Abstract: A medical device includes a housing configured for implantation within a body of a patient, and detection circuitry disposed in the housing and coupled to an electrode arrangement. The detection circuitry is configured to sense cardiac signals from the patient. A processor is coupled to the detection circuitry. The processor is configured to compare the cardiac signals to an initial detection threshold, automatically generate an additional detection threshold in response to a predetermined number of the cardiac signals meeting or exceeding the initial detection threshold or a previously generated detection threshold, count each occurrence of a cardiac signal meeting or exceeding each of the respective detection thresholds, and record cardiac signal data only for a cardiac signal that meets or exceeds the highest of the detection thresholds.

Abstract: Methods and apparatus combine patient measurement data with demographic or physiological data of the patient to determine an output that can be used to diagnose and treat the patient. A customized output can be determined based the demographics of the patient, physiological data of the patient, and data of a population of patients. In another aspect, patient measurement data is used to predict an impending cardiac event, such as acute decompensated heart failure. At least one personalized value is determined for the patient, and a patient event prediction output is generated based at least in part on the personalized value and the measurement data. For example, bioimpedance data may be used to establish a baseline impedance specific to the patient, and the patient event prediction output generated based in part on the relationship of ongoing impedance measurements to the baseline impedance. Multivariate prediction models may enhance prediction accuracy.

Abstract: Embodiments relate to devices and methods for monitoring, identifying, and determining risk of congestive heart failure (CHF) hospitalization. Methods include determining physiological values of a patient by electrocardiogram (ECG), bioimpedance, and 3-axis accelerometer, filtering the physiological values, comparing physiological values to baseline parameters and determining CHF risk. Devices include a 3-axis accelerometers, bioimpedance sensors, and an electrocardiogram, each capable of measuring patient physiological values, and one or more processors to receive the measured physiological values.

Abstract: Methods and apparatus combine patient measurement data with demographic or physiological data of the patient to determine an output that can be used to diagnose and treat the patient. A customized output can be determined based the demographics of the patient, physiological data of the patient, and data of a population of patients. In another aspect, patient measurement data is used to predict an impending cardiac event, such as acute decompensated heart failure. At least one personalized value is determined for the patient, and a patient event prediction output is generated based at least in part on the personalized value and the measurement data. For example, bioimpedance data may be used to establish a baseline impedance specific to the patient, and the patient event prediction output generated based in part on the relationship of ongoing impedance measurements to the baseline impedance. Multivariate prediction models may enhance prediction accuracy.

Abstract: Methods and apparatus combine patient measurement data with demographic or physiological data of the patient to determine an output that can be used to diagnose and treat the patient. A customized output can be determined based the demographics of the patient, physiological data of the patient, and data of a population of patients. In another aspect, patient measurement data is used to predict an impending cardiac event, such as acute decompensated heart failure. At least one personalized value is determined for the patient, and a patient event prediction output is generated based at least in part on the personalized value and the measurement data. For example, bioimpedance data may be used to establish a baseline impedance specific to the patient, and the patient event prediction output generated based in part on the relationship of ongoing impedance measurements to the baseline impedance. Multivariate prediction models may enhance prediction accuracy.

Abstract: Methods and apparatus to determine the presence of and track functional chronotropic incompetence (hereinafter “CI”) in an in-home setting under conditions of daily living. The functional CI of the patient may be determined with one or more of a profile of measured patient heart rates, a measured maximum patient heart rate, or a peak of the heart rate profile. The functional CI of the patient may be determined with the measured heart rate profile, in which the measured heart rate profile may correspond to heart rates substantially less than the maximum heart rate of the patient, such that the heart rate can be safely measured when the patient is remote from a health care provider. The functional CI of the patient may be determined based a peak of the remotely measured heart rate profile, for example a peak corresponding to the mode of the heart rate distribution profile.

Abstract: A medical device includes a housing configured for implantation within a body of a patient, and detection circuitry disposed in the housing and coupled to an electrode arrangement. The detection circuitry is configured to sense cardiac signals from the patient. A processor is coupled to the detection circuitry. The processor is configured to compare the cardiac signals to an initial detection threshold, automatically generate an additional detection threshold in response to a predetermined number of the cardiac signals meeting or exceeding the initial detection threshold or a previously generated detection threshold, count each occurrence of a cardiac signal meeting or exceeding each of the respective detection thresholds, and record cardiac signal data only for a cardiac signal that meets or exceeds the highest of the detection thresholds.

Abstract: An apparatus, system, and method directed to detecting a physiological signal during discrete time separated detection windows, deriving one or more respiratory disturbance indices from the physiological signal, detecting a respiratory disturbance state in response to the one or more respiratory disturbance indices deviating from a threshold value, interpolating the one or more respiratory disturbance indices between adjacent time separated detection windows, and declaring a respiratory disturbance episode based on the detected respiratory disturbance state during the detection windows and the interpolation between detection windows.

Abstract: In various examples, an apparatus includes an apparatus configured for implantation within a body of a patient. The apparatus, in some examples, includes a housing. At least one antenna extends from the housing, the antenna being flexible such that the antenna conforms to the body of the patient. In some examples, the apparatus includes at least three electrodes, wherein at least a first electrode is disposed on the antenna and at least a second electrode is disposed on the housing. The at least three electrodes are disposed in a non-linear configuration, allowing for differential processing of signals recorded by the at least three electrodes.

Abstract: A medical device includes a housing and an electrode arrangement coupled to the housing and configured to sense an electrical physiologic signal from a patient. The device also includes detection circuitry coupled to the electrode arrangement and configured to obtain a cardiac signal component and a non-cardiac signal component from the physiological signal. A processor is coupled to the detection circuitry. The processor is configured to detect patient activity using at least the non-cardiac signal component and discriminate between voluntary and involuntary activity of the patient based on a comparison of temporally aligned cardiac and non-cardiac signal components.

