15 results about "Auditory brainstem response" patented technology

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 relates to the technical field of electroencephalogram signal monitoring and processing, in particular to a user category determination method based on an auditory brainstem reaction normal form and source localization, and the method comprises the following steps: obtaining auditory brainstem reaction data and electroencephalogram data when a target user is stimulated by external sound; based on a feature extraction module, feature extraction is carried out on source estimation data corresponding to the auditory brainstem response data and the electroencephalogram data; performing feature fusion on the basis of the features extracted from the auditory brainstem reaction data and the features extracted from the source estimation data, and determining fusion features; and determining the category of the target user based on the fusion features and a user category prediction model. According to the scheme for carrying out early diagnosis and screening on the autism infants based on the auditory brainstem reaction data and the electroencephalogram data, early autism (ASD) screening of the infants can be finally achieved under the support of the electroencephalogram equipment and auditory brainstem reaction testing.

A method for early diagnosis of autism spectrum disorder (ASD) including determining an ASD risk index, determining an ASD brain index that includes administering at least one auditory brainstem response (ABR) test to an ear canal of a patient, producing a plurality of biomarkers and identifying at least one biomarker of the plurality of biomarkers as a potential ASD biomarker, and determining an ASD likelihood responsive to each of the ASD risk index and ASD brain index.

The invention discloses an auditory brainstem response automatic classification method and device based on a time-frequency cross attention network, equipment and a storage medium, and relates to the technical field of hearing monitoring. Double-branch parallel processing is carried out on ABR signals; the frequency domain branch obtains time-frequency spectrogram features through short-time Fourier transform; a characteristic linear modulation mechanism is introduced into each of the two branches, and stimulation intensity is used as conditional information to carry out adaptive scaling and offset on intermediate characteristics; complementary fusion of the time-frequency characteristics is realized through a bidirectional cross attention mechanism, so that the time-domain characteristics can query the frequency-domain information, and the frequency-domain characteristics can query the time-domain information; and performing binary classification decision by combining global average pooling, stimulation intensity late fusion and a multi-layer perceptron. According to the method, multi-modal information and physiological priori can be fully utilized in automatic classification without manual feature engineering, and the accuracy, objectivity and efficiency of hearing screening and diagnosis are remarkably improved.

The present invention discloses a neural response data monitoring system and method based on electroencephalogram (EEG) signals. An acquisition device generates an audio signal and controls an audio output module based on the audio signal to output sound stimulation of a set stimulation duration to an object at set stimulation intervals, thereby forming multiple groups of stimulations. A sensor module synchronously acquires original EEG signals of the object of the set collection duration corresponding to each group of sound stimulations and uploads them to a host computer, wherein the set stimulation duration is less than the set collection duration and less than the set stimulation interval. The host computer receives the multiple groups of original EEG signals, performs average signal preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals, performs average processing on the multiple groups of preprocessed EEG signals to obtain auditory brainstem response waveform data including seven positive waves, namely, wave I to wave VII; based on the auditory brainstem response waveform data, peak delay data of wave I to wave VII is acquired; and based on the peak delay data of wave I to wave VII, the neural response data of the object is acquired.

Disclosed are methods including receiving, at a computing device, for at least two intensities of a plurality of intensities emitted during an auditory brainstem response (ABR) test, an individual plurality of ABR waveform samples; generating, for the at least two intensities of the plurality of intensities, an individual cross-correlation distribution including a plurality of cross-correlation samples generated by iterative bootstrap sample signal processing of the individual plurality of ABR waveform samples; and calculating, for the at least two intensities of the plurality of intensities, based on the plurality of cross-correlation samples, an individual mean of the individual cross-correlation distribution; and identifying an ABR threshold intensity of the plurality of intensities by comparing the individual mean for the at least two intensities of the plurality of intensities to a threshold criterion.

