265 results about "Sleep Stages" patented technology

Systems and methods involve automatic activation, de-activation or modification of therapies or other medical processes based on brain state. A medical system includes a sensor system having one or more sensors configured to sense signals related to the brain state of the patient. A brain state analyzer detects various brain states, including sleep stage and/or brain seizures. A controller uses the brain state detection information to control a medical system configured to perform at least one respiratory or cardiac process. Methods involve sensing signals related to brain state and determining the brain state of a patient based on the sensed signals. At least one respiratory or cardiac medical process is controlled based on the patient's brain state.

A sleep efficiency monitor and methods for pacing and leading a sleeper through an optimal sleep pattern. Embodiments of the present invention include a physiological characteristic monitor for monitoring the sleep stages of a sleeper, a sensory stimulus generator for generating stimulus to affect the sleep stages of a sleeper, and a processor for determining what sleep stage the sleeper is in and what sensory stimulus is needed to cause the sleeper to move to another sleep stage. A personalized sleep profile may also be established for the sleeper and sleep guided in accordance with the profile parameters to optimize a sleep session. By providing sensory stimulus to a sleeper, the sleeper may be guided through the various sleep stages in an optimal pattern so that the sleeper awakens refreshed even if sleep is disrupted during the night or the sleeper's allotted sleep period is different than usual. Embodiments of the invention also involve calibration of the sleep guidance system to a particular sleeper.

Devices, systems and methods to modify memory and/or cognitive function by delivering a sensory stimulus paired with learned material at opportune physiological periods during sleep. Described herein are systems, methods and devices to enhance a user's cognitive function in memorization and learning. A machine (e.g., a system or device) may be used to identify opportune periods of the sleep cycle and to deliver a stimulus during specific phases of the sleep cycle to facilitate or interrupt memory consolidation. The machine may record ambient sensory inputs during awake acquisition or reinforcement/relearning and replay all or an extracted form of the ambient sensory stimuli during a specified portion of the user's sleep. A stimulus may be delivered to a user to affect brain activity during sleep to modulate the phase of a user's sleep, quality of sleep, and/or quality or intensity of brain rhythms during a particular phase of sleep.

A method for operating a sleep phase actigraphy synchronized alarm clock that communicates with a remote sleep database, such as an internet server database, and compares user physiological parameters, sleep settings, and actigraphy data with a large database that may include data collected from a large number of other users with similar physiological parameters, sleep settings, and actigraphy data. The remote server may use “black box” analysis approach by running supervised learning algorithms to analyze the database, producing sleep phase correction data which can be uploaded to the alarm clock, and be used by the alarm clock to further improve its REM sleep phase prediction accuracy.

The invention relates to an alarm clock system. The system according to the invention comprises sensor means for measuring ambient parameters. In particular, a user's body parameters are monitored so as to determine in which stage of sleep he is. Properties of the wake-up stimulus, such as sound volume of the stimulus or moment of generation of the stimulus, are adjusted in dependence on the inferred stage of sleep.

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).

An evaluation of heart failure status is provided based on a disordered breathing index. Patient respiration is sensed and a respiration signal is generated. Disordered breathing episodes are detected based on the respiration signal. A disordered breathing index is determined based on the disordered breathing episodes. The disordered breathing index is trended and used to evaluate heart failure status. The disordered breathing index may be combined with additional information and/or may take into account patient activity, posture, sleep stage, or other patient information.

The present invention relates to systems and methods for automatically and continuously regulating the level of nasal pressure to an optimal value during OSA treatment. OSA therapy is implemented by a device which automatically re-evaluates an applied pressure and continually searches for a minimum pressure required to adequately distend a patient's pharyngeal airway. For example, this optimal level varies with body position and stage of sleep throughout the night. In addition, this level varies depending upon the patient's body weight and whether or not alcohol or sleeping medicine has been ingested.

