361 results about "Respiratory signal" patented technology

The present invention is directed to improved systems and methods for processing respiratory signals derived generally from respiratory plethysmography, and especially from respiratory inductive plethysmographic sensors mounted on a garment for ambulatory recording. The systems and methods provide improved signal filtering for artifact rejection, improved calibration of sensor data to produce outputs indicative of lung volumes. Further, this invention provides improved systems and methods directed to processing lung volume signals, however measured or derived, to provide improved determination of respiratory parameters and improved recognition of selected respiratory events.

A system and method for positioning a patient for radiotherapy is provided. The method comprises: acquiring a first x-ray image sequence of a target inside the patient at a first angle; acquiring first respiratory signals of the patient while acquiring the first x-ray image sequence; acquiring a second x-ray image sequence of the target at a second angle; acquiring second respiratory signals of the patient while acquiring the second x-ray image sequence; synchronizing the first and second x-ray image sequences with the first and second respiratory signals to form synchronized first and second x-ray image sequences; identifying the target in the synchronized first and second x-ray image sequences; and determining three-dimensional (3D) positions of the target through time in the synchronized first and second x-ray image sequences.

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.

The present invention is directed to systems and methods for monitoring characteristics of a subject. A system according to an exemplary embodiment of the invention includes a sensor subsystem including at least one respiratory sensor disposed proximate to the subject and configured to detect a respiratory characteristic of the subject, wherein the sensor subsystem is configured to generate and transmit at least one respiratory signal representing the respiratory characteristic, and at least one physiological sensor disposed proximate to the subject and configured to detect a physiological characteristic of the subject, wherein the sensor subsystem is configured to generate and transmit at least one physiological signal representing the physiological characteristic, and a processor subsystem in communication with the sensor subsystem, the processor subsystem being configured to receive at least one of the at least one respiratory signal and the at least one physiological signal.

Respiratory-based biofeedback devices, systems, and methods are provided. A respiratory biofeedback method includes producing a respiratory signal in response to a user's respiratory activity, generating an audio output signal that includes a modified version of the respiratory signal, and converting the audio output signal into sound waves output to the user to provide biofeedback. The sound waves can be output to the user in real time response to the user's respiratory activity. A microphone can be used to generate the respiratory signal. The generated audio output signal can includes the respiratory signal modified to increase a volume level of a portion of the respiratory signal where the volume level exceeds a specified volume level.

A method for recognizing occurrences of breaths in respiratory signals. The method includes receiving digitized respiratory signals that includes tidal volume signals, filtering the received respiratory signals to limit artifacts having a duration less than a selected duration, and recognizing breaths in the filtered respiratory signals. A breath is recognized when amplitude deviations in filtered tidal volume signals exceed a selected fraction of an average of previously determined breaths. This invention also include methods for recognizing breathes from electrocardiogram R-waves; computer methods having code for performing the methods of this invention; monitoring systems that monitor a subject and include local or remote computers or other devices that perform the methods of this invention.

A medical device system for comparing a cardiopulmonary signal to a brain signal. In one embodiment of the invention, a medical device system is provided that includes a brain monitoring element, respiratory monitoring element and a processor. The processor is configured to receive a brain signal from the brain monitoring element and a respiratory signal from the respiratory monitoring element. The processor is further configured to compare the brain signal to the respiratory signal. Methods of comparing a brain signal to a cardiopulmonary signal are also provided.

Disclosed is a sleep stage determination apparatus which comprises respiratory-signal detection means for detecting a variation of a respiratory signal from the body of a human subject, and sleep-stage determination means for determining a plurality of sleep stages using only the respiratory-signal variation. The sleep stage determination apparatus of the present invention can eliminate the need for detecting a plurality of biological signals and have simplified sensor and circuit configurations designed to detect only the respiratory signal variation which is relatively easily detectable. This makes it possible to reduce a detection error and achieve enhanced determination accuracy or creditability.

