Measurement and analysis of trends in physiological and/or health data

• US 7,485,095 B2
• Filed: 01/06/2006
• Issued: 02/03/2009
• Est. Priority Date: 05/30/2000
• Status: Expired due to Fees

• Current Assignee: Vladimir Shusterman
• Original Assignee: Vladimir Shusterman
• Inventors: Shusterman, Vladimir
• Primary Examiner(s): Getzow; Scott M
• Application Number:
  US11/327,546
• Publication Number:
  US 20060122525A1
• Time in Patent Office: 1,124 Days
• Field of Search: 600508-521
• US Class Current: 600/508
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/243 specially adapted for magnetocardiographic [MCG] signals
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/6822 Neck
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: A61B 5/726 using Wavelet transforms

Abstract

System comprised of a portable medical device and method for registering at least one of electrocardiographic (ECG), magnetocardiographic (MCG), physical activity, body position, respiration, temperature, blood pressure, vasomotor activity, blood flow, neural activity, and other physiological, and health data, extracting and representing the most significant parameters from time series (trends) of said data. The system achieves the necessary sensitivity (signal-to-noise ratio) in order to miniaturize the device by collecting data of at least one fiducial point in a cardiac complex over a period of at least one, and preferably, several seconds, and extracting the underlying typical patterns from these data. Due to the miniaturization (pocket-size), the system can be implemented in a shape of a pen (or another miniature shape) that can be worn in a pocket.

Claims

1. A method for registering at least one of electrocardiographic (EGG), magnetocardiographic (MCG), mechanocardiographic, ballistocardiographic data, physical activity, body position, respiration, temperature, blood pressure, vasomotor activity, physiological, and health data, extracting and representing the most significant parameters from series of said data, said method comprising:

collecting said data from at least one fiducial point in a physiological cycle over a period of at least several seconds;

analyzing said data in low-level-of-detail to extract significant features from monitored signals and compare said features with reference values, said low-level-of-detail analysis applied locally, close to the point of data acquisition; and

analyzing said data in a higher-level-of-detail to identify at least one of short-term and long-term trends of changes in said significant features using at least one method selected from mathematical decomposition, mathematical modeling, statistical analysis, pattern recognition, and artificial intelligence.

2. A method as set forth in claim 1 in which said analyzing includes:

collecting at least one of electrocardiographic (ECG), magnetocardiographic (MCG), ballistocardiographic, mechanocardiographic, physical activity, body position, respiration, blood pressure, vasomotor activity, temperature, physiological, and health data over a period of at least several seconds so that the minimum data collection time depends on the sensitivity (signal-to-noise ratio) of the data collection device;

finding fiducial points indicative of a specific phase of physiological cycle;

aligning said data using said fiducial points;

applying a mathematical transformation to the said aligned time series of said data to obtain a typical pattern or waveform or a set of coefficients or primary elements that represent the most typical features of the said data in the corresponding time interval; and

forming time series from the said typical patterns or waveforms or coefficients to analyze trends.

3. A method as set forth in claim 1 in which said mathematical transformation and mathematical modeling includes at least one of the following methods:

averaging the signal over a period of several cardiac beats (seconds), orthogonal and non-orthogonal decompositions, principal component analysis, mappings and projections, wavelet transform, Fourier transform, Laplace transform, and Hilbert transform, linear and non-linear correlations, linear and non-linear regression models, methods of artificial intelligence, neural networks, expert systems, fuzzy logic, statistical estimators, and filtering techniques.

4. A method as set forth in claim 1, said method includes:

detecting fiducial points indicative of a specific phase of physiological cycle;

time-aligning the cardiac beats using said fiducial points; and

applying said mathematical transformation to extract typical patterns from said time-aligned series of said data.

5. A method as set forth in claim 4, said detecting of fiducial points is performed by measuring at least one of the following signals:

mechanical movements of the chest and other parts of the human body measured by a 1, 2, or 3 -axial accelerometer, giant magnetic resistance (GMR), transmitter and receiver of radio waves, or another sensor determining position and movements of the chest and body, mechanical sounds of the heart, electrical signals generated by the heart, light, infrared, and ultrasound signals measuring cardiac mechanical activity.

6. A method as set forth in claim 1 which includes exchanging information between said low level and said higher level of analysis to improve at least one of said levels of analysis.

