216 results about "Sleep disorder" patented technology

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention provides a sleep stage detection method based on a multi-modal and visual transformation network, and the method comprises the steps: extracting EEG channel signals and EOG channel signals in PSG, carrying out the feature extraction of different frequency bands through a multi-scale convolutional network, and fusing the multi-modal features to enhance the classification accuracy. Thirdly, the time-space relevance of network modeling signals is transformed through vision, and then different stages of sleep are effectively recognized; the method also provides confidence evaluation of the classification result, and the reliability of the detection result is enhanced. The automatic detection method can be used for clinical sleep staging to assist doctors in evaluating sleep quality and diagnosing sleep disorders.

The invention discloses an all-night snore detection method based on a frequency domain attention and self-attention pooling double-fusion mechanism, and belongs to the field of sound event detection. The method comprises the following steps: designing a waveform feature extraction module based on a frequency domain self-attention sub-module, a Mel-cepstrum feature extraction module based on a deformable convolution residual sub-module, a self-attention pooling double-fusion module and a decoder based on a multilayer perceptron; a deep learning snore event detection model based on a frequency domain attention and self-attention pooling double-fusion mechanism is constructed by using the modules. The model can detect and position snoring sound in all-night sleep sound data in a point-by-point framing detection mode by loading trained parameters, and the starting and ending time of a snoring event in all-night time and the corresponding snoring sound type are obtained. The model can rapidly and accurately detect snore segments in the whole night sleep period, and is helpful for assisting medical staff in rapidly screening patients with sleep breathing disorder in the early stage.

The present invention relates to an integrated sleep diagnosis and treatment device, and more particularly to an integrated apnea diagnosis and treatment device. The present invention additionally relates to method of sleep diagnosis and treatment.

The invention relates to an intelligent sleep adjusting method and system based on closed-loop acousto-optic brain wave entrainment, electroencephalogram (EEG) signals are collected and preprocessed in real time through a wireless dry electrode, and high-precision sleep staging is achieved through a deep learning model. The system adopts a fuzzy PID (Proportion Integration Differentiation) controller to dynamically adjust acousto-optic synergistic stimulation: an acoustic module generates binaural beat signals, an optical module outputs blue light (470nm) and amber light (590nm) pulses, and the brain wave entrainment efficiency is enhanced through phase synchronization (the phase difference is less than or equal to 10 degrees) and golden section frequency coupling (fL is equal to 1.618 fA). Parameters are updated every 30 seconds through closed-loop feedback, the sound pressure level (30-50 dB) and the light intensity (10-100 lux) are adjusted according to the Weber-Fechner law, and the response delay is lt; the time is 200 ms. A three-level safety mechanism monitors gamma wave abnormity, epilepsy sample discharge and impedance overrun in real time, and triggers graded protection (alarming, cutting off light stimulation and shutdown). Clinical verifications show that the entrainment success rates of the delta wave and the theta wave respectively reach 71% and 68%, the sleep improvement effect is good, and a safe and efficient intervention scheme is provided for sleep disorders.

The invention discloses portable sleep breathing disorder monitoring equipment based on electroencephalogram, oxyhemoglobin saturation and electrocardio, and belongs to the technical field of sleep monitoring equipment. The monitoring equipment comprises a wireless sleep monitoring module and a sleep breathing disorder diagnosis module, the wireless sleep monitoring module is used for collecting EEG, SpO2 and ECG signals in the sleep process and transmitting the EEG, SpO2 and ECG signals to the sleep dyspnea diagnosis module in real time for signal processing and analysis. The equipment collects multiple modes directly related to accurate diagnosis of sleep breathing disorder, and huge deviation of evaluation of sleep breathing disorder caused by a single mode is avoided. A sensor for collecting forehead electroencephalogram and oxyhemoglobin saturation is integrated on the flexible circuit board, so that the integration level and portability of the device are higher. A multi-modal fusion algorithm is designed for multi-modal signals collected by equipment, and multi-task and accurate detection of automatic sleep staging and sleep breathing events is achieved.

