Abstract: An apparatus, system, and method for the measurement, aggregation and analysis of data collected using non-contact or minimally-contacting sensors provides quality of life parameters for individual subjects, particularly in the context of a controlled trial of interventions on human subjects (e.g., a clinical trial of a drug, or an evaluation of a consumer item such as a fragrance). In particular, non-contact or minimal-contact measurement of quality-of-life parameters such as sleep, stress, relaxation, drowsiness, temperature and emotional state of humans may be evaluated, together with automated sampling, storage, and transmission to a remote data analysis center. One component of the system is that the objective data is measured with as little disruption as possible to the normal behavior of the subject. The system can also support behavioral and pharmaceutical interventions aimed at improving quality of life.

Abstract: An apparatus, system, and method for the measurement, aggregation and analysis of data collected using non-contact or minimally-contacting sensors provides quality of life parameters for individual subjects, particularly in the context of a controlled trial of interventions on human subjects (e.g., a clinical trial of a drug, or an evaluation of a consumer item such as a fragrance). In particular, non-contact or minimal-contact measurement of quality-of-life parameters such as sleep, stress, relaxation, drowsiness, temperature and emotional state of humans may be evaluated, together with automated sampling, storage, and transmission to a remote data analysis center. One component of the system is that the objective data is measured with as little disruption as possible to the normal behavior of the subject. The system can also support behavioral and pharmaceutical interventions aimed at improving quality of life.

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: An apparatus, system, and method is disclosed for monitoring the motion, breathing, heart rate of humans in a convenient and low-cost fashion, and for deriving and displaying useful measurements of cardiorespiratory performance from the measured signals. The motion, breathing, and heart rate signals are obtained through a processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Processing into separate cardiac and respiratory components is described. The heart rate can be determined by using either spectral or time-domain processing. The respiratory rate can be calculated using spectral analysis. Processing to derive the heart rate, respiratory sinus arrhythmia, or a ventilatory threshold parameter using the system is described. The sensor, processing, and display can be incorporated in a single device which can be worn or held close to the body while exercising (e.g.

Abstract: An apparatus, system, and method is disclosed for monitoring the motion, breathing, heart rate of humans in a convenient and low-cost fashion, and for deriving and displaying useful measurements of cardiorespiratory performance from the measured signals. The motion, breathing, and heart rate signals are obtained through a processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Processing into separate cardiac and respiratory components is described. The heart rate can be determined by using either spectral or time-domain processing. The respiratory rate can be calculated using spectral analysis. Processing to derive the heart rate, respiratory sinus arrhythmia, or a ventilatory threshold parameter using the system is described. The sensor, processing, and display can be incorporated in a single device which can be worn or held close to the body while exercising (e.g.

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: 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: An apparatus, system, and method for the measurement, aggregation and analysis of data collected using non-contact or minimally-contacting sensors provides quality of life parameters for individual subjects, particularly in the context of a controlled trial of interventions on human subjects (e.g., a clinical trial of a drug, or an evaluation of a consumer item such as a fragrance). In particular, non-contact or minimal-contact measurement of quality-of-life parameters such as sleep, stress, relaxation, drowsiness, temperature and emotional state of humans may be evaluated, together with automated sampling, storage, and transmission to a remote data analysis center. One component of the system is that the objective data is measured with as little disruption as possible to the normal behavior of the subject. The system can also support behavioral and pharmaceutical interventions aimed at improving quality of life.

Abstract: An apparatus for diagnosing sleep apnea only uses an electrocardiogram signal and a computer processor and associated computer code that are configured to analyze the electrocardiogram signal and classify each time period of the electrocardiogram signal as either apneic or normal. A diagnostic measure of sleep apnea for the human patient is provided based on classification results obtained by combining a results for various electrocardiogram signal time periods. A computer-readable medium has computer code that causes the computer to analyze an electrocardiogram signal relating to the human patient and classify each time period in a set of time periods as either apneic or normal. A diagnostic measure of sleep apnea is provided based on classification results obtained by combining a plurality of results from the set of time periods using either time or frequency domain processing, or both.

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 hypoponeas, 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: An apparatus, system, and method is disclosed for monitoring the motion, breathing, heart rate of humans in a convenient and low-cost fashion, and for deriving and displaying useful measurements of cardiorespiratory performance from the measured signals. The motion, breathing, and heart rate signals are obtained through a processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Processing into separate cardiac and respiratory components is described. The heart rate can be determined by using either spectral or time-domain processing. The respiratory rate can be calculated using spectral analysis. Processing to derive the heart rate, respiratory sinus arrhythmia, or a ventilatory threshold parameter using the system is described. The sensor, processing, and display can be incorporated in a single device which can be worn or held close to the body while exercising (e.g.

Abstract: An apparatus, system, and method for the measurement, aggregation and analysis of data collected using non-contact or minimally-contacting sensors provides quality of life parameters for individual subjects, particularly in the context of a controlled trial of interventions on human subjects (e.g., a clinical trial of a drug, or an evaluation of a consumer item such as a fragrance). In particular, non-contact or minimal-contact measurement of quality-of-life parameters such as sleep, stress, relaxation, drowsiness, temperature and emotional state of humans may be evaluated, together with automated sampling, storage, and transmission to a remote data analysis center. One component of the system is that the objective data is measured with as little disruption as possible to the normal behavior of the subject. The system can also support behavioral and pharmaceutical interventions aimed at improving quality of life.

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: A system for remote assessment of a user is disclosed. The system comprises application software resident on a server and arranged to interact across a network with a user operating a client device to obtain one or more sample signals of the user's speech. A datastore is arranged to store the user speech samples in association with details of the user. A feature extraction engine is arranged to extract one or more first features from respective speech samples. A comparator is arranged to compare the first features extracted from a speech sample with second features extracted from one or more reference samples and to provide a measure of any differences between the first and second features for assessment of the user.

Abstract: Techniques are provided for detecting abnormal respiration within a patient based upon intracardiac electrogram (IEGM) signals or other electrical cardiac signals. Briefly, abnormal respiration is detected using a pattern recognition trained to discriminate normal and abnormal respiration based on morphological parameters and interval-based parameters extracted from the IEGM signals. In addition, techniques are described for distinguishing among different cardiac rhythm types within the patient while using one or more pattern classifiers or other pattern recognition devices.

Abstract: An apparatus for diagnosing sleep apnea only uses an electrocardiogram signal and a computer processor and associated computer code that are configured to analyze the electrocardiogram signal and classify each time period of the electrocardiogram signal as either apneic or normal. A diagnostic measure of sleep apnea for the human patient is provided based on classification results obtained by combining a results for various electrocardiogram signal time periods. A computer-readable medium has computer code that causes the computer to analyze an electrocardiogram signal relating to the human patient and classify each time period in a set of time periods as either apneic or normal. A diagnostic measure of sleep apnea is provided based on classification results obtained by combining a plurality of results from the set of time periods using either time or frequency domain processing, or both.

Abstract: There is provided a method of determining a diagnostic measure of sleep apnea including the following steps: acquiring an electrocardiogram signal, calculating a set of RR intervals and electrocardiogram-derived respiratory signal from said electrocardiogram, and hence calculating a set of spectral and time-domain measurements over time periods including power spectral density, mean, and standard deviation. These measurements are processed by a classifier model which has been trained on a pre-existing data base of electrocardiogram signals to provide a probability of a specific time period containing apneic episodes or otherwise. These probabilities can be combined to form an overall diagnostic measure. The system also provides a system and apparatus for providing a diagnostic measure of sleep apnea.