A method of determining sleep stages from signals of electrical activity recorded of a chest of a sleeping subject, the signals being measured over a plurality of epochs. The method comprising: (a) extracting a series of cardiac R-R intervals from the signals and obtaining a time-frequency decomposition from the series of cardiac R-R intervals; (b) using the time-frequency decomposition to determine at least one Slow-Wave-Sleep (SWS) period and at least one Non-SWS (NSWS) period; (c) from the at least one NSWS period, determining at least one sleep-onset (SO) period and a plurality of non-sleep periods; (d) extracting a plurality of electromyogram (EMG) parameters from a portion of the signals, the portion corresponds to a NSWS period other than the at least one SO period and other than the plurality of non-sleep period; (e) using the plurality of EMG parameters to determine at least one REM period thereby also to obtain also at least one light-sleep (LS) period defined as a NSWS period other than the SO periods, other than the non-sleep periods and other than the REM periods; thereby determining the sleep stages of the sleeping subject.

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.

Systems, methods, and/or apparatuses for acclimatizing a user to positive airway pressure (PAP) therapy are provided. Generally, a sub-therapeutic treatment pressure is provided initially. It may be ramped up to a full treatment pressure over the course of one or more therapy sessions. The pressure level may be ramped up based on, for example, sleep state, sleep phase, patient compliance with types of treatment (e.g. bilevel vs. CPAP, etc.), clinician input (either at the site, remotely, via pre-programmed smartcards, etc.), etc. Such techniques may be used alone or in combination.

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).

Systems and methods for evaluating a patient condition using autonomic balance information involve providing an implantable cardiac device that acquires a cardiac waveform from a patient. One or more characteristics associated with autonomic balance of the patient are detected and used to evaluate a patient condition, such as sleep onset, sleep stage, cardiac vulnerability over a predetermined duration, and sleep disordered breathing. Patient activity levels may be sensed and used to evaluate the patient's condition, such as for determining a level of systemic stress. Characteristics associated with the autonomic balance include calculating an LF/HF ratio waveform and/or determining one or more morphological features of the LF/HF ratio waveform. Coordination with a patient-external device may facilitate transmission of information about one or more of the cardiac waveform, the one or more characteristics associated with the autonomic balance, and a marked cardiac waveform.

A short sleep/nap management apparatus and method are disclosed. The apparatus has sensor means (35) to detect one or more physiological parameters associated with a transition in sleep stages (13-19) from wakefulness (13), processing means (37, 39, 33) to process the parameters to determine when the transition is reached and start the timer to run for a predetermined period, and alarm means (47) to actuate at the end of said predetermined period to awaken the user.

A system that determines one or more sleep phenotyping parameters of a subject. In one embodiment, the system comprises a sleep sensor, a stimulus generator, and a processor. The sleep sensor generates signals that convey information related to the physiological functions that indicate the sleep stage of the subject. The stimulus generator provides a stimulus to the subject that enables information related to the sleep phenotyping parameters to be determined. The processor receives the signals generated by the sleep sensor and is in operative communication with the stimulus generator. The processor (i) determines, based on the signals received from the sleep sensor, whether a trigger condition related to the current sleep stage of the subject is satisfied, (ii) controls the stimulus generator to provide the stimulus to the subject if the trigger condition is satisfied, and (iii) quantifies the response of the subject to the stimulus.

Systems and methods detect respiration of a subject, process respiration data, and based on the processed respiration data, perform extended monitoring. A monitoring system is configured with sensors that can be worn by the subject to provide the respiration data. The respiration data is processes to create a stability index. The stability index is used to, for example, determine sleep stages.

The present disclosure pertains to a system and method for determining sleep stages during individual sleep cycles based on algorithms and/or parameters that correspond to the individual sleep cycles. The system enables more accurate real-time sleep stage determinations compared to prior art systems. Sleep cycles are detected in real-time based on an electroencephalogram (EEG), and/or by other methods. At the end of a sleep cycle, the system is configured such that the specific algorithms and/or parameters used for the previous sleep cycle to determine sleep stages are replaced by new ones which are specifically adapted for the next sleep cycle.