A signal representative of a patient's respiration is split into equal length epochs. A primary feature is extracted from each epoch that acts as a compressed representation of the signal events. Statistics are applied to the primary feature to produce one or more secondary features that represent the entire epoch. Each secondary feature is grouped with one or more other features that are extracted from the entire epoch rather than selected epoch events. This grouping is the epoch pattern. The pattern is manipulated with suitable classifier algorithm to produce a probability for each class within the algorithm, that the signal may be representative of a disease state (Cheyne-Stokes, OSA etc). The epoch is assigned to the class with the highest probability. Also defined are methods of detecting Cheyne-Stokes breathing by analysing a signal to detect one or regions of hyperpnoea and if the length of a hyperpnoea exceeds a parameter, Cheyne-Stokes is present.

A method for diagnosis of a sleep-related condition of a patient having a thorax. The method includes receiving physiological signals from sensors coupled to the thorax of the patient, and analyzing the physiological signals, independently of any electroencephalogram (EEG) or electro-oculogram (EOG) signals, in order to identify sleep stages of the patient.

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.

A medical device system includes a housing, the housing at least partially supporting a therapy module and a processor, and a respiratory monitoring element coupled to the processor. The processor is configured to activate the therapy module upon detection of a respiratory event in a respiratory signal. In some embodiments, the system may further comprise a brain monitoring element, and the processor may be configured to monitor a brain signal and change the therapeutic output based upon the brain signal.

The present invention is directed to systems and methods for monitoring characteristics of a subject. A system according to an exemplary embodiment of the invention includes a sensor subsystem including at least one respiratory sensor disposed proximate to the subject and configured to detect a respiratory characteristic of the subject, wherein the sensor subsystem is configured to generate and transmit at least one respiratory signal representing the respiratory characteristic, and at least one physiological sensor disposed proximate to the subject and configured to detect a physiological characteristic of the subject, wherein the sensor subsystem is configured to generate and transmit at least one physiological signal representing the physiological characteristic, and a processor subsystem in communication with the sensor subsystem, the processor subsystem being configured to receive at least one of the at least one respiratory signal and the at least one physiological signal.

An active implantable medical device comprising circuits for measuring trans-thoracic impedance and delivering an impedance signal varying with respiratory activity of a patient. A signal representative of the respiratory activity of the patient is delivered starting from the impedance signal, and circuits for diagnosing respiratory disorder analyze variations of the respiratory signal on a plurality of successive cycles to detect there a profile of predetermined variation in relation to a given respiratory disorder. The device also includes circuits for automatically controlling respiratory cycles with artifacts, able to identify in the impedance signal a jump of static impedance, and / or to identify in a respiratory cycle or in a sequence of respiratory cycles a predetermined singularity representative of a cycle with artifact.

Systems and methods are provided for monitoring changes in blood volume using waveforms in the peripheral vasculature. In particular, the systems and methods relate to detecting ventilation-induced variation (VIV) of waveforms in the peripheral vasculature. Advantageously, the systems and methods may relate to analyzing VIV in peripheral venous pressure (PVP). Thus, the VIV of PVP may be measured, wherein decreased VIV is indicative of decreased blood volume In exemplary embodiments, such as involving spontaneous breathing, it may be necessary to account for changes in respiratory signal strength. Thus systems and methods are also provided for assessing coherence between ventilation and VIV for a flow or pressure waveform. Specifically, coherence is evaluated by comparing the waveform to a detected respiratory signal. Finally, systems and method are provided for distinguishing the impact of respiration on the PG signal during hypervolemia as compared to hypovolemia. Such systems and methods may advantageously be utilized to monitor fluid status during fluid replacement.

The invention is applicable to the field of medical apparatuses, and discloses a wireless portable human health and sleep quality monitor, which comprises a sensor module, a central processing unit, an output module, a wireless communication module, a power module and an upper computer. The sensor module is electrically connected with the central processing unit. The central processing unit is used for receiving and processing physiological signals acquired by the sensor module and, at the same time, controlling the sensor module, the output module and the wireless communication module to operate. The wireless portable human health and sleep quality monitor disclosed by the invention can detect the physiological signals of a monitor wearer, such as electrocardiosignals, respiratory signals, body temperature signals, blood oxygen signals and posture signals, in real time, make an assessment on the health and sleep quality of the monitor wearer by assessing and analyzing the physiological signals and upload data to a cloud wirelessly or via the Internet, so that medical workers can conveniently give reasonable suggestions to the wearer.