The invention discloses a compound composition for improving sleep disorders, which is characterized by comprising the following components: an inhibitory neurotransmitter component, melatonin and a nano-lipid carrier, the nano-lipid carrier comprises phospholipid and cholesterol, the weight ratio of phospholipid to cholesterol is (1-6): 1, the weight ratio of phospholipid to cholesterol is (1-6): 1, and the weight ratio of melatonin to cholesterol is (1-6): 1. The inhibitory neurotransmitter component is wrapped in the nano-lipid carrier to form lipid nanoparticles, and the weight ratio of the inhibitory neurotransmitter component to the nano-lipid carrier is 1: (1-15). The compound composition can be prepared into a compound oral mucosa preparation, can be rapidly disintegrated when encountering saliva in the oral cavity, is good in patient compliance and small in side effect, all the components play a synergistic effect from different mechanisms through multiple target points, and the compound oral mucosa preparation can improve the sleep quality, prolong the sleep time and shorten the sleep latency; and patients with sleep disorders are helped to improve insomnia from the pathologic root.

The invention discloses a processing system and equipment for sleep disorder intervention and a storage medium, and relates to the technical field of information processing systems. The system comprises a user terminal and a sleep intervention analysis device, the user terminal is used for collecting treatment period data information of a sleep disorder patient in a CBTI treatment process; the sleep intervention analysis device is used for configuring CBTI treatment requirement information of a patient, then comparing the treatment period data information of the patient with the CBTI treatment requirement information corresponding to the patient, obtaining difference information between the treatment period data information and the CBTI treatment requirement information, and then obtaining treatment matter difference information of the patient. And generating a sleep management suggestion report including the treatment item gap information and displaying the sleep management suggestion report to the patient. According to the method, the sleep management suggestion report including the treatment item gap information can be generated, so that the patient can perform self-sleep management, and the difficulty of processing and completing the CBTI therapy treatment plan by the patient is reduced.

Monitoring sleep disorders in a subject using a mobile device, such as a smartphone or other wearable device, includes: co-locating the mobile device with the subject; acquiring an audio signal indicative of the respiration of the subject by means of a microphone of the mobile device; acquiring an accelerometer signal including posture, angle and motion information of the tested object by means of an accelerometer of the mobile device; measuring thoracic movements associated with respiration and respiration of sleep disorders by means of an accelerometer of the mobile device; identifying a plurality of silence events by means of the audio signal; classifying the silence event as apnea or hypopnea; classifying the apnea into a plurality of apnea types by means of thoracic motion recorded with the accelerometer sensor; and associating the classified silence events with respective angle information by combining audio and accelerometer signals.

The invention relates to the technical field of physiological signal analysis, in particular to a sleep disorder intelligent diagnosis system based on a multi-modal knowledge graph, which comprises a signal lag analysis module, an abnormal signal identification module, a graph construction module, a graph path generation module and a diagnosis result output module. According to the method, difference comparison and normalized sequence analysis in a time window are performed on multiple types of physiological response data, and derivative trend identification and mutation section calibration are combined, so that collaborative identification of signal delay response and abnormal change positions is realized, and multi-dimensional information of duration, frequency and fluctuation amplitude in abnormal events is further structured; the method comprises the following steps: constructing a traceable symptom expression vector, extracting structural features of the traceable symptom expression vector in a time sequence evolution process through a cross-time window entity evolution path, identifying a growth trend of association strength between symptoms according to an attribute change direction and a path connection mode, and judging a fluctuation mode and an abnormal evolution tendency of a sleep state of a user in a specific time period.