7. A method for analysis of serial changes in at least one of electrocardiographic (ECG), magnetocardiographic (MCG), mechanocardiographic, ballistocardiographic data, physical activity, body position, respiration, temperature, blood pressure, vasomotor activity, physiological, and health data from a subject, said method comprising:

registering at least one type of said data using at least one non-contact sensor over a period of at least several seconds;

determining a typical feature comprising a pattern, waveform, set of coefficients or wavelets by applying at least one method selected from mathematical transformation, mathematical modeling, statistical analysis, pattern recognition and artificial intelligence to said at least one type of data which has been registered over a period of at least several seconds;

forming a time series from said typical feature which has been determined over a plurality of periodic time intervals;

characterizing said time series using at least one method selected from mathematical decomposition, mathematical modeling, statistical analysis, pattern recognition and artificial intelligence to generate serial characteristics indicative of changes in the subject'"'"'s data; and

quantifying trends in said serial characteristics using at least one method selected from mathematical transformation, mathematical modeling, statistical analysis, pattern recognition and artificial intelligence.

8. A method as set forth in claim 7 in which said mathematical transformation and mathematical modeling includes at least one of the following methods:

averaging said serial changes in the cardiac beats using at least one of orthogonal and non-orthogonal decompositions, principal component analysis, mappings and projections, wavelet transform, Fourier transform, Laplace transform, and Hilbert transform, linear and non-linear correlations, linear and non-linear regression models, analysis of variance, cluster analysis, factor analysis, canonical analysis, regression and discriminant function analyses, and probabilistic methods, Bayesian probability and Mahalanobis distance, pattern recognition, methods of artificial intelligence, fuzzy logic, neural networks, expert systems, statistical estimators, filtering techniques, and hybrid artificial intelligence systems.

9. A method as said forth in claim 7 in which said

characterizing uses Principal Component Analysis (PCA) and generating PCA-coefficients indicative of both linear and nonlinear changes in the individual pattern;

and

said determining the magnitude of said linear and nonlinear changes uses time varying mean and variance of said PCA-coefficients and determining the complexity of said linear and nonlinear changes by calculating the number of PCA-coefficients that exhibit substantially simultaneous changes.

10. A method as set forth in claim 7 in which said serial changes in data are communicated to a network of computers for processing and detailed analysis of the serial changes in a higher level of resolution analysis.

11. A method as set forth in claim 7 in which said registering of data occurs over a period of many days.

12. A portable system for registering at least one of electrocardiographic (ECG), magnetocardiographic (MCG), mechanocardiographic, ballistocardiographic, physical activity, body position, respiration, temperature, blood pressure, central neural activity, spinal cord activity, peripheral neural activity, cranial nerve activity, neural ganglia activity, vasomotor activity, physiological, and health data, extracting and representing the most significant parameters from series of cardiac beats, said system comprising:

at least one sensor for collecting the data for at least one fiducial point over a period of at least several seconds; and

a processor for analyzing said data in at least two levels of detail to extract typical patterns from these data using a method selected from at least one of mathematical transformation, mathematical modeling, statistical analysis, pattern recognition and artificial intelligence, forming a time series from said data, extracting a typical feature comprising a pattern, waveform, set of coefficients or wavelets representing characteristic features of the said data during a plurality of time intervals, analyzing changes in said time series, and quantifying said changes.

13. A portable system as set forth in claim 12 in which said extracting of typical patterns includes:

at least one sensor for finding fiducial points indicative of specific phase of the physiological cycle; and

a processor for aligning the said data using said fiducial points and applying a mathematical transformation to the said aligned time series of said data.

14. A portable system as set forth in claim 12 in which said mathematical transformation and mathematical modeling includes at least one of the following methods:

averaging the signal over a period of at least several cardiac beats, orthogonal and non-orthogonal decompositions, principal component analysis, mappings and projections, wavelet transform, Fourier transform, Laplace transform, and Hilbert transform, linear and non-linear correlations, linear and non-linear regression models, analysis of variance, cluster analysis, factor analysis, canonical analysis, regression and discriminant function analyses, and probabilistic methods, Bayesian probability and Mahalanobis distance, pattern recognition, methods of artificial intelligence, fuzzy logic, neural networks, expert systems, statistical estimators, filtering techniques, and hybrid artificial intelligence systems.

15. A portable system as set forth in claim 12 in which said mathematical transformation and mathematical modeling includes at least one of the following methods:

averaging the signal over a period of at least several cardiac beats, orthogonal and non-orthogonal decompositions, principal component analysis, mappings and projections, wavelet transform, Fourier transform, Laplace transform, and Hubert transform, linear and non-linear correlations, linear and non-linear regression models, analysis of variance, cluster analysis, factor analysis, canonical analysis, regression and discriminant function analyses, and probabilistic methods, Bayesian probability and Mahalanobis distance, pattern recognition, methods of artificial intelligence, fuzzy logic, neural networks and expert systems, statistical estimators, filtering techniques, and hybrid artificial intelligence systems.