The invention relates to the field of identification of sleep EGG (electroencephalogram) detection signals and discloses a method for classifying sleep stages on basis of sleep EGG (electroencephalogram) signal detection. The method comprises steps as follows: (1), signal acquisition equipment is connected with a left frontal pole and a right frontal pole and is used for acquiring EGG signals, electrooculogram signals and lower jaw electromyographic signals; (2), filtering is performed, the signals are subjected to filtering processing through a low pass filter, and the signals with frequency higher than 50 Hz are cut off, and the signals with frequency lower than 50 Hz are allowed to pass; (3), the signals are subjected to frequency band division, and a time domain is subjected to band division processing with 30s as a unit; (4), characteristic values are extracted, energy of each frequency band is calculated, and an energy ratio is taken as a characteristic value of each frequency band. According to the method, a polysomnography is simplified through the acquisition equipment, then the acquired signals are processed, and the signals of the frequency bands are separated, so that which sleep stage a detected person is in is determined; the overall process is simple to operate, the analysis is automated, and the problem of insufficiency of equipment in a hospital can be effectively solved.

The invention provides an air conditioner sleep control method as well as a device and an air conditioner. The air conditioner sleep control method comprises the following steps of: judging whether a human body goes to sleep or not according to a sign parameter value; while judging that the human body goes to sleep, controlling the air conditioner to switch to a sleep mode from an existing operation mode, and controlling the air conditioner according to the sleep mode; and while judging that the human body does not go to sleep, keeping a before-sleep operation mode. According to the technical scheme adopted by the invention, the operation of the sleep mode corresponds to environmental requirements and a sleep state of a true sleep stage of the human body, so that accuracy effectiveness and intelligent property of sleep mode control are improved, use comfort of the air conditioner by a user is improved, different requirements on the environmental requirements and an air outlet direction after the human body is in different sleep stages are met better, and energy saving is achieved to certain extent while sleep comfort of the user is improved, and therefore, energy consumption of the air conditioner is improved.

Apparatus for monitoring a clinical condition of a subject is described. A motion sensor monitors the subject, and generates a signal in response thereto. A control unit analyzes the signal, and, in response to the analyzing, (a) identifies a sleep stage of the subject, and (b) identifies a clinical parameter of the subject in the identified sleep stage. The control unit monitors the clinical condition, by comparing the clinical parameter to a baseline clinical parameter for the identified sleep stage, and generates an output in response thereto. Other applications are also described.

Major depression can affect multiple physiologic systems. Analysis of signals that reflect integrated function may be useful in probing dynamical changes in this syndrome. Complex variability can be used as a marker of healthy, adaptive control mechanisms and dynamical complexity decreases with aging and disease. The heart rate (HR) dynamics in non-medicated, young to middle-aged males during an acute major depressive episode exhibit lower complexity compared with healthy counterparts. By analyzing HR time series, a neuroautonomically regulated signal, during sleep, using the multiscale entropy method, a measure of complexity of HR dynamics can be determined. The complexity of the HR dynamics is significantly lower for depressed than for non-depressed subjects for the entire night and combined sleep stages 1 and 2, providing an indication of depression. These complexity signals, individually, or in combination with the complexity of other physiologic signals, can be used to define novel dynamical biomarkers of depression.

The invention provides an automatic sleep stage classification method based on dual character filtering. The stage classification method comprises steps of firstly extracting a two-guidance sleep electroencephalogram signal and an one-guidance horizontal electrooculogram signal; performing filtering for the original electroencephalogram signals and electrooculogram signal; extracting multiple characters from the filtered electroencephalogram signals and the filtered electrooculogram signal; selecting the optimal character subsets by use of a dual character filtering method combining the Fisher score method and the sequential forward selection method. Via the dual characteristic filtering method, character dimension is greatly reduced and redundancy among the characters is reduced. At last, a support vector machine classifier is used for identifying the optimal characters, so automatic stage classification of sleep is finished. According to the invention, objectivity, precision and convenience of automatic sleep stage classification can be well increased; the automatic sleep stage classification method is characterized by high precision, low calculation complexity, simple operation and easy popularization; and considerable social and economic benefit can be gained.