Abstract: Disclosed embodiments include an apparatus that comprises (a) a kinematics sensor module including an accelerometer, a gyroscope, a magnetometer, or combinations thereof; and (b) a bidirectional wireless communication module configured for wirelessly synchronizing the sampling time instances of the kinematics sensor module with the sampling time instances of at least a second wearable apparatus including a second kinematics sensor module.

Abstract: Disclosed embodiments include a method and an apparatus for monitoring sleep apnea severity that comprise: (a) analyzing an oxygen saturation signal to extract a plurality of time-domain and frequency-domain metrics; (b) calculating an oxygen saturation-based Apnea Hyponea Index (AHI) by employing a predetermined functional mapping between said metrics and polysomnography (PSG)-based AHI; and (c) displaying an oxygen saturation-based AHI to enable a specialist to monitor sleep apnea severity without requiring PSG. According to a particular embodiment, the oxygen saturation signal is nocturnal oxygen saturation and the functional mapping is a multilinear regression model (MLR) or a multilayer perceptron network (MLP).

Abstract: Disclosed embodiments include an apparatus for generating a plurality of movement impairment indices from one or more kinematic signals to characterize movement disorders. Additionally we disclose methods for generating a plurality of movement impairment indices from one or more kinematic signals obtained from one or more kinematic sensors, said methods implemented in a digital computer with one or more processors in order to characterize movement disorders based on spectral analysis, regularity metrics, and time-frequency analysis.

Abstract: Disclosed embodiments include a method for automatic identification of activity and rest periods based on an actigraphy signal. According to an embodiment, the method comprises (a) applying one or more filters to the actigraphy signal to generate a filtered actigraphy signal, and (b) comparing the filtered actigraphy signal against an adaptive threshold to generate an output signal corresponding to the activity and rest periods. The method can be implemented as part of a medical apparatus or medical system for automatic activity/rest identification from actigraphy signals.

Abstract: Disclosed embodiments include a multi-function wearable apparatus comprising (a) a sensor module including a plurality of low power solid state kinematics sensors,(b) a microprocessor module comprising a low power microcontroller configured for device control, device status, and device communication; (c) a data storage module comprising a solid state local storage medium, said data storage module configured for sampling and storage of kinematics data; (d) a wireless communication module comprising a low power bidirectional transceiver wherein said wireless communication module is configured for communicating and synchronizing sampling time instances of said sensor module with signals from a second apparatus; and (e) a power module comprising a battery and an energy charging regulator. According to one embodiment, the wearable apparatus is a watch capable of quantifying human movement.

Abstract: Disclosed embodiments include a method for measuring non-invasive blood pressure from an oscillometric signal and a cuff pressure signal implemented in a medical apparatus comprising: (a) calculating a pulse pressure signal by subtracting an upper and a lower envelope of the oscillometric signal; and (b) calculating without the use of beat detection a mean arterial pressure, a systolic blood pressure, and a diastolic blood pressure from said oscillometric signal, said cuff pressure signal, and a plurality of thresholds a device with at least one processor.

Abstract: Disclosed embodiments include a complete system and platform which allows for continuous monitoring of movement disorders during normal daily activities in the clinic, home, and other normal daily environments. The system comprises: 1) a wearable apparatus for continuous monitoring of movement disorders, 2) a docking station, 3) a web server, and 4) methods for statistical analysis that generate movement impairment measures.

Abstract: A patient monitoring device that combines physiological data collection with actigraphy data collection and associates the physiological data with synchronous actigraphy data. A method for processing actigraphy data by calculating absolute difference vectors of actigraphy signal vectors.

Abstract: Disclosed embodiments include a method for automatic detection of sleep apnea implemented in a medical apparatus, the method comprising (a) extracting a plurality of signal features by analyzing an oxygen saturation signal, (b) performing dimensionality reduction on the plurality of signal features to generate a plurality of signal features in a transformed space; and (c) displaying a sleep apnea diagnosis result based on a statistical classifier that operates on the plurality of signal features in a transformed space.

Abstract: Disclosed embodiments include an apparatus that comprises (a) a kinematics sensor module including an accelerometer, a gyroscope, a magnetometer, or combinations thereof; and (b) a bidirectional wireless communication module configured for wirelessly synchronizing the sampling time instances of the kinematics sensor module with the sampling time instances of at least a second wearable apparatus including a second kinematics sensor module.