16. A portable system as set forth in claim 15, in which said processor detects fiducial points by measuring at least one of the following signals:

mechanical movements of the chest and other parts of the human body measured by a 1, 2, or 3-axial accelerometer, giant magnetic resistance (GMR), optical sensor, transmitter and receiver of radio waves, or another sensor determining position and movements of the chest and body, mechanical sounds of the heart, electrical signals generated by the heart, light, infrared, and ultrasound signals measuring cardiac mechanical activity.

17. A portable system as set forth in claim 12 in which said processor

forms a time series from said serial changes;

characterizes said time series using Principal Component Analysis (PCA) and generating PCA-coefficients indicative of both linear and nonlinear changes in the individual pattern; and

determines the magnitude of said linear and nonlinear changes by using time varying mean and variance of said PCA-coefficients and determining the complexity of said linear and nonlinear changes by calculating the number of PCA-coefficients that exhibit substantially simultaneous changes.

18. A portable system as set forth in claim 12 in which said processor detects:

fiducial points indicative of a specific phase of the physiological cycle;

time-aligns the said data using said fiducial points; and

applies said mathematical transformation to extract typical patterns from said time-aligned series of said data.

19. A portable system as set forth in claim 12, said system is suitable for use as a simple diagnostic and screening tool during regular daily activities and during night-time, for analysis of slow, long-term trends in cardiac electromagnetic signals that develop gradually over periods of days-weeks-months, for detection of slow changes and abnormalities in the pattern of at least one of electrocardiographic (ECG), magnetocardiographic (MCG), mechanocardiographic, ballistocardiographic, physical activity, body position, blood pressure, vasomotor activity, respiration, temperature, physiological, and health data, including changes in the heart rate, amplitude, polarity, and duration of P, Q, R, S, T and U waves, ST-segment, and the intervals between the peaks (including PR, QRS, and QT intervals), for periodic personal self-examination for subjects at risk or with a history of cardiovascular disease, for monitoring slow changes in the said data due to development of cardiovascular disease, treatment results, physical exercises, behavioral, environmental, psychophysiological, pharmacological, and health factors.

20. A portable system as set forth in claim 12 which is suitable for analysis of slow changes in the pattern of the signal and representation of the results at different levels of detail using the color-coded scales which are understandable to a lay person and a medical professional.

21. A portable system as set forth in claim 12, in which said different levels of detail include:

a low-resolution Scale I that represents a small number of primary elements;

an intermediate-resolution Scale II that represents serial changes in each of the said elements using a mathematical decomposition into series of representative indicators; and

a high-resolution Scale III that combines serial changes in all elements of the said data to provide complete information about the dynamics of the signal.

22. A portable system as set forth in claim 12 that is wearable, being worn in a pocket or attached to the clothes or a belt or attached using a stripe.

23. A portable system as set forth in claim 22, which includes sensors for measuring at least one of the following signals:

a 1, 2, or 3-axial accelerometer, giant magnetic resistance (GMR), or another sensor determining position and movements of the chest and body, and physical activity of a subject, optical light or infra-red sensor for measuring cardiac mechanical activity, sonic or ultra-sonic sensors for measuring heart and lung sounds, blood flow, and blood pressure, non-invasive, non-contact sensors for measuring glucose, hemoglobin, and other biochemical, hormonal, biophysical, respiratory, genetic, proteomic;

environmental, and health data.

24. A portable system as set forth in claim 12 that includes:

a data acquisition unit for measuring at least one of electrocardiographic (ECG), magnetocardiographic (MCG), blood pressure, vasomotor activity, physical activity, body position, respiration, temperature, physiological, and health data;

a processing unit for collecting the data and extracting the typical patterns; and

a communication unit for sending the data and receiving information from other devices.

25. A portable system as set forth in claim 24 in which said communication unit is wireless.

26. A portable system as set forth in claim 12, in which said at least one sensor includes

at least one of the following sensors:

fluxgate, optical, laser-optical, superconducting quantum interference devices (SQUIDs) and non-contact electrocardiographic sensor.

27. A system as set forth in claim 12 in which said processor comprises at least one computer device selected from a specialized computer, a personal computer, a computer organizer (PDA), a cell phone, a smart phone, a group of computers connected via at least one of a local network, wireless network, and the Internet.

