615results about "Audiometering" patented technology

Techniques and apparatuses are described that perform audioplethysmography and motion-sensing data fusion. Fusing motion-sensing data with audioplethysmography expands the situations in which audioplethysmography can operate. In one aspect, audioplethysmography and motion-sensing data fusion can be used for motion-artifact filtering. With motion-artifact filtering, noise caused by motion of a user can be attenuated to improve sensitivity and accuracy for audioplethysmography. This performance improvement expands the ability of audioplethysmography to support use cases during situations in which the user engages in an activity. In another aspect, audioplethysmography and motion-sensing data fusion can expand a functionality of a hearable to include activity detection and / or activity classification. While activity detection and / or activity classification can provide additional contextual information for other use cases associated with audioplethysmography, either can also be used to control an operation of the hearable and / or a computing device.

The invention belongs to the technical field of hearing testing, and relates to a self-adaptive hearing screening optimization method based on acoustic feature analysis, which comprises the following steps: generating audio data with different signal-to-noise ratios through a constructed audio library, measuring an acoustic transfer function of a user ear canal by using a probe microphone, and generating a personalized acoustic transfer function in combination with a head-related transfer function. Time domain and time frequency features are extracted by means of EEG data, and the auditory reaction ability and the spatial auditory ability of the user are quantified in combination with the spatial positioning features of the binaural simulation microphones; the method comprises the following steps: constructing a multi-modal auditory feature vector, weighting features in combination with a multi-head attention mechanism, predicting the hearing age of a user through a time sequence Transform encoder in combination with the age of the user and a standard threshold value, and evaluating hearing loss according to a prediction result; the individual difference of each user can be accurately adapted, the defect that individual difference and complex environmental noise are neglected in a traditional hearing screening method is overcome, and therefore the accuracy of a screening result is improved.

The invention provides a sound coordination method and system based on a tinnitus condition, and relates to the technical field of tinnitus treatment.The method comprises the steps that a tinnitus feature vector of a patient is established through broadband hearing testing and three-dimensional sound field modeling, and a dual-mode treatment audio is generated based on the tinnitus feature vector; the dual-mode treatment audio comprises offset sound waves with opposite phases and masking sound waves with matched frequency bands; establishing a dynamic mapping relationship between a game interaction event and the audio parameters of the dual-mode treatment audio; and dynamically adjusting treatment parameters according to the dynamic mapping relation and the real-time physiological feedback data. The tinnitus characteristic vector of the patient is accurately established through comprehensive application of broadband hearing testing, three-dimensional sound field modeling and binaural sound field matching, and the treatment audio is dynamically adjusted according to personalized characteristics of tinnitus. According to the personalized sound coordination method, the tinnitus symptom can be effectively relieved according to the tinnitus symptom of each patient, and the treatment compliance of the patient is improved.

The invention discloses an auditory cognitive impairment evaluation and screening system, which belongs to the technical field of data analysis, and specifically comprises the following steps: setting basic test parameters and initial stimulation parameters, and synchronously acquiring voice, eye movement trajectory data and brain wave signals by combining space-time anchoring marks to form a multi-modal data set with a timestamp; a multi-stage cognitive test is carried out in an adaptive test engine, and stimulation parameters are dynamically adjusted by using a forgetting curve prediction algorithm according to real-time accuracy and response time; based on the adjusted stimulation parameters, performing time domain alignment on the multi-modal data set by adopting a dynamic time warping algorithm and taking a space-time anchoring mark as a benchmark, extracting features and combining the features into a cross-modal feature vector; and the cross-modal feature vectors are input into a pre-trained LSTM-decision tree fusion diagnosis model, and a quantitative evaluation report of obstacle type and degree grading is output, so that the accuracy of cognitive disorder diagnosis is improved.

The invention relates to an apparatus (1) and method for automatically determining the blood flow direction (42) in a blood vessel (14) using the fluorescence light from a fluorophore (16). Blood flow direction (42) is determined by first identifying a blood vessel structure (38) in an input frame (6) from a camera assembly (2) using a pattern recognition module (26). Blood flow direction (42) is determined from the spatial gradient (dl / dx) of the fluorescence intensity (I) along the identified blood vessel structure (38) and the temporal gradient (dl / dt). An output frame (48) is displayed on a display (36) with time-varying marker data (52) overlaid on the identified blood vessels structure (38) and representative of the blood flow direction (42).