The invention discloses a wearable human body multiple physiological parameter acquiring device. The wearable human body multiple physiological parameter acquiring device comprises a wearable acquiring band and acquiring clothes, wherein the wearable acquiring band comprises a chest strap, shoulder straps, a waistband and a connecting band located on the chest. The chest strap, the waistband and the connecting band comprise an outer layer and a liner respectively, the chest strap is fixedly connected with the waistband through the connecting band, and the chest strap is fixedly connected with the shoulder straps. Sandwich layers between the outer layers and the liners of the chest strap, the waistband and the connecting band are provided with corresponding electrode buttons, the outer surfaces of the liners of the chest strap, the waistband and the connecting band are correspondingly provided with electrodes for acquiring electrocardiosignals or respiratory signals, and the outer surface of the liner of the chest strap is further provided with a first temperature sensor and a second temperature sensor which are used for acquiring human body temperature signals. The acquiring clothes comprises a pocket provided with an acquiring circuit, sleeve bands for measuring blood pressure and provided with air bags are fixed at the upper-limb positions of sleeves of the acquiring clothes, and the air bags are connected with a pressure sensor in a blood pressure signal interface circuit in the acquiring circuit through air guiding tubes.

Motion artifacts and patient dose during 4D CT imaging are reduced by adaptive control of data acquisition. The respiration signal (310) and CT data acquisition (340) are linked, such that ‘bad’ data from erratic breathing cycles that cause artifacts is not acquired by pausing CT data acquisition (360) when erratic breathing is detected, and not resuming CT data acquisition until steady-state respiration is resumed. Training data is used to develop a tolerance envelope for a respiratory signal such that for erratic breathing cycles the respiratory signal is not within the tolerance envelope (330).

A method and system is provided for monitoring a respiratory signal by radio. The method includes the steps of: converting a change in electric resistance, which is caused by a change in abdominal circumference measured through a rubber waistband that is made of conductive rubber and is mounted on a lower garment of a testee during respiration, into a voltage signal, performing A / D conversion on the voltage signal, and transmitting the converted digital signal to a short distance by radio using a wireless communication protocol for ZigBee; and receiving the respiratory signal transmitted by radio, transmitting it to a computer unit by wire through an RS-232 port that is a serial communication port, and enabling a tester to monitor the respiratory signal through a screen.

This invention relates to a system and a kit for stress and relaxation management. A cardiac activity sensor (101) is used for measuring the heart rate variability (HRV) signal of the user and a respiration sensor (102) for measuring the respiratory signal of the user. The system contains a user interaction device (103) having an input unit (104) for receiving user specific data and an output unit for providing information output to the user. A processor (106) is used to assess the stress level of the user by determining a user related stress index. The processor is also used to monitor the user during a relaxation exercise by means of determining a relaxation index based on the measured HRV and respiratory signals, the relaxation index being continuously adapted to the incoming measured signals and based thereon the processor instructs the output unit to provide the user with biofeedback and support messages. Finally, the processor uses the user specific data as an input in generating a first set of rules defining an improvement plan for self-management of stress and relaxation. The first set of rules is adapted to trigger commands instructing the output unit to provide the user with motivation related messages. Also, at least a portion of said user specific data is further used to define a second set of rules indicating the user's personal goals.

A method for determining the effectiveness of an image transformation process includes acquiring a four-dimensional (4D) image data set, sorting the 4D image data set into separate field-of-view bins using a temporal gating system generating a plurality of deformation vectors using the sorted 4D image data set, and using the plurality of deformation vectors to generate a transformation effectiveness value that is representative of the effectiveness of the image transformation process. The method further includes acquiring a respiratory signal, calculating a power spectrum of the respiratory signal, calculating a power spectrum for each of the plurality of deformation vectors, and comparing the power spectrum of the respiratory signal to the power spectrum of the plurality of deformation vectors to generate the transformation effectiveness value.

A system operable to track a respiratory cycle of a subject is provided. The system includes at least a first sensor positioned on the subject, and at least a second sensor located at a reference relative to a change in position of the first sensor associated with respiration of the subject. The system also includes a respiratory cycle measurement device coupled to receive the position data of the first sensor relative to the reference. The respiratory cycle measurement device is configured to translate the position data of the first sensor relative to time into a respiratory signal representative of a respiratory cycle of the subject.

