8 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.

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 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.

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 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.