The invention discloses a Eustachian tube blowing VR training method and system based on an image sensor, and relates to the technical field of biological feature recognition, the method comprises the steps that training tasks are set, corresponding VR scenes are constructed, and each task corresponds to a tympanic membrane standard motion model; a user wears VR equipment to select a training task, the system collects tympanic membrane image data, extracts dynamic characteristic parameters such as tympanic membrane displacement, deformation curvature and response frequency, compares the dynamic characteristic parameters with a standard model, generates a training score and displays the training score in a VR environment. The technical problems that the training effect is difficult to guarantee and the user experience is poor due to the fact that an existing Eustachian tube blowing training method lacks an objective quantitative evaluation and real-time feedback mechanism are solved, objective quantitative evaluation and real-time feedback of the tympanic membrane dynamic characteristics are achieved by combining the VR technology and the image sensor technology, and the training efficiency is improved. And the technical effects of training effect and user experience are obviously improved.

The invention relates to a hearing threshold prediction method based on frequency sweep OAEs and a deep learning model, and the method comprises the steps: employing a frequency sweep stimulation signal normal form with a unified mode to extract an SFOAEs signal and a DPOAEs signal, the stimulation sound normal form of the SFOAEs signal is a four-segment frequency sweep signal, and the stimulation sound normal form of the DPOAEs signal is a single-segment frequency sweep signal. And carrying out interference signal filtering on a residual signal of the single DPOAEs signal or the SFOAEs signal through a tracking filter with a dynamically changed zero pole so as to extract frequency sweep OAEs spectrum information. And constructing a data set of a deep learning model based on the sweep frequency OAEs spectrum information, and training and evaluating the deep learning model to obtain a hearing threshold prediction model. And calling the hearing threshold prediction model to perform hearing threshold prediction on the SFOAEs signals and the DPOAEs signals under different test intensities, and outputting hearing thresholds corresponding to the frequencies, thereby ensuring the accuracy of diagnosing the hearing loss degree.

The invention relates to the technical field of medical data processing, in particular to a data analysis and processing system based on clinical hearing detection, and aims to solve the problems that dynamic characteristics of auditory nerve response cannot be systematically described, high-dimensional multi-modal feature vectors cannot be constructed, and the data analysis and processing efficiency is low in the prior art. A stable and reliable input basis cannot be provided for intelligent classification, anomaly detection and individualized analysis, and the sensitivity and specificity of hearing state recognition are reduced; the multi-dimensional hearing feature extraction module fuses time domain, frequency domain and non-linear complexity analysis methods, systematically depicts dynamic features of auditory nerve response, constructs a high-dimensional multi-modal feature vector by integrating three-dimensional features, remarkably improves sensitivity and specificity of hearing state recognition, and improves the accuracy of hearing state recognition. The feature representation method provides a stable and reliable input basis for subsequent intelligent classification, anomaly detection and individualized analysis.

The invention discloses a tympanic membrane repair postoperative wound assessment method based on artificial intelligence, and relates to the technical field of medical assessment, and the method comprises the steps: obtaining multi-modal image data of a reference tympanic membrane and a target tympanic membrane through a multi-modal dynamic imaging fusion technology, determining a target biomechanical feature ratio through a biomechanical feature fusion technology, and obtaining a target tympanic membrane target biomechanical feature ratio; the method further comprises the steps of obtaining a physiological parameter set of the reference tympanic membrane and the target tympanic membrane, training an associated parameter prediction model to construct a metabolism-mechanical associated model, and achieving accurate evaluation of tympanic membrane wound healing conditions. The invention also covers related electronic equipment, a computer readable storage medium and a computer program product.