An implantable neuromodulation system can include an electrode configured to be disposed at or near a first branch of a hypoglossal nerve of a patient and configured to provide a first electrostimulation therapy to treat a sleep disorder or breathing disorder of a patient. The system can include a sensor configured to provide a sensor signal that includes information about a patient response to the first electrostimulation therapy, information about a patient respiration, or information about one or more other physiologic status indicators for the patient. The sensor signal information can be used to quantify a progression of a breathing disorder for the patient.

A method of reducing or treating snoring, sleep apnea and other forms of sleep disordered breathing, comprising having a patient in need thereof sleep in contact with or in the proximity of an article containing copper. A fabric having anti-snoring, sleep apnea and other forms of sleep disordered breathing properties, the fabric comprising a copper.

The invention discloses a household collaborative nursing management system for diabetic sleep disorders and an implementation method, relates to the technical field of nursing management, and aims to automatically establish and adjust a layered nursing structure according to health data of a patient, realize correlation analysis among multiple targets, ensure that the most urgent health problem is effectively intervened firstly, and improve the nursing efficiency. The information collaboration degree is utilized to fuse related data of a plurality of nursing targets, accuracy and high efficiency of the data are ensured, so that treatment and nursing strategies are optimized, mutual support and coordination of various nursing targets are ensured, conflicts among targets in a traditional treatment scheme are avoided, dynamic adjustment is carried out through a self-adaptive optimization algorithm, and the treatment efficiency is improved. According to the health state change of the patient, the nursing scheme is adjusted in real time, it is guaranteed that the patient can obtain personalized and continuously-optimized nursing management, the treatment effect of diabetes and sleep disorder dual management is remarkably improved, and the life quality and treatment compliance of the patient are improved.

The present invention provides a method of treating Parkinson's disease by light stimulation in which light of a specific wavelength such as violet light is irradiated at a specific blinking frequency. The present invention, by irradiating light of a specific wavelength such as violet light to a living body, carries out treatment and / or prophylaxis of Parkinson's disease by improving or suppressing at least one symptom selected from a group of symptoms consisting of cognitive memory disorder, working memory disorder, dysphoria, anxiety, apathy, psychological symptoms (hallucination, visual hallucination, and auditory hallucination), impulse control disorder, orthostatic hypotension, urinary disorder, sexual dysfunction, digestive symptoms (drooling, swallowing, nausea, constipation), sleep disorder, pain, weight loss, olfactory abnormality, fatigue, and diaphoresis.

The composition disclosed by the invention not only can improve the sleep state or maintain deep sleep, but also can recover intestinal microorganism balance caused by sleep disorder, thereby being beneficial to health management of metabolic syndrome and the like.

The present invention relates to a valerian composition and / or valerian extract for the prevention, improvement, and maintenance of sleep disorders and / or sleep management, and as such, improves sleep quality, improves sleep time, improves sleep duration, reduces sleep disturbances, and maintains healthy stress levels in subjects in need thereof. A valerian composition comprising a valerian extract and at least one pharma- tically and / or nutraceutical acceptable excipient. The present invention further encompasses the valerian composition, which is further formulated in various forms, such as powders, beadlets, granules, capsules, tablets, oil suspensions, films, or any other suitable oral or topical dosage forms, using pharma-tically and / or nutraceutical acceptable excipients.

The invention provides a formula of sleep-aiding essential oil containing cedar himalaya, relates to the technical field of sleep-aiding essential oil, and aims to solve the problems of single effect, poor stability and insufficient skin tolerance of existing sleep-aiding essential oil. The formula comprises the following components in parts by weight: 0.5-5 parts of Himalaya cedar essential oil, 80-95 parts of base oil, 1-10 parts of synergistic sleep-aiding essential oil, at least one of lavender essential oil, chamomile essential oil and sandalwood essential oil, and 0.1-1 part of optional antioxidant. During preparation, the components are ensured to be uniform through a process of stirring at a constant temperature of 25-35 DEG C and mixing at a rotating speed of 200-300rpm, the pH value of the product is 5.5-7.0, the retention rate of the active components is greater than or equal to 90% after the product is stored in a dark place at 25 DEG C for 6 months, and the use mode is that the product is smeared on wrists / necks / soles 1-2 hours before sleep, and 0.5-2mL of the product is smeared on each time. Tests show that the sleep time of insomnia people can be shortened to 15-22 min after the essential oil is continuously used for 28 days, the PSQI score is reduced by 40%-54%, the skin irritation is zero, the essential oil is suitable for preparing external sleep-aiding essential oil cream, ball bottles and other products, and a safe and efficient solution is provided for sleep disorder people.

