, and parameter to achieve separation of said dynamical patterns associated with systemic blood pressure from local changes and noises.

12. A system as set forth in claim 11 in which at least one measurement of a signal related to blood pressure wave is obtained using a sensor placed in the vicinity of a non-central (smaller) artery to compare said signal with that registered in the vicinity of a central vessel and to separate peripheral vascular activity from the systemic blood pressure.

13. A system as set forth in claim 11 in which said at least one sensor is selected from at least one accelerometer, microphone, micro-electro-mechanical (MEMS) sensor, plethysmographic, photoplethysmographic, acoustic, ultrasound, infrared, magnetic, electromagnetic, electrochemical and pulse oximetry sensor.

14. A system as set forth in claim 11 in which said adjusting is performed using at least one point-by-point comparison of the signals recorded during the same phase of a cardiac cycle at different anatomical locations, time-alignment of the signals by finding the time lag corresponding to the maximum value of the cross-correlation function, normalization of the signals by finding the stretching/compression coefficients that maximize the cross-correlation function and normalizing the waveform duration/amplitude by these coefficients, rejection of the noisy or uncharacteristic signals that deviate from the standard pressure-waveform template, averaging of the signals registered by several sensors with a time-alignment and normalization as necessary, a mathematical decomposition, pattern-recognition, principal component analysis and other orthogonal and non-orthogonal transforms that extract typical features of the pressure waveforms.

15. A system as set forth in claim 11 in which said at least one and, preferably, more than one sensor is placed inside at least one wearable bag, pack, electrocardiographic electrode, belt, necklace, headband, wristband, waist belt, vest, t-shirt and other clothes.

16. A system as set forth in claim 11 in which said adjusting at least one analysis, processing, filtering and pattern-recognition method, cutoff value, threshold, and model parameter is performed in the setting of at least one actively induced mechanical, electrical and chemical stimulus and sequences of stimuli to improve the pressure-wave detection and discrimination.

17. A system as set forth in claim 14 in which said stimulus is given at the frequency of at least one pressure-pulse and its sub-harmonic to induce a stronger, resonant signal (or resonance) of the vascular walls at a particular frequency and phase, which would allow a more reliable detection of the pressure signal and its discrimination from noises.

18. A system as set forth in claim 14 in which said stimulus is applied to improve at least one detection of blood pressure signal and its discrimination from noises, system calibration, analysis of physiological responses to said stimulus and diagnosis of syncope, transient hypertension and hypotension.

19. A system as set forth in claim 14, in which said stimulus is applied to modify arterial pressure for at least one system calibration and estimation of an individual'"'"'s baroreflex and associated changes in the pressure waveform.