Measurement and analysis of trends in physiological and/or health data
First Claim
1. A method for non-contact registering 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, 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 so that the minimum data collection time depends on the sensitivity (signal-to-noise ratio) of the data collection device; and
extracting typical patterns from said data using at least one of mathematical transformation and mathematical modeling.
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Abstract
System comprised of a portable medical device and method for non-contact 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.
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Citations
47 Claims
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1. A method for non-contact registering 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, extracting and representing the most significant parameters from series of said data, said method comprising:
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collecting said data from at least one fiducial point in a physiological cycle 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; and
extracting typical patterns from said data using at least one of mathematical transformation and mathematical modeling. - View Dependent Claims (2, 3, 4, 5)
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6. 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, said method comprising:
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registering at least one type of said data using at least one non-contact sensor over a period of at least several seconds so that the minimum registration time is determined by the sensor sensitivity (signal-to-noise ratio);
determining a typical pattern or waveform or a set of coefficients or wavelets by applying a mathematical transformation to at least one of said registered data over a period of at least several seconds;
forming a time series from said typical patterns or waveforms or coefficients or wavelets corresponding to the consecutive time intervals; and
characterizing trends in said time series using a mathematical transformation. - View Dependent Claims (7, 8)
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9. A system for non-contact registering at least one of electrocardiographic (ECG), magnetocardiographic (MCG), mechanocardiographic, ballistocardiographic, physical activity, body position, blood pressure, vasomotor activity, respiration, temperature, physiological, and health data, extracting and representing the most significant parameters from time series of said data, said system comprising:
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at least one of miniaturized, non-contact physiological sensors to collect said data over a period of at least several seconds with the minimum time for the data collection being determined using the sensitivity (signal-to-noise ratio) of the said sensors; and
software or hardware for extracting typical patterns from these data using at least one method selected from mathematical transformation, modeling, statistics and artificial intelligence. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. 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:
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at least one miniaturized, non-contact sensor for measuring said data from at least one fiducial point;
software or hardware for forming a time series from said data obtained from a period of at least several seconds, with the minimum time interval depending on the sensitivity (signal-to-noise ratio) of the said non-contact sensor;
software or hardware for 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 an averaging or another mathematical transformation;
software or hardware for forming time series from said typical patterns or waveforms or coefficients or wavelets representing consecutive time intervals; and
software or hardware for analyzing trends or changes in said time series using a mathematical transformation. - View Dependent Claims (22, 23, 24, 25)
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26. 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 software or hardware for:
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at least one sensor for collecting the data for at least one fiducial point over a period of at least several seconds with the minimum collection time being dependent on the system sensitivity (signal-to-noise ratio); and
a processor for extracting typical patterns from these data using a mathematical transformation. - View Dependent Claims (27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40)
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41. A portable 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:
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at least one miniaturized, non-contact sensor for measuring said data from at least one fiducial point in a physiological cycle;
software or hardware for forming a time series from said data obtained from a period of at least several physiological cycles;
software or hardware for 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 an averaging or another mathematical transformation;
software or hardware for forming time series from said typical patterns or waveforms or coefficients or wavelets representing consecutive time intervals; and
software or hardware for analyzing trends or changes in said time series using a mathematical transformation. - View Dependent Claims (42)
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43. A portable system for dynamic long-term 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:
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at least one miniaturized, non-contact sensor for measuring said data from at least one fiducial point in a physiological cycle;
software or hardware for forming a time series from said data obtained from a period of at least several days;
software or hardware for extracting a typical pattern of at least one waveform, coefficient, and wavelet representing characteristic features of the said data during the corresponding time interval using at least one method selected from averaging, mathematical transformation, modeling, statistics, and artificial intelligence;
software or hardware for forming time series from said at least one typical pattern, waveform, coefficient, and wavelet representing consecutive time intervals;
software or hardware for analyzing trends or changes in said time series using at least one method selected from mathematical transformation, modeling, statistics and artificial intelligence; and
a communications unit for sending at least one parameter of said data to a computer device for processing and detailed analysis of serial changes in said data, said computer device using at least one of the methods selected from mathematical decomposition, modeling, statistics, and methods of artificial intelligence. - View Dependent Claims (44)
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45. A method for dynamic 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, said method comprising:
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registering at least one type of said data using at least one non-contact sensor over a period of at least several days so that the minimum registration time is determined by the sensor sensitivity (signal-to-noise ratio);
determining at least one typical pattern, waveform, coefficient, and wavelet by applying at least one method selected from mathematical transformation, modeling, statistics, and artificial intelligence to at least one of said registered data over a period of at least several days;
forming a time series from said at least one typical pattern, waveform, coefficient, and wavelet corresponding to the consecutive time intervals; and
characterizing trends in said time series using at least one method selected from mathematical transformation, modeling, statistics, and artificial intelligence. - View Dependent Claims (46, 47)
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Specification