Abstract: 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.

Abstract: Systems and methods assist with managing a chronic disease of a user such as a chronic respiratory or cardiac disease. The system may include a physiological monitor adapted to be carried by the user and operative to sense a physiological parameter of the user. The system may include a management device operatively coupled with the physiological monitor to receive the sensed physiological parameter of the user. The management device, such as with an included processor, may be configured to analyze the physiological and/or environmental parameters to detect a trigger pattern of the parameters, the trigger pattern indicative of a probable event of exacerbation of the chronic respiratory and/or cardiac condition. The management device may then generate automated responses based on the trigger pattern such as by providing instructions for activities and/or treatment for the chronic condition.

Abstract: An apparatus, system, and method monitors the motion, breathing, heart rate and sleep state of subjects, e.g., humans, in a convenient, non-invasive/non-contact, and low-cost fashion. More particularly, the motion, breathing, and heart rate signals are obtained through processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Periods of sleep disturbed respiration, or central apnea can be detected through analysis of the respiratory signal. The mean heart rate, and derived information, such as the presence of cardiac arrhythmias can be determined from the cardiac signal. Motion estimates can be used to recognize disturbed sleep and periodic limb movements. The sleep state may be determined by applying a classifier model to the resulting streams of respiratory, cardiac and motion data. A means for display of the sleep state, respiratory, cardiac, and movement status may also be provided.

Abstract: One or more sensors are configured for detection of characteristics of moving objects and living subjects for human identification or authentication. One or more processors, such as in a system of sensors or that control a sensor, may be configured to process signals from the one or more sensors to identify a person. The processing may include evaluating features from the signals such as breathing rate, respiration depth, degree of movement and heart rate etc. The sensors may be radio frequency non-contact sensors with automated detection control to change detection control parameters based on the identification of living beings, such as to avoid sensor interference.

Abstract: Radio frequency motion sensors may be configured for operation in a common vicinity so as to reduce interference. In some versions, interference may be reduced by timing and/or frequency synchronization. In some versions, a master radio frequency motion sensor may transmit a first radio frequency (RF) signal. A slave radio frequency motion sensor may determine a second radio frequency signal which minimizes interference with the first RF frequency. In some versions, interference may be reduced with additional transmission adjustments such as pulse width reduction or frequency and/or timing dithering differences. In some versions, apparatus may be configured with multiple sensors in a configuration to emit the radio frequency signals in different directions to mitigate interference between emitted pulses from the radio frequency motion sensors.

Abstract: Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor (100) may access sensor data signals related to bodily movement and/or respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include one or more of variability of respiration rate and variability of respiration amplitude. A processor may then determine a sleep stage based on one or more of respiration variability and bodily movement, such as with a combination of both. The determination of sleep stages may distinguish between deep sleep and other stages of sleep, or may differentiate between deep sleep, light sleep and REM sleep. The bodily movement and respiration movement signals may be derived from one or more sensors, such as non-invasive sensor (e.g., a non-contact radio-frequency motion sensor or a pressure sensitive mattress).

Abstract: Disclosed is a cardio-pulmonary health monitoring apparatus. The apparatus comprises a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session; a processor; and a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals. The method comprises extracting one or more sleep disordered breathing features from the one or more movement signals, and predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

Abstract: A processing system includes methods to promote sleep. The system may include a monitor such as a non-contact motion sensor from which sleep information may be determined. User sleep information, such as sleep stages, hypnograms, sleep scores, mind recharge scores and body scores, may be recorded, evaluated and/or displayed for a user. The system may further monitor ambient and/or environmental conditions corresponding to sleep sessions. Sleep advice may be generated based on the sleep information, user queries and/or environmental conditions from one or more sleep sessions. Communicated sleep advice may include content to promote good sleep habits and/or detect risky sleep conditions. In some versions of the system, any one or more of a bedside unit 3000 sensor module, a smart processing device, such as a smart phone or smart device 3002, and network servers may be implemented to perform the methodologies of the system.

Abstract: An apparatus, such as a pressure support ventilator or respiratory monitor, provides operations for a patient, such as a patient with heart failure, Chronic Obstructive Pulmonary Disease or other respiratory insufficiency. In some embodiments, the apparatus may detect a respiratory condition, such as a decompensation event and/or exacerbation event, from respiratory parameters. A clinical alert message may be transmitted or displayed for a physician based on the results of query presented to the patient in response to an analysis of the one or more respiratory parameters. In some embodiments, the apparatus generates a potential relapse indicator that may provide a prediction of a risk of an oncoming clinical event such as for evaluating whether to release the patient from a hospital.

Abstract: Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor may access sensor data signals related to bodily movement and respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include variability of respiration rate or variability of respiration amplitude. A processor may then determine a sleep stage based on a combination bodily movement and respiration variability. The determination of sleep stages may distinguish between deep sleep and other stages of sleep, or may differentiate between deep sleep, light sleep and REM sleep. The bodily movement and respiration movement signals may be derived from one or more sensors, such as non-invasive sensor (e.g., a non-contact radio-frequency motion sensor or a pressure sensitive mattress).