Abstract: Disclosed embodiments relate to methods, apparatuses, and systems for characterizing gait. Specifically, disclosed embodiments are related methods, apparatuses, and systems for characterizing gait with wearable and wirelessly synchronized inertial measurement units. These include a method for gait characterization that comprises (a) detecting zero-velocity periods using two or more wearable and wirelessly synchronized movement monitoring devices including a triaxial accelerometer and a triaxial gyroscope and (b) calculating temporal measures of gait during walking by estimating the change in position and orientation during each step.

Abstract: Disclosed embodiments include a movement monitoring apparatus comprising a wireless synchronization scheme. Depending on the particular embodiment such wireless synchronization scheme is a master synchronization scheme or a mesh synchronization scheme. Additionally, in a particular embodiment, the movement monitor further comprises a robust wireless data transfer data controller. The disclosure includes a description of the complete system, namely, the wireless synchronized movement monitors with robust data transfer capabilities, the docking station, the access point, and the computer-implemented analysis system.

Abstract: Methods and apparatus for determining a cardiac parameter from cardiovascular pressure signals including arterial blood pressure (ABP) and the photoplethysmographic signal to quantify the degree of amplitude modulation due to respiration and predict fluid responsiveness are disclosed. Disclosed embodiments include a method for assessing fluid responsiveness implemented in a digital computer with one or more processors comprising: (a) measuring a cardiovascular signal, and (b) computing a dynamic index predictive of fluid responsiveness from said cardiovascular signal using a nonlinear state space estimator. According to one particular embodiment, and without limitation, the nonlinear state space estimator is based on a model for cardiovascular signals such as arterial blood pressure or plethysmogram signals, and employs a marginalized particle filter to estimate a dynamic index predictive of fluid responsiveness that is substantially equivalent to a variation in pulse pressure of said cardiovascular signal.

Abstract: Disclosed embodiments include a method for automatic identification of activity and rest periods based on an actigraphy signal. According to an embodiment, the method comprises (a) applying one or more filters to the actigraphy signal to generate a filtered actigraphy signal, and (b) comparing the filtered actigraphy signal against an adaptive threshold to generate an output signal corresponding to the activity and rest periods. The method can be implemented as part of a medical apparatus or medical system for automatic activity/rest identification from actigraphy signals.

Abstract: Disclosed embodiments include a method and an apparatus for monitoring sleep apnea severity that comprise: (a) analyzing an oxygen saturation signal to extract a plurality of time-domain and frequency-domain metrics; (b) calculating an oxygen saturation-based Apnea Hyponea Index (AHI) by employing a predetermined functional mapping between said metrics and polysomnography (PSG)-based AHI; and (c) displaying an oxygen saturation-based AHI to enable a specialist to monitor sleep apnea severity without requiring PSG. According to a particular embodiment, the oxygen saturation signal is nocturnal oxygen saturation and the functional mapping is a multilinear regression model (MLR) or a multilayer perceptron network (MLP).

Abstract: The invention disclosed includes a method and apparatus for calculation of T-wave alternas in electrocardiographic (ECG) signals; said method comprises the following method steps: (a) collecting and storing an ECG signal; (b) detecting each heartbeat T-wave in the ECG signal using an automatic detection and segmentation algorithm; (c) classifying said heartbeat T-wave as an even or odd heartbeat T-wave; (d) initializing said method by taking the first even and the first odd heartbeat T-wave as their respective initial average; (e) processing all the heartbeat T-waves in the ECG signal to generate an even heartbeat T-wave weighted average and odd heartbeat T-wave weighted average; and (f) calculating TWA using a distance metric between the even heartbeat weighted average and the odd heartbeat weighted average, said distance metric including curve matching Continuous Dynamic Time Warping (CDTW).

Abstract: Disclosed embodiments include a telemedicine communication system designed to make possible “virtual” patient visits by approved visitors such as the spouse, family, and friends of the hospitalized patient having the appropriate digital certificate and visitation/communication permissions. According to one embodiment, the system enables relatives to visit hospitalized patients using audiovisual communication modalities selected according to the severity and health status of the patient. Additionally, the system provides functionality to enable approved relatives to follow the health status of the patient according to their permissions.

Abstract: The present invention discloses a method and related apparatus for determining a cardiac parameter from either the arterial blood pressure signal or the photoplethysmographic signal to quantify the degree of amplitude modulation due to respiration (pulse pressure variation) and predict fluid responsiveness. The method involves the application of Lempel-Ziv complexity to a filtered and segmented physiologic signal for direct determination of the fluid status of a patient. Real-time monitoring of fluid status involves the implementation of the disclosed method as part of a bedside monitoring apparatus.