The invention discloses a sleep disorder treatment effect evaluation system based on multiple modes, which relates to the technical field of sleep disorder treatment effect evaluation and comprises a multi-mode data acquisition module, a multi-mode data preprocessing module, a multi-mode evaluation module, a sleep disorder treatment monitoring module and a sleep disorder treatment evaluation module, the multi-modal data acquisition module is used for acquiring multi-modal data through acquisition equipment; the multi-modal evaluation module is used for acquiring evaluation indexes of the vital sign data, the questionnaire data and the sleep environment data on the sleep disorder treatment effect. A multi-modal data acquisition technology, a data preprocessing technology and a neural network model construction technology are closely combined with a modern information technology; the problems that in the prior art, the sleep disorder treatment effect cannot be evaluated in combination with multi-modal data, and monitoring of the evaluation effect of the multi-modal data is lacked are solved.

The invention relates to a traditional Chinese medicine preparation for treating mild and moderate spleen and kidney deficiency and phlegm and blood stasis type Alzheimer's disease sleep disorder and a preparation method of the traditional Chinese medicine preparation, and belongs to the field of traditional Chinese medicine treatment preparations for Alzheimer's disease. The traditional Chinese medicine preparation is prepared from the following traditional Chinese medicine raw materials: rhizoma acori graminei, borneol, poria with hostwood, rhizoma atractylodis macrocephalae, codonopsis pilosula, radix astragali preparata, fructus evodiae, ramulus euonymi, polygala tenuifolia, lily, caulis polygoni multiflori and fossil fragments. The traditional Chinese medicine composition is used for treating mild and moderate spleen and kidney deficiency and phlegm and blood stasis type Alzheimer's disease sleep disorder, and has a good treatment effect. Sleep of patients with mild and moderate spleen and kidney deficiency and phlegm and blood stasis type Alzheimer's disease sleep disorder is evaluated through the intelligent bracelet, sleep condition quantitative monitoring is conducted, traditional Chinese medicine acupoint application treatment is conducted through a traditional Chinese medicine characteristic therapy method, the sleep quality and structure changes of the patients are evaluated, the treatment effect of traditional Chinese medicine acupoint application on the patients is researched, and the clinical application prospect is wide. Therefore, a clinical basis is provided for objective evaluation and standardized treatment of the sleep disorder, and the method has very important clinical significance for improving the life quality of a patient and evaluating prognosis.

The invention discloses a real-time self-adaptive electroencephalogram artifact suppression and enhancement embedded system. The system aims at overcoming the limitation of an existing portable EEG device in the aspect of artifact processing, various artifacts in EEG signals are recognized and removed in a low-power-consumption, high-precision and real-time mode, and meanwhile the quality of effective EEG signals is optimized and enhanced. One key innovation of the invention lies in breaking through the limitation of single EEG signal processing, and accurate separation and correction of artifacts are carried out through the assistance of EOG and EMG. For example, the EOG signal is used for assisting eye movement artifact removal through synchronous correlation analysis and an adaptive regression filter; the EMG signal is used for assisting myoelectricity artifact correction, and suppression is carried out through spectral analysis and dynamic adjustment of filter parameters. In addition, the system also has the functions of real-time detection and repair of electrode artifacts. According to the system, high-quality EEG signals close to offline processing are provided on portable embedded equipment with limited resources, and the system has universality and high-precision application potential and can be widely applied to a plurality of frontier fields such as medical diagnosis, brain-computer interface (BCI), cognitive ability enhancement, neurological rehabilitation training, sleep disorder monitoring and human-computer interaction.