The application discloses an electrode sheet for auditory evoked potential detection and a use method thereof, which comprises a skin-friendly layer, a conductive layer and a bearing layer arranged in sequence in the thickness direction of the electrode sheet, wherein the skin-friendly layer comprises a leakage-stopping rubber ring located at an outer ring, a physiotherapy rubber block arranged at an inner ring of the leakage-stopping rubber ring and a non-woven fabric, the non-woven fabric is located at a side away from the conductive layer and covers the physiotherapy rubber block; the physiotherapy rubber block comprises a temperature-sensitive solvent and solute particles arranged in the temperature-sensitive solvent, the temperature-sensitive solvent can change with temperature and transform between solid and liquid states; through the coating cooperation of the temperature-sensitive solvent and the solute particles in the skin-friendly layer, the electrode sheet can warm, massage and medicate the used object before auditory evoked detection, thereby providing effective calming effect, and the solute particles provide a uniform and stable current channel, thereby ensuring the stability of evoked electrical signals and the safety of external input current.

The present application relates to a system for diagnosing cochlear hydrops of a person or an animal. The system comprises an acoustic stimulus generating unit comprising a stimulus generator and an output transducer, where the acoustic stimulus generating unit is configured to provide an audio stimulus by the stimulus generator to at least one of the ears of the person or animal via the output transducer, wherein the acoustic stimulus generating unit is configured to provide a plurality of audio stimuli comprising at least a first click audio stimulus and a first chirp audio stimulus or at least a first and a second chirp audio stimuli, a recording unit configured to record one or more auditory electrophysiological response of the person or animal in response to the one or more audio stimulus being provided by the acoustic stimulus generating unit to at least one of the ears of the person or animal, and a diagnostic unit configured to process the recorded ABR.

The invention discloses an I-wave, III-wave and V-wave detection method and device for auditory brainstem reaction, an FPGA, equipment and a medium. The method comprises the following steps: acquiring an auditory brainstem response (ABR) signal, filtering the ABR signal to obtain a filtered signal, finding out a wave I, a wave III and a wave V from the filtered signal, checking whether wave intervals and latency periods of the wave I, the wave III and the wave V meet a preset reliability condition or not, and if so, determining that the reliability of the wave I, the wave III and the wave V meets the preset reliability condition. Taking the found wave I, the wave III and the wave V as final results, and outputting respective latency periods of the wave I, the wave III and the wave V; and if not, inputting the original signal into a pre-trained artificial neural network for processing, finding out a wave I, a wave III and a wave V from a processing result output by the artificial neural network, and outputting the incubation periods of the wave I, the wave III and the wave V found from the processing result as a final result. Therefore, automatic detection of the I wave, the III wave and the V wave is realized, the consumed time is short, the manual labor amount is reduced, and the working efficiency is improved.

The present application discloses a test system and method for an auditory brainstem response measuring instrument, relating to the technical field of auditory brainstem response measuring instrument testing, and solving the problem that the performance parameters of the physiological electrical response measurement part of the existing auditory brainstem response measuring instrument cannot be tested. The system includes: a signal generation module, an audio amplification module, an acoustic coupler, an attenuation circuit module, a main control module, a display module, and a dual-channel signal acquisition module. The acoustic signal emitted by the air conduction earphone of the auditory brainstem response measuring instrument is converted into an electrical signal for triggering the signal generation module to start, so that a sine signal can be automatically output according to the acoustic signal, and after attenuation, it is input to the electrode input interface of the auditory brainstem response measuring instrument. By analyzing the waveform and amplitude collected by the dual-channel signal acquisition module, the amplitude measurement error and latency measurement error of the auditory brainstem response measuring instrument can be obtained.