The invention relates to the technical field of ophthalmology informatization, in particular to an intelligent optic nerve function evaluation method and system based on visual evoked potential, and the method comprises the steps: obtaining an electroencephalogram signal, an eye movement track and an auditory reaction signal of a subject; obtaining visual stimulation parameters, wherein the visual stimulation parameters comprise stimulation frequency, stimulation intensity and stimulation duration; on the basis of the electroencephalogram signals, the eye movement tracks and the auditory reaction signals, signal preprocessing operation is executed, and preprocessed multi-modal physiological signals are obtained; extracting features by adopting an improved space-time convolutional neural network based on the preprocessed multi-modal physiological signals to obtain optic nerve function features; according to the optic nerve function features and the visual stimulation parameters, establishing an optic nerve conduction pathway model; evaluating the optic nerve function state based on the optic nerve conduction pathway model to obtain an optic nerve function evaluation result; generating an evaluation report containing the optic nerve function evaluation result; predicting the optic nerve function change trend, and generating a prediction report.

The invention discloses a polarization-based multichannel fNIRS auditory stimulation detection system, which comprises a signal generator module, a light source and detector module, an amplifier module, a voltage-current module, a power supply module, a data acquisition module and an intelligent terminal, the light source and detector module generates two emergent light sources with different wavelengths to a to-be-detected area according to the current signal with the carrier wave, and receives second polarized light and third polarized light reflected by the to-be-detected area; the amplifier module amplifies and converts the second polarized light and the third polarized light into voltage signals; the intelligent terminal is used for calculating the blood oxygen concentration change through the electric signal. According to the invention, the interference of reflected light and noise of surface tissues on blood oxygen concentration signals of a brain area is reduced.

The invention discloses a speech training hearing aid system carrying an AI speech anomaly assessment algorithm, and relates to the AI speech anomaly assessment algorithm, which is characterized in that comprehensive hearing assessment is carried out by collecting a composite signal and formulating an age adaptation rule base, hearing threshold dispersion and signal-to-noise ratio attenuation are obtained, and then a hearing level assessment coefficient is obtained; daily speech evaluation is carried out by collecting speech feature data and formulating a standard speech comparison library, a daily speech anomaly evaluation model is constructed by using a convolutional neural network learning model, and then a daily speech evaluation coefficient is obtained; by collecting multi-dimensional data, professional speech evaluation is carried out, and then a professional speech evaluation coefficient is obtained; and according to the hearing level evaluation coefficient, the daily speech evaluation coefficient and the professional speech evaluation coefficient, analysis is performed to obtain an analysis result, and a comprehensive analysis scheme is formulated, so that the accuracy is greatly improved.

The invention relates to the technical field of wearable equipment and intelligent audio processing, in particular to an intelligent control method for TWS earphone heart rate detection and earphone function adaptive adjustment, which comprises the following steps: S1, synchronously acquiring an original heart rate signal, a contact pressure signal and a motion acceleration signal; s2, performing real-time baseline correction on the original heart rate signal; s3, the motion acceleration signals are fused to calculate motion interference weight factors, and a multi-modal physiological feature set is generated; s4, outputting a multi-dimensional physiological state vector; s5, matching a predefined adjustment strategy library according to the multi-dimensional physiological state vector; and S6, analyzing the combined control instruction set. According to the TWS earphone, physiological signals are accurately collected through the ear canal composite sensor array, high-precision correction of heart rate detection is achieved in combination with dynamic compensation, feature extraction and physiological state classification, noise reduction, audio equalization and ear canal pressure adjustment parameters are intelligently matched based on an adjustment strategy library, and the TWS earphone can adapt to the physiological state of a user.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and / or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Disclosed herein are systems and methods for classifying a tympanic membrane by using a classifier. The classifier is a machine learning algorithm. A method for classifying a tympanic membrane includes steps of: receiving, from an interrogation system, one or more datasets relating to the tympanic membrane; determining a set of parameters from the one or more datasets, wherein at least one parameter of the set of parameters is related to a dynamic property or a static position of the tympanic membrane; and outputting a classification of the tympanic membrane based on a classifier model derived from the set of parameters. The classification comprises one or more of a state, a condition, or a mobility metric of the tympanic membrane.