The invention provides a sleep breathing state monitoring method, a sleep monitoring system, equipment and a medium. The method comprises the following steps: generating a time-frequency spectrogram of a normal mixed heart impact signal; screening out an area associated with the heart rate in the time-frequency spectrogram based on the mask matrix to obtain time-frequency spectrum information; decomposing an intrinsic mode function of a normal mixed heart impact signal according to the information of the trained mask; time domain waveform information in the intrinsic mode function is extracted; and performing fusion analysis on the time-frequency spectrum information and the time-domain waveform information to generate a sleep apnea classification result. According to the sleep breathing state monitoring method provided by the invention, the time-frequency spectrum information with high quality and extremely low noise is obtained, the time-domain waveform information without distortion is also obtained, and further through fusion analysis of the time-frequency spectrum information and the time-domain waveform information, comprehensive analysis of sleep apnea events is realized, so that the sleep apnea state monitoring accuracy is improved. An accurate sleep apnea classification result is generated, and powerful technical support is provided for diagnosis and treatment of sleep disorder diseases.

An example computer system analyzes nasal airflow data to detect sleep disorders. The system can estimate the breathing cycle frequency on the pressure nasal airflow data. Based on this frequency, the system can calculate optimal smoothing window sizes. The system can perform a smoothing operation on the airflow data and auxiliary sensor data using these windows. Breathing cycles can be detected in the smoothed data streams. Cycles meeting artifact conditions but not sigh conditions can be removed as noise. Remaining clean cycles can be analyzed further. A transition segment between the peak of one cycle and trough of the next can be classified. Using this classification and the cleaned auxiliary data, the system can detect sleep disorder events.

Intelligent systems and methods for facilitating insomnia therapy are disclosed. Sensor data (e.g., non-contact sensor data) can be used to determine physiological parameter(s), such as sleep-related physiological parameter(s), which can be used to generate a sleep disorder prediction. The sleep disorder prediction can be used, along with an identified sleep therapy plan, to generate and facilitate application of (e.g., present to a user or automatically apply) a sleep therapy recommendation. When sleep apnea is predicted in concert with an identified cognitive behavioral therapy for insomnia (CBTi) plan, a warning can be presented to the user to not engage in certain CBTi therapies. Sensor data can also be used to automatically update therapy parameter(s) of an ongoing sleep therapy plan, such as in realtime.

The invention relates to the technical field of health food, in particular to a nervonic acid nutrient composition for relieving sleep disorder and application of the nervonic acid nutrient composition for relieving sleep disorder. The active components comprise natural vegetable oil such as acer truncatum seed oil purified nervonic acid, linseed oil and the like and gamma-aminobutyric acid extracted by fermentation; the synergistic component is prepared from soybean enzymolysis phosphatidylserine, saccharomycetes fermentation coenzyme Q10 and plant source B vitamins; the slow-release carrier adopts pectin-Arabic gum composite gel and plant starch to form a natural cross-linked structure, and long-acting release for 8-12 hours is realized through physical cross-linking. Green technologies such as ultrasonic dispersion and low-temperature drying are adopted in the preparation process, and chemical synthesis steps are avoided; the composition can improve the difficulty in falling asleep and improve the sleep quality, the activity retention rate is greater than or equal to 85% after the composition is stored at 45 DEG C for 6 months, and the composition is suitable for preparing capsules, gel candies and other health-care foods and has no dependence after being taken for a long time.