An automatic test device and method for auditory brainstem response (ABR) collects an ABR dataset at a plurality of sound loudness levels, increases the times of level averaging by iteration based on an adaptive average method, and improves a signal-to-noise ratio until ABR signal detection conditions are met. Signal detection includes determining that the time lag between average curves obtained from the ABR dataset is within a specified range. Iteration is terminated when the ABR signal is detected or a maximum number of iterations is reached. A minimum loudness level required to detect the ABR signal is used as a hearing threshold. An accurate loudness level corresponding to the hearing threshold is obtained by function fitting on the number of iterations used at each loudness level and interpolation. The threshold detection can effectively reduce the number of times that an ABR recording needs to be acquired.

This invention discloses a method for constructing an animal model of sensorineural hearing loss induced by phenylacetylglutamine and its application. The method includes: selecting 6-8 week old male C57BL / 6J mice, intraperitoneally injecting phenylacetylglutamine solution at a dose of 50 mg / kg body weight / day, once daily, for at least 4 months to obtain the sensorineural hearing loss animal model. This model is characterized by a significantly elevated auditory brainstem response threshold, loss of cochlear outer hair cells, and increased oxidative stress levels. This invention establishes for the first time an animal model of sensorineural hearing loss induced by the circulating metabolite phenylacetylglutamine, filling the technical gap in existing models that cannot simulate hearing loss mediated by metabolic abnormalities. The model construction method is simple to operate, has a high success rate, and good reproducibility, and can be used for research on the pathogenesis of sensorineural hearing loss and screening for preventive and therapeutic drugs.

A method and system for de-noising evoked potential waveforms is provided. In a first aspect, a method includes receiving an auditory brainstem response (ABR) waveform set. The ABR waveform set includes evoked potential waveforms for each ear, stimulation level, and frequency. The method includes generating a set of de-noised waveforms by applying a dual tree complex wavelet transform (DTCWT) to the set of ABR waveforms and minimizing an error between each de-noised waveform and its corresponding ABR waveform, while: limiting a total amplitude of complex wavelet coefficients to an amplitude threshold; limiting the total magnitude of the difference between the complex wavelet coefficients at adjacent stimulation levels to a level threshold; and limiting the total magnitude of the difference between the complex wavelet coefficients at adjacent frequencies to a frequency threshold.

The invention relates to the technical field of hearing testing, in particular to a hearing evaluation method and device based on fixed stimulation intensity, a terminal and a medium, and the method comprises the steps: obtaining auditory brainstem response waves collected under preset fixed stimulation intensity and demographic characteristics corresponding to the auditory brainstem response waves; extracting waveform features of the auditory brainstem reaction waves, converting the waveform features into waveform feature vectors, and converting the demographic features into demographic feature vectors; and inputting the waveform feature vector and the demographic feature vector into a trained auditory evaluation model, and outputting an auditory evaluation result which comprises any one or more of a hearing loss level classification result, a hearing loss property classification result and a hearing graph classification result. According to the method and the device, the test times required for evaluating the auditory level are reduced, the auditory evaluation time is shortened, and the recognition result of one or more dimensions is output based on the auditory evaluation model, so that the auditory evaluation accuracy is improved.

The application discloses an auditory brainstem response automatic classification method and device based on a time-frequency cross attention network, equipment and a storage medium, relates to the technical field of hearing monitoring, and through double-branch parallel processing of ABR signals: a one-dimensional convolution is adopted in a time domain branch to extract local waveform form features, and a short-time Fourier transform is adopted in a frequency domain branch to obtain a time-frequency spectrum feature; a feature linear modulation mechanism is introduced into both branches, a stimulus intensity is taken as condition information to adaptively scale and offset intermediate features; complementary fusion of time-frequency features is realized through a bidirectional cross attention mechanism, time domain features can query frequency domain information, and frequency domain features can query time domain information; global average pooling, stimulus intensity late fusion and a multilayer perception machine are combined to make a binary classification decision. In the automatic classification, multi-modal information and physiological priori can be fully utilized without manual feature engineering, and the accuracy, objectivity and efficiency of hearing screening and diagnosis are significantly improved.