The present invention relates to a method and a system designed to determine an individual's state of attention from an individual's respiratory signal. The method of the invention comprises a first learning step, wherein the characteristics of an individual's normal state are determined by selecting a respiratory signal fragment that is considered to be normal according to a pre-established criterion, and a second analysis phase, wherein the individual's state of attention is determined from parameters extracted from the respiratory signal on the basis of some pre-defined rules and the individual's normal state previously characterised. The method of the invention is implemented in two embodiments. A first embodiment is based on the identification of pre-defined patterns in the respiratory signal and, in the second embodiment, an index indicative of the variability of the respiratory signal is defined.

A method for identifying an occlusion in a breathing tube of a ventilator system is disclosed herein. The method includes obtaining data related to a respiratory signal, transferring the data to a processor, and implementing the processor to evaluate the data in a manner adapted to automatically identify the presence of an occlusion in the breathing tube. A corresponding ventilator system is also disclosed.

Systems and methods for assessment of sleep quality in adults and children are provided. These techniques include an apparatus worn above the forehead containing the circuitry for collecting and storing physiological signals. The apparatus integrates with a sensor strip and a nasal mask to obtain the physiological signals for the user. The form factor of this apparatus is comfortable, easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data for analyzing sleep quality. Neuro-respiratory signals are analyzed using means to extract more accurate definitions of the frequency and severity of sleep discontinuity, sleep disordered breathing and patterns of sleep architecture.; Biological biomarkers and questionnaire responses can also be compared to a database of healthy and chronically diseased patients to provide a more accurate differential diagnosis and to help determine the appropriate disease management recommendations.

The objective is to obtain a respiratory induction apparatus and a particle beam therapy system in which respiration can appropriately be induced by accurately evaluating the respiration. There are provided a respiratory induction control unit (7cC) that generates a desired respiratory signal (Rtj(t)) for respiratory induction; a real respiration measurement unit (7a) that outputs a real respiratory signal (Rrl(t)) obtained by measuring real respiration of a patient; and a respiration evaluation unit (7cE) in which by, as a unit, utilizing data of a single period (Tres) of the desired respiratory signal (Rtj(t)), there is calculated a pair of coefficients (a1 and b1), of trigonometric functions, which correspond to the 1st-order terms by means of Fourier series expansion of data of the desired respiratory signal (Rtj(t)) and data of the real respiratory signal (Rrl(t)), which is acquired in synchronization with the data of the desired respiratory signal (Rtj(t)), and there are performed comparisons between the respective gains (Gtj and Grl) and between the respective phases (φtj and φrl), which are obtained from the coefficients (a1 and b1), so that there is evaluated a misalignment of the real respiration from the desired respiratory signal.

The invention relates to a heart rate variability biofeedback exercise systematic method, comprising a set of autonomic nervous cardiopulmonary system regulation state and harmonicity metrics and a method of guiding a trainee into harmonious state. A heart rate variability biofeedback exercise apparatus of the invention collects heart rate and respiratory signals of the trainee in real time, analyzes and computes the autonomic nervous cardiopulmonary system regulation state and harmonicity metrics of the trainee, and guides the trainee to regulate mood and respiration to the harmonious state. A heart rate variability biofeedback exercise is effective in regulating autonomic nervous regulation state, thus lowering cardiopulmonary system risk; is effective in relieving competition mental stress, thus increasing the score. Therefore the method and apparatus are significant to keeping people healthy and improving living quality.

The invention discloses a real-time sleep staging detection method based on a piezoelectric sensor and a device for realizing the method. According to the method and the device, a sleep piezoelectric sensing mattress is adopted for collecting BCG (Ballistocardiographic, namely, the ballistocardiography) signals of long-term sleep, and the signals are mixed with the information including the heartbeat, the breath and the body motion during the sleep process. The sleep staging algorithm mixed with multiple signals is adopted, the hidden markov model is applied to the research for the sleep breathing data, by utilizing the advantages of the hidden markov model mixed with the heartbeat and the breath rate at different stages, and in combination with the body motion detecting algorithm, the influences of the body motion for the staging based on the heartbeat and the respiratory signals are reduced, meanwhile, the discernibility of the body motion detecting algorithm for the waking state and the sleep state is enhanced, and further, the long-time automatic sleep staging accuracy rate based on the piezoelectric sensing signals is improved; the algorithm is deployed to a server, so that the automatic sleep staging is realized.