Abstract: Methods and apparatus to determine the presence of and track functional chronotropic incompetence (hereinafter “CI”) in an in-home setting under conditions of daily living. The functional CI of the patient may be determined with one or more of a profile of measured patient heart rates, a measured maximum patient heart rate, or a peak of the heart rate profile. The functional CI of the patient may be determined with the measured heart rate profile, in which the measured heart rate profile may correspond to heart rates substantially less than the maximum heart rate of the patient, such that the heart rate can be safely measured when the patient is remote from a health care provider. The functional CI of the patient may be determined based a peak of the remotely measured heart rate profile, for example a peak corresponding to the mode of the heart rate distribution profile.

Abstract: Methods and apparatus combine patient measurement data with demographic or physiological data of the patient to determine an output that can be used to diagnose and treat the patient. A customized output can be determined based the demographics of the patient, physiological data of the patient, and data of a population of patients. In another aspect, patient measurement data is used to predict an impending cardiac event, such as acute decompensated heart failure. At least one personalized value is determined for the patient, and a patient event prediction output is generated based at least in part on the personalized value and the measurement data. For example, bioimpedance data may be used to establish a baseline impedance specific to the patient, and the patient event prediction output generated based in part on the relationship of ongoing impedance measurements to the baseline impedance. Multivariate prediction models may enhance prediction accuracy.

Abstract: Embodiments relate to a device and a method of monitoring and analyzing physiological parameters of a patient. The method includes electrically connecting one or more electrodes with a measurement site of a patient, generating a stimulation signal or signals sufficient to provide multiple stimulation frequencies, multiple waveforms or a combination thereof, measuring a one or more bioimpedance values from the generated signals and analyzing at least one of a fluid bioimpedance contribution, fat bioimpedance contribution or ion bioimpedance contribution within the one or more bioimpedance values sufficient to generate a physiological report.

Abstract: Methods and apparatus combine patient measurement data with demographic or physiological data of the patient to determine an output that can be used to diagnose and treat the patient. A customized output can be determined based the demographics of the patient, physiological data of the patient, and data of a population of patients. In another aspect, patient measurement data is used to predict an impending cardiac event, such as acute decompensated heart failure. At least one personalized value is determined for the patient, and a patient event prediction output is generated based at least in part on the personalized value and the measurement data. For example, bioimpedance data may be used to establish a baseline impedance specific to the patient, and the patient event prediction output generated based in part on the relationship of ongoing impedance measurements to the baseline impedance. Multivariate prediction models may enhance prediction accuracy.

Abstract: Embodiments relate to a method of monitoring physiological parameters of a patient with renal dysfunction. The method includes electrically connecting one or more medical device electrodes with a measurement site of a patient, generating one or more first stimulation signals sufficient to provide input physiological parameters specific to the patient, measuring one or more first bioimpedance values from the generated signals, analyzing at least one of the input physiological parameters within the one or more first bioimpedance values and generating a personalized dialysis program. The systems and methods can further provide essentially real-time data of patient undergoing treatment and control of treatment to a patient.

Abstract: Embodiments relate to a device and a method of monitoring and analyzing physiological parameters of a patient. The method includes electrically connecting one or more electrodes with a measurement site of a patient, generating a stimulation signal or signals sufficient to provide multiple stimulation frequencies, multiple waveforms or a combination thereof, measuring a one or more bioimpedance values from the generated signals and analyzing at least one of a fluid bioimpedance contribution, fat bioimpedance contribution or ion bioimpedance contribution within the one or more bioimpedance values sufficient to generate a physiological report.

Abstract: The present invention relates to a physiological monitor and system, particularly to an electroencephalogram (EEG) monitor and system, and a method of detecting the presence and absence of artifacts and possibly removing artifacts from an EEG, other physiological signal or sensor signal without corrupting or compromising the signal. The accurate, real-time detection of the presence or absence of artifacts and removal of artifacts in EEG or other signals allows for increased reliability in the efficacy of those signals. The strategy of rejecting artifact-corrupted EEG can result in unacceptable data loss, and asking subjects to minimize movements in order to minimize artifacts is not always feasible. The present invention allows for increased accuracy in detection and removal of artifacts from physiological signals, substantially in real time, and without loss or corruption of signal or data in order to increase the accuracy of such signals for diagnosis and treatment purposes.

Abstract: Methods and apparatus to determine the presence of and track functional chronotropic incompetence (hereinafter “CI”) in an in-home setting under conditions of daily living. The functional CI of the patient may be determined with one or more of a profile of measured patient heart rates, a measured maximum patient heart rate, or a peak of the heart rate profile, such as the peak of a heart rate distribution profile. The functional CI of the patient may be determined with the measured heart rate profile, in which the measured heart rate profile may correspond to heart rates substantially less than the maximum heart rate of the patient, such that the heart rate can be safely measured when the patient is remote from a health care provider. The functional CI of the patient may be determined based a peak of the remotely measured heart rate profile, for example a peak corresponding to the mode of the heart rate distribution profile.

Abstract: Methods and apparatus combine patient measurement data with demographic or physiological data of the patient to determine an output that can be used to diagnose and treat the patient. A customized output can be determined based the demographics of the patient, physiological data of the patient, and data of a population of patients. In another aspect, patient measurement data is used to predict an impending cardiac event, such as acute decompensated heart failure. At least one personalized value is determined for the patient, and a patient event prediction output is generated based at least in part on the personalized value and the measurement data. For example, bioimpedance data may be used to establish a baseline impedance specific to the patient, and the patient event prediction output generated based in part on the relationship of ongoing impedance measurements to the baseline impedance. Multivariate prediction models may enhance prediction accuracy.