Abstract: Disclosed is a cardio-pulmonary health monitoring apparatus. The apparatus comprises a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session; a processor; and a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals. The method comprises extracting one or more sleep disordered breathing features from the one or more movement signals, and predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

Abstract: A processing system includes methods to promote sleep. The system may include a monitor such as a non-contact motion sensor from which sleep information may be determined. User sleep information, such as sleep stages, hypnograms, sleep scores, mind recharge scores and body scores, may be recorded, evaluated and/or displayed for a user. The system may further monitor ambient and/or environmental conditions corresponding to sleep sessions. Sleep advice may be generated based on the sleep information, user queries and/or environmental conditions from one or more sleep sessions. Communicated sleep advice may include content to promote good sleep habits and/or detect risky sleep conditions. In some versions of the system, any one or more of a bedside unit 3000 sensor module, a smart processing device, such as a smart phone or smart device 3002, and network servers may be implemented to perform the methodologies of the system.

Abstract: Aspects of the of the disclosure relate to a non-contact physiological motion sensor and a monitor device that can incorporate use of the Doppler effect. A continuous wave of electromagnetic radiation can be transmitted toward one or more subjects and the Doppler-shifted received signals can be digitized and/or processed subsequently to extract information related to the cardiopulmonary motion in the one or more subjects. The extracted information can be used, for example, to determine apneic events and/or snoring events and/or to provide apnea or snoring therapy to subjects when used in conjunction with an apnea or snoring therapy device. In addition, methods of use are disclosed for sway cancellation, realization of cessation of breath, integration with multi-parameter patient monitoring systems, providing positive providing patient identification, or any combination thereof.

Abstract: A sensor for physiology sensing may be configured to generate oscillation signals for emitting radio frequency pulses for range gated sensing. The sensor 402 may include a radio frequency transmitter configured to emit the pulses and a receiver configured to receive reflected ones of the emitted radio frequency pulses. The received pulses may be processed to detect physiology characteristics such as motion, sleep, respiration and/or heartbeat. In some embodiments, the sensor may employ a circuit including a pulse generator configured to generate signal pulses. The circuit may also include a dielectric resonator oscillator configured to generate a radio frequency oscillating signal. A switched oscillation circuit may be coupled to the pulse generator and the dielectric resonator oscillator. The switched circuit may be configured to generate a pulsed radio frequency oscillating signal for emitting the radio frequency pulses.

Abstract: An apparatus, system, and method monitors the motion, breathing, heart rate and sleep state of subjects, e.g., humans, in a convenient, non-invasive/non-contact, and low-cost fashion. More particularly, the motion, breathing, and heart rate signals are obtained through processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Periods of sleep disturbed respiration, or central apnea can be detected through analysis of the respiratory signal. The mean heart rate, and derived information, such as the presence of cardiac arrhythmias can be determined from the cardiac signal. Motion estimates can be used to recognize disturbed sleep and periodic limb movements. The sleep state may be determined by applying a classifier model to the resulting streams of respiratory, cardiac and motion data. A means for display of the sleep state, respiratory, cardiac, and movement status may also be provided.

Abstract: Methods and apparatus perform periodic breathing detection, such as Cheyne-Stokes respiration detection. The detection may be performed by one or more processors, such as by analysis of data from one or more sensors. In some cases, the detection may be based on an electrocardiogram (ECG) signal, such as from ECG electrodes and/or an accelerometer signal, such as from an accelerometer. An occurrence of periodic breathing may be detected based on features derived from the signal(s). For example, detection may be based on deriving a respiration signal from the sensed signal(s) and/or analysis of RR interval times or relative QRS amplitude values, which may be evaluated on a segment-by-segment basis. The detection may provide monitoring and reporting of the occurrence of periodic breathing by a monitoring device and/or provide a basis for controlling changes to a provided respiratory treatment or therapy, such as by a respiratory pressure therapy device.

Abstract: A sensor may be configured to detect periodic limb movement in a sleeping person. The sensor may be a non-contact sensor, such as a radar motion sensor. The sensor may include a radio frequency transmitter for emitting radio frequency signals toward the person. The sensor may include a receiver for receiving reflected ones of the emitted radio frequency signals and processing the reflected ones of the emitted radio frequency signals to produce motion signal(s). A processor, such as one integrated with or coupled to the sensor, may evaluate the motion signals, such as in-phase and quadrature motion signals, and generate an indicator to identify occurrence of periodic limb movement in the motion signals based on the evaluation of the motion signals.

Abstract: A sensor for physiology sensing may be configured to generate oscillation signals for emitting radio frequency pulses for range gated sensing. The sensor 402 may include a radio frequency transmitter configured to emit the pulses and a receiver configured to receive reflected ones of the emitted radio frequency pulses. The received pulses may be processed to detect physiology characteristics such as motion, sleep, respiration and/or heartbeat. In some embodiments, the sensor may employ a circuit including a pulse generator configured to generate signal pulses. The circuit may also include a dielectric resonator oscillator configured to generate a radio frequency oscillating signal. A switched oscillation circuit may be coupled to the pulse generator and the dielectric resonator oscillator. The switched circuit may be configured to generate a pulsed radio frequency oscillating signal for emitting the radio frequency pulses.

Abstract: Methods and apparatus detect events of sleep disordered breathing from an input audio signal such as from a sound sensor. A processor may be configured to receive the audio signal that represents sounds of a user during a period of sleep. The processor determines reliable respiration epochs from the audio, such as on a frame-by-frame basis, that include periods of audible breathing and/or snoring. The processor detects presence of a sleep disordered breathing events, such as hypopnea, apnea, apnea snoring or modulated breathing, in the reliable respiration epoch(s) and generates output to indicate the detected event(s). Optionally, the apparatus may serve as a cost-effective screening device such as when implemented as a processor control application for a mobile processing device (e.g., mobile phone or tablet).