Measurement and analysis of trends in physiological and/or health data
First Claim
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.
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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.
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Citations
40 Claims
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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:
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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; 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. - View Dependent Claims (2, 3, 4, 5, 6)
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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:
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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; 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. - View Dependent Claims (8, 9, 10, 11)
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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:
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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; 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. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27)
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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:
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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. - View Dependent Claims (29, 30, 31, 32)
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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:
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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. - View Dependent Claims (34, 35, 36, 37, 38, 39, 40)
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Specification