28. A system for analysis of serial changes or trends in at least one of electrocardiographic (ECG), magnetocardiographic (MCG), mechanocardiographic, ballistocardiographic, physical activity, body position, respiration, temperature, blood pressure, vasomotor activity, physiological, and health data, said system comprising:

at least one miniaturized, non-contact sensor for measuring said data from at least one fiducial point; and

at least one analysis unit for forming a time series from said data obtained from a period of at least several seconds, extracting a typical pattern or waveform or a set of coefficients or wavelets representing characteristic features of the said data during the corresponding time interval using at least one method selected from averaging, mathematical transformation, mathematical modeling, statistical analysis, pattern recognition, and artificial intelligence;

forming time series from said typical patterns or waveforms or coefficients or wavelets representing consecutive time intervals, analyzing trends or changes in said time series using a mathematical transformation, characterizing said time series using Principal Component Analysis (PCA), generating PCA-coefficients indicative of changes in the individual pattern, and determining the magnitude of said changes by using time varying mean and variance of said PCA-coefficients and determining the complexity of said linear and nonlinear changes by calculating the number of PCA-coefficients that exhibit substantially simultaneous changes.

29. A system set forth in claim 28 said system comprised of software and hardware for filtering at least one of electrocardiographic (ECG), magnetocardiographic (MCG), mechanocardiographic, ballistocardiographic data, blood pressure, vasomotor activity, physical activity, body position, respiration, temperature, physiological, and health data, rejecting noisy or corrupted data prior to said extraction of typical patterns.

30. A system as set forth in claim 28 in which said mathematical transformation and mathematical modeling includes at least one of the following methods:

averaging the signal over a period of at least several cardiac beats (seconds), orthogonal and non-orthogonal decompositions, principal component analysis, mappings and projections, wavelet transform, Fourier transform, Laplace transform, and Hilbert transform, linear and non-linear correlations, linear and non-linear regression models, analysis of variance, cluster analysis, factor analysis, canonical analysis, regression and discriminant function analyses, and probabilistic methods, Bayesian probability and Mahalanobis distance, pattern recognition, methods of artificial intelligence, fuzzy logic, neural networks, expert systems, statistical estimators, filtering techniques, and hybrid artificial intelligence systems.

31. A system as set said forth in claim 28, in which said

characterizing uses Principal Component Analysis (PCA) and generating PCA-coefficients indicative of both linear and nonlinear changes in the individual pattern.

32. A system as set forth in claim 28 said detecting of fiducial points is performed by measuring at least one of the following signals:

mechanical movements of the chest and other parts of the human body measured by a 1, 2, or 3-axial accelerometer, giant magnetic resistance (GMR), or another sensor determining position and movements of the chest and body, mechanical sounds of the heart, electrical signals generated by the heart, light, infrared, and ultrasound signals measuring cardiac mechanical activity.

33. A system for analysis of serial changes or trends in at least one of electrocardiographic (ECG), magnetocardiographic (MCG), mechanocardiographic, ballistocardiographic, physical activity, body position, respiration, temperature, blood pressure, vasomotor activity, physiological, and health data, said system comprising:

at least one miniaturized sensor for measuring said data from at least one fiducial point;

at least one first analysis unit for analyzing said data in low-level-of-detail to extract significant features from monitored signals and compare said features with reference values, said low-level-of-detail analysis applied locally, close to the point of data acquisition; and

at least one second analysis unit for analyzing said data in a higher-level-of-detail to identify at least one of short-term and long-term trends of changes in said significant features using at least one method selected from mathematical decomposition, modeling, statistical analysis, pattern recognition, and artificial intelligence.

34. A system as set forth in claim 33, said system applied for analysis of slow changes in the pattern of the signal and representation of the results at different levels of detail using the color-coded scales which are understandable to a lay person and a medical professional.

35. A system as set forth in claim 34, in which said different levels of detail include:

a low-resolution Scale I that represents a small number of primary elements;

an intermediate-resolution Scale II that represents serial changes in each of the said elements using a mathematical decomposition into series of representative indicators; and

a high-resolution Scale III that combines serial changes in all elements of at least one of electrocardiographic (ECG), magnetocardiographic (MCG), physical activity, body position, respiration, blood pressure, vasomotor activity, temperature, physiological, and health data to provide complete information about the dynamics of said data.

36. A system as set forth in claim 33 in which:

the measurement of biomagnetic signals of the heart is performed using at least one of the following magnetometers;

fluxgate, optical, laser-optical, and superconducting quantum interference device (SQUID);

or

the measurement of electrocardiographic signals is performed by at least one non-contact sensor.

37. A system as set forth in claim 33 which includes at least one non-contact physiological sensor capable of exchanging information with an implantable device.

38. A system as set forth in claim 37 in which said at least one non-contact physiological sensor is adapted to be implanted under the skin.

39. A system as set forth in claim 33 in which said at least one sensor records at least one signal related to at least one of peripheral, spinal, and cranial neural activity.

40. A system as set forth in claim 33 in which said at least one sensor records at least one signal related to the central (brain) neural activity.