The invention relates to a method for testing a participant's ability to accurately detect speech under background noise, comprising: (a) audibly presenting a series of 2-bit numbers, 3-bit numbers, 4-bit numbers and / or 5-bit numbers in a random or fixed order to a participant's first ear at a fixed or varying signal-to-noise ratio (SNR) in a quiet or noisy environment, (b) audibly presenting a series of 2-bit numbers, 3-bit numbers, 4-bit numbers and / or 5-bit numbers in a random or fixed order to the second ear of the participant at a fixed or random signal-to-noise ratio (SNR) in a quiet or noisy environment, where the numbers are presented in the native language of the participant or the fluent language of the participant, (c) recording the numbers or sequence of numbers correctly recognized by the participant, and (c) storing the recorded numbers or sequence of numbers at the second ear of the participant. And (d) calculating a score as a function of the percentage of the number or sequence of numbers correctly replicated by the subject, or as a function of the sound intensity difference between the speech and noise required to achieve the correct recognition of 50% sequence of numbers. The invention also relates to a device and a method for measuring the digital recognition capability related to hearing loss, and to a computer-implemented method for evaluating the speech recognition capability of a participant.

The invention is suitable for the technical field of intelligent equipment, and provides a hearing loss risk assessment method and system, intelligent equipment and a storage medium, and the method comprises the steps: a near-ear wearable device collects an audio signal through a configured acoustic sensor, carries out the sound source classification marking of the audio signal based on a current working mode, and carries out the sound source classification marking of the audio signal; and according to a set time interval or a trigger condition, counting a signal intensity mean value of each sound source type audio signal, sending a sound source classification mark and the counted signal intensity mean value to a cloud server corresponding to the user, and indicating the cloud server to carry out hearing loss risk assessment on the user according to the sound source classification mark and the counted signal intensity mean value. The hearing loss risk assessment method and device can effectively improve the convenience, accuracy and universality of hearing loss risk assessment.

The invention discloses a health assessment system and method based on wearable device multi-source data fusion analysis. Wearable equipment health assessment features are obtained through the hearing feature vectors obtained through processing and the heart rate and blood pressure features based on a multi-source data fusion analysis method, and a wearable equipment health assessment model is constructed through the wearable equipment health assessment features and an adopted wearable health assessment result. According to the method, the convolutional neural network model is adjusted based on the auditory features acquired and processed by the wearable device in combination with the physiological parameters, so that data with highly structured features are efficiently processed, and the health assessment features of the wearable device, which are more suitable for fusion analysis of the multi-source data of the wearable device, are obtained by imaging the multi-source data, so that the health assessment accuracy of the wearable device is improved. And the trained model has less parameter quantity and stronger generalization ability, and the health assessment of the wearable device on the wearer is more objective under the multi-source processing of the health assessment features generated by fusion on the model.

The invention belongs to the field of biological medicine research and development, discloses a method and a system for collecting and analyzing auditory electrophysiological data of experimental animals, and is particularly suitable for non-human primates. The method comprises the following steps: delivering a drug to a middle ear cavity through an external auditory canal, and synchronously recording auditory electrophysiological data before and after drug administration; aBR and DPOAE multi-mode detection is adopted, and the frequency range of 4 kHz to 32 kHz is covered; setting anesthesia depth and three-electrode positioning to reduce interference; optimizing the signal-to-noise ratio through noise suppression and dynamic signal superposition; an auditory threshold database is constructed, and a machine learning model is combined to judge an injured part. The system integrates a multi-modal stimulation module, a minimally invasive drug delivery device and a data analysis platform, and supports cross-species data compatibility. The accuracy of otology medicine research is remarkably improved, the surgical operation risk is avoided, and the method is suitable for gene therapy, ototoxicity screening and other scenes.