The invention relates to a heterocyclic carbonyl derivative regulator as well as a preparation method and application thereof. In particular, the present invention relates to a compound represented by general formula (I), a preparation method thereof, a pharmaceutical composition containing the compound, and a use thereof as a modulator in the treatment of Alzheimer's disease, schizophrenia, pain, addiction and sleep disorders, each substituent in the general formula (I) being as defined in the specification. # imgabs0 #

The invention relates to the technical field of development of devices for promoting sleep, in particular to application of 70dB white noise in preparation of a device for promoting sleep. The research finds that the 70dB white noise has the effects of promoting sleep and improving the sleep quality. The invention provides an application of 70dB white noise in preparation of a device for promoting sleep. White noise is a uniformly distributed sound signal and can effectively cover burst noise in the environment, so that a more quiet sleep environment is created for a user. As one of non-invasive sensory stimulation, white noise can be used as a non-drug sleep disorder treatment method, side effects are small, and the method has a good application prospect.

The invention discloses a peanut sprout water extraction enriched product, a preparation method and application of the peanut sprout water extraction enriched product in a sleep-aiding composition. The content of resveratrol in the peanut sprout water extraction enriched product is 0.8-1.5 mg / g, the content of total amino acid is 100-200 mg / g, the content of tryptophan is 0.24-0.30 mg / g, and the molar ratio of tryptophan to magnesium metal is 1: (0.4-0.6). The content of resveratrol in the obtained peanut sprout water extraction enriched product is high, the peanut sprout water extraction enriched product has a directional enrichment effect on tryptophan and metal magnesium ions, and research proves that the peanut sprout water extraction enriched product has a remarkable effect on improving sleep disorders.

Examples of the disclosure include a system for treating disordered breathing in a patient, including an electrical-pulse generator (IPG) to generate an electrical stimulation and including a controller and a power source, a lead including a first sensor, and a second sensor, the controller being configured to provide, via the lead, first electrical stimulation to a target nerve including a phrenic nerve, receive, from the lead, respiratory information indicative of a respiratory parameter of the patient, determine, based on the respiratory information, a therapy-specific indicator indicative of an effect of the stimulation, receive, from the at least one second sensor, non-respiratory information, determine, based on the non-respiratory information, a patient-specific indicator relating to at least one patient condition, modify, based on at least one of the indicators, at least one parameter of the stimulation, and provide, via the lead, at least one second electrical stimulation having the modified parameter.

The invention provides a sleep disorder animal model device and an experiment method, the sleep disorder animal model device comprises an experiment box, the experiment box is internally provided with grid division boxes in grid distribution, and the sleep disorder animal model device integrates a liftable auxiliary box body, a cover plate mechanism capable of amplitude modulation and jittering and a sound, light, vibration and tactile stimulation module; complex stimulation close to insomnia inducements of real human beings can be applied in a staged and combined mode, the problem of strong stress caused by a traditional single stimulation model is effectively solved, and the clinical goodness of fit of an animal model is remarkably improved. Meanwhile, based on intelligent management of a central control system, real-time non-invasive monitoring of animal behaviors and sleep states is achieved by fusing camera visual analysis, pressure sensing and wireless physiological telemetry technologies, execution units are automatically and accurately regulated according to the real-time non-invasive monitoring, standardization and repeatability of the experimental process are ensured, and the experimental efficiency is improved. And the construction efficiency and the evaluation reliability of the sleep disorder animal model are improved.

The invention discloses a sleep environment adjusting method of a sleep monitoring and sleep disorder intervention sleep bed, which comprises the following steps: collecting and sorting physical state data and environmental parameters of a historical user before and after falling asleep to form a historical database, classifying sleep disorders in the historical database, sorting out user physiological feature data, user environment feature data and user behavior feature data according to the sleep disorder data, and performing real-time sleep quality evaluation to obtain a real-time sleep quality coefficient; according to the method, the sleep user is monitored in real time, the user physiological feature data, the user environment feature data and the user behavior feature data are obtained, the expected sleep duration of the sleep user is loaded, a historical database is formed through the body state data and the environment parameters, and the sleep quality expected curve is established; and performing real-time sleep quality evaluation according to the user physiological feature data, the user environment feature data and the user behavior feature data.