A dynamic analysis apparatus includes a hardware processor. The hardware processor is configured to perform the following, calculate a prediction rate multiplied by a respiratory function value of the subject in predicting the respiratory function value when an exclusion target portion is excluded; obtain input of the exclusion target portion in an anatomical unit from the input unit, based on the anatomical unit, specify a partial region of the lung field in which a characteristic amount relating to a respiratory function in the plurality of frame images is calculated, calculate the characteristic amount related to the respiratory function in the partial region of the lung field specified from the plurality of frame images and the characteristic amount related to the respiratory function of an entire lung field, and calculate the prediction rate based on a characteristic amount ratio which is a ratio of the two calculated characteristic amounts.

A dynamic analysis apparatus includes a hardware processor. The hardware processor is configured to perform the following, calculate a prediction rate multiplied by a respiratory function value of the subject in predicting the respiratory function value when an exclusion target portion is excluded; obtain input of the exclusion target portion in an anatomical unit from the input unit, based on the anatomical unit, specify a partial region of the lung field in which a characteristic amount relating to a respiratory function in the plurality of frame images is calculated, calculate the characteristic amount related to the respiratory function in the partial region of the lung field specified from the plurality of frame images and the characteristic amount related to the respiratory function of an entire lung field, and calculate the prediction rate based on a characteristic amount ratio which is a ratio of the two calculated characteristic amounts.

An apparatus, system, and method for monitoring a person suffering from a chronic medical condition predicts and assesses physiological changes which could affect the care of that subject. Examples of such chronic diseases include (but are not limited to) heart failure, chronic obstructive pulmonary disease, asthma, and diabetes. Monitoring includes measurements of respiratory movements, which can then be analyzed for evidence of changes in respiratory rate, or for events such as hypopneas, apneas and periodic breathing. Monitoring may be augmented by the measurement of nocturnal heart rate in conjunction with respiratory monitoring. Additional physiological measurements can also be taken such as subjective symptom data, blood pressure, blood oxygen levels, and various molecular markers. Embodiments for detection of respiratory patterns and heart rate are disclosed, together with exemplar implementations of decision processes based on these measurements.

An apparatus, system, and method for monitoring a person suffering from a chronic medical condition predicts and assesses physiological changes which could affect the care of that subject. Examples of such chronic diseases include (but are not limited to) heart failure, chronic obstructive pulmonary disease, asthma, and diabetes. Monitoring includes measurements of respiratory movements, which can then be analyzed for evidence of changes in respiratory rate, or for events such as hypopneas, apneas and periodic breathing. Monitoring may be augmented by the measurement of nocturnal heart rate in conjunction with respiratory monitoring. Additional physiological measurements can also be taken such as subjective symptom data, blood pressure, blood oxygen levels, and various molecular markers. Embodiments for detection of respiratory patterns and heart rate are disclosed, together with exemplar implementations of decision processes based on these measurements.

Sensing an environment by confining a monitored live subject in an enclosure, detecting an effect on a coaxial piezoelectric cable resulting from the monitored live subject, wherein the coaxial piezoelectric cable is located at least proximate to the enclosure, and deriving information about a state of the monitored live subject based on the detected effect.

Sensing an environment by confining a monitored live subject in an enclosure, detecting an effect on a coaxial piezoelectric cable resulting from the monitored live subject, wherein the coaxial piezoelectric cable is located at least proximate to the enclosure, and deriving information about a state of the monitored live subject based on the detected effect.

A bed integrates sensors and other inputs to detect specific sleep environment conditions including point-specific pressure and/or temperature conditions. The bed includes a controller for commanding actuator or other devices to adjust these conditions. The controller may do so based on reference patterns for conditions and profiles of desired conditions. Information regarding the conditions may be provided to a remote computer, which may analyze the conditions and provide revised profiles of desired conditions.

A method for determining a rate of repetitive bodily motion of an individual with negligible contact with the individual begins by one or more computing devices transmitting a signal for reflection off of the individual and receiving a reflected signal. The method continues with one or more computing device applying a frequency estimation algorithm to the baseband signal to produce an estimated spectral density, where the estimated spectral density is in frequency domain and includes at least one frequency component corresponding to the repetitive bodily motion. The method further includes applying a repetitive bodily motion pattern search function to the estimated spectral density to estimate the rate of the repetitive bodily motion of the individual based on the at least one frequency component.

A method for determining a rate of repetitive bodily motion of an individual with negligible contact with the individual begins by one or more computing devices transmitting a signal for reflection off of the individual and receiving a reflected signal. The method continues with one or more computing device applying a frequency estimation algorithm to the baseband signal to produce an estimated spectral density, where the estimated spectral density is in frequency domain and includes at least one frequency component corresponding to the repetitive bodily motion. The method further includes applying a repetitive bodily motion pattern search function to the estimated spectral density to estimate the rate of the repetitive bodily motion of the individual based on the at least one frequency component.

Devices and methods are described that provide improved diagnosis from the processing of physiological data. The methods include use of multiple algorithms and intelligently combing the results of multiple algorithms to provide a single optimized diagnostic result. The algorithms are adaptive and may be customized for particular data sets or for particular patients. Examples are shown with applications to electrocardiogram data, but the methods taught are applicable to many types of physiological data.