Provided herein are composition comprising a) a first recombinant adeno-associated viral (rAAV) vector genome comprising a first expression cassette comprising a promoter operably linked to nucleic acid sequence comprising a 5' portion of an otoferlin gene, wherein the first expression cassette is flanked by inverted terminal repeats (ITRs); and b) a second rAAV vector genome comprising a second expression cassette comprising a nucleic acid sequence comprising a 3' portion of an otoferlin gene, wherein the second expression cassette is flanked by ITRs, and wherein the composition can comprise about 4E10-9E13 vg / mL, optionally about about 4.1E10- 4.1E12 total vg or about 8.1E10-8.1E12 total vg and the use of these compositions to treat hearing loss in a subject.

The invention discloses a tinnitus grading method and system based on EEG electroencephalogram analysis and deep learning, and relates to the technical field of tinnitus grading, and the method comprises the steps: in a pre-training stage, converting EEG signals of tinnitus patients with different tinnitus severity degrees into EEG images, inputting the EEG images into an EEG self-encoder, extracting potential features in the EEG signals, and carrying out the recognition of the potential features in the EEG signals; and inputting into a five-classification linear classifier in a downstream fine tuning stage to obtain the probability of each tinnitus severity level, and predicting the tinnitus severity according to the probability. Through training, a deep neural network model containing an EEG auto-encoder used for EEG feature extraction and a linear classifier used for generating the tinnitus severity of a patient is obtained. The probability that the tinnitus severity of the to-be-graded tinnitus patient belongs to each severity level in the five levels is obtained by using the deep neural network model, so that the tinnitus severity is accurately predicted, and medical personnel are helped to formulate a personalized rehabilitation scheme for the patient.

The invention relates to the technical field of crossing of medical instruments and artificial intelligence, and provides a hearing threshold detection method based on multi-mode hearing electrophysiological signal fusion. Comprising stimulation frequency otoacoustic emission and auditory brainstem response induced by sweep frequency stimulation sound, distortion product otoacoustic emission induced by dual-tone stimulation and middle ear sound immittance / reflection signals, and a multi-task deep learning model is constructed to perform fusion analysis on the signals. According to the method, accurate quantitative regression prediction of hearing thresholds with any frequency resolution and automatic classification and discrimination of hearing loss properties are synchronously realized, the problems of single evaluation dimension and insufficient complex hearing loss analysis ability in the prior art are effectively solved, the comprehensiveness, accuracy and clinical operation efficiency of objective hearing evaluation are remarkably improved, and the method is suitable for popularization and application. And an innovative system solution is provided for fine diagnosis of the auditory function.

The invention mainly provides a noise hearing loss assessment device, which comprises a screening module, a collection module, a calculation module, an analysis module and an assessment module, and is characterized in that the assessment device performs single-factor regression analysis on key information of a hearing loss group and a hearing non-loss group and accumulated noise exposure; obtaining difference factors causing hearing loss, and performing multi-factor regression analysis according to the difference factors causing hearing loss to obtain independent risk factors causing hearing loss; according to the independent risk factors causing hearing loss, the column diagram is constructed and used for evaluating hearing loss of a plurality of to-be-detected objects, the evaluation device has good sensitivity and specificity when being used for evaluating hearing loss risks caused by occupational noise exposure, early recognition of high-risk groups can be achieved, effective individual intervention measures can be taken in time, and the evaluation efficiency is improved. The occupational health of workers is protected.

Disclosed are system and methods for automated fitting of a hearing aid, through an automated hearing test based upon which a suitable set of hearing aid parameters is determined.

An earpiece is configured for providing audiometric testing. The earpiece includes an earpiece housing, an intelligent control system disposed within the earpiece housing, at least one transducer operatively connected to the intelligent control, and at least one speaker operatively connected to the intelligent control. The earpiece is configured to perform audiometric testing of a user by reproducing sounds at the at least one transducer and receiving user feedback regarding the sounds to provide audiometric test data.

The embodiment of the invention discloses a tinnitus detection method based on an electroencephalogram level. The tinnitus detection method comprises the following steps: preselecting a preliminary frequency range of a tinnitus patient; extracting a plurality of frequency points in the initial frequency range according to a set frequency interval, and generating a plurality of narrow-band pure tone signals by taking each frequency point as a center; performing tinnitus matching on the patient by using the plurality of narrow-band pure tone signals to obtain initial tinnitus frequency and intensity; according to the hearing loss degree of the patient, a target narrow-band pure tone signal covering the initial tinnitus frequency and notch signals on the two sides of a center frequency point of the target narrow-band pure tone signal are used for conducting a sound giving test on the patient; and determining the final tinnitus frequency according to the electroencephalogram response of the patient under the sound supply test. According to the embodiment, the tinnitus detection accuracy is improved.

The invention discloses a high-frequency hearing loss prediction method. The method comprises the following steps: S1, data preparation; s2, constructing an initial feature set, performing feature selection by adopting an RFECV algorithm, and screening out a plurality of key features most related to prediction of the high-frequency hearing loss from the initial feature set; s3, constructing a high-frequency hearing loss prediction model; s4, training a high-frequency hearing loss prediction model, and training the high-frequency hearing loss prediction model based on an XGBoost model by using the plurality of key features and the secondary training set which are screened out in the step S2 and are most related to prediction of the high-frequency hearing loss; and S5, evaluating the high-frequency hearing loss prediction model trained in the step S4 by using a secondary test set. According to the prediction method, the subjectivity of feature selection and the limitation of model prediction performance of a traditional method are effectively overcome, the automation, precision and reliability of high-frequency hearing loss prediction can be remarkably improved, and technical support is provided for early-stage precise screening and personalized intervention of high-frequency hearing loss.

The invention relates to an otoacoustic emission signal detection system and method, and the system comprises a collection and transmission module which is used for transmitting a stimulation signal and collecting an auditory meatus signal. And the fine structure detection mechanism comprises a DPOAEs fine structure detection module and an SFOAEs fine structure detection module which are respectively used for extracting fine structure information of otoacoustic emission from the auditory meatus signal. The I / O function detection mechanism comprises a DPOAEs I / O function detection module and an SFOAEs I / O function detection module which are respectively used for extracting I / O function information of otoacoustic emission from auditory meatus signals, so that the comprehensive research type otoacoustic emission instrument with a test parameter customizable SFOAEs and DPOAEs multi-test parameter rapid detection function is realized. Comprising a frequency dimension fine structure detection function and an intensity dimension I / O function detection function, and can be suitable for different demand scenes related to otoacoustic emission research and application.

The invention relates to the technical field of health data processing, in particular to an intelligent health data processing method and device and a storage medium, and the method comprises the steps: collecting the pure tone hearing threshold of a user under a standard frequency table, constructing a pure tone audiometry map of the user, extracting the hearing threshold features of the user, constructing the hearing sensitive state index of the user, and obtaining the hearing sensitive state index of the user; then determining a basic hearing state of the user; collecting health data of the user in real time; calibrating an otoacoustic emission amplitude and an ABR wave V amplitude of the user; constructing a peripheral-central function coupling index of the user; and constructing an occult hearing loss model based on the peripheral-central function coupling index and the hearing processing disorder index of the user so as to output the occult hearing loss state of the user, and performing hearing health early warning on the user by integrating the occult hearing loss state of the user and the basic hearing state. According to the invention, the efficiency of health data processing is effectively improved.

The invention particularly relates to a noise adjustment method of an audiometer and a noise generator thereof, and the method comprises the following steps: constructing a noise database, the types of the noise comprising analog noise, random noise and mixed noise; the frequency range and amplitude data during pure tone output are obtained, a preselection scheme of masking noise matched with the pure tone is generated from the corresponding type according to the input instruction, and the preselection scheme comprises the duration time, the expected frequency range and the expected amplitude of the selected masking noise; superposing and outputting the selected masking noise and the pure tone, obtaining the mixed tone segment signal again at the vibrating diaphragm, and performing time-frequency conversion to generate a waveform frequency spectrum; based on the waveform frequency spectrum, removing the waveform frequency spectrum calculated according to the frequency range and the amplitude data of the pure tone, then calculating to obtain the actual frequency range and the actual amplitude of the masking noise, and adjusting the frequency range and the amplitude output by the masking noise according to the actual frequency range and the actual amplitude. Therefore, the actual frequency range and the actual amplitude are infinitely close to the expected frequency range and the expected amplitude respectively.