CN-120980436-B - Tinnitus monitoring method and system based on intelligent earphone

CN120980436BCN 120980436 BCN120980436 BCN 120980436BCN-120980436-B

Abstract

The invention belongs to the technical field of electronic equipment, and particularly relates to a tinnitus monitoring method and system based on an intelligent earphone. Environmental noise can be effectively restrained through a high-precision frequency spectrum analysis chip and an adaptive filtering algorithm, and accuracy of tinnitus frequency and loudness data is ensured. And a depth learning model is utilized to generate a personalized control signal, and a sound signal is customized according to the tinnitus characteristics of each patient, so that the effectiveness and pertinence of analysis are remarkably improved. In addition, the data encryption and remote transmission functions ensure the safety and privacy protection of patient data, and simultaneously, a doctor can conveniently monitor and adjust a personalized analysis scheme remotely. The tinnitus self-monitoring and personalized analysis system effectively solves the problem that the convenient and accurate tinnitus self-monitoring and personalized analysis are difficult to realize in the prior art, and brings more scientific and efficient health management means for tinnitus patients.

Inventors

ZHAO XIAOYANG
ZHAO MINFANG

Assignees

上海博听科技有限公司

Dates

Publication Date: 20260512
Application Date: 20250916

Claims (8)

1. A tinnitus monitoring method based on intelligent earphone is characterized by comprising the following steps: pressing an operation key to start a tinnitus detection module, acquiring ear sound signals through a frequency spectrum analysis chip, converting the acquired time domain signals into frequency domain signals through fast Fourier transform by the frequency spectrum analysis chip, and identifying a tinnitus frequency range; The frequency spectrum analysis chip acquires tinnitus loudness data through the sound pressure sensor, and transmits tinnitus frequency range and loudness data to the main control module; The main control module stores the tinnitus frequency range and the loudness data to the data storage module through an AES encryption standard, and sends the tinnitus frequency range and the loudness data to the rehabilitation sound synthesizing module; Generating a personalized control signal by the rehabilitation sound synthesizing module based on the tinnitus frequency range and the loudness data, transmitting the personalized control signal to a programmable waveform generator, generating rehabilitation sound, and outputting the rehabilitation sound by the programmable waveform generator through a sound generating unit; The method comprises the steps of generating a personalized control signal, matching a tinnitus frequency range with a rehabilitation sound frequency through a preset mapping rule, adjusting the amplitude of the rehabilitation sound according to tinnitus loudness data to keep dynamic balance between the intensity of the rehabilitation sound and the tinnitus loudness, optimizing control signal parameters through a history rehabilitation record, and generating a personalized control signal; the method comprises the steps of synthesizing a composite signal of white noise and reverse phase masking sound through a programmable waveform generator, adjusting the amplitude proportion of the white noise and the masking sound according to a personalized control signal, and performing low-pass filtering on the synthesized signal through a digital signal processing module to remove high-frequency noise components.
2. The intelligent earphone-based tinnitus monitoring method according to claim 1, wherein the spectrum analysis chip converts the acquired time domain signal into a frequency domain signal through fast fourier transform, comprising: carrying out framing treatment on the collected ear sound signals, wherein each frame comprises a fixed number of sampling points; applying a hamming window function to each frame of signal to reduce spectral leakage; the frequency domain component of each frame of signal is calculated through fast Fourier transformation, and the frequency range with energy higher than background noise is extracted.
3. The intelligent earphone-based tinnitus monitoring method according to claim 1, wherein the spectrum analysis chip obtains tinnitus loudness data through a sound pressure sensor, comprising: Short-time energy calculation is carried out on the ear sound signals, and an instantaneous sound pressure value is obtained; smoothing the instantaneous sound pressure value through a moving average filter to eliminate transient noise interference; And comparing the smoothed sound pressure value with a reference sound pressure value, and calculating the relative variation amplitude of the tinnitus loudness.
4. The intelligent earphone-based tinnitus monitoring method of claim 1, wherein the main control module stores tinnitus frequency range and loudness data to a data storage module via AES encryption standard, comprising: dividing tinnitus frequency range and loudness data into fixed-size data blocks; applying an AES encryption algorithm to each data block to generate corresponding ciphertext data; and writing the ciphertext data into a designated storage area of the data storage module.
5. The intelligent earphone-based tinnitus monitoring method of claim 1, further comprising the steps of: The main control module transmits tinnitus frequency range and loudness data to a mobile phone end application program through a Bluetooth module; And the mobile phone end application program displays the change trend of the tinnitus frequency along with time in a line graph form and uploads the data to the cloud server through the Wi-Fi module.
6. The intelligent earphone-based tinnitus monitoring method of claim 5, wherein the main control module transmits tinnitus frequency range and loudness data to a mobile phone application program via a bluetooth module, comprising: packaging the tinnitus frequency range and the loudness data into Bluetooth data packets; transmitting the data packet to mobile phone terminal equipment through a Bluetooth protocol stack; and the mobile phone terminal equipment receives the data packet, analyzes the data packet and stores the data packet in a local database.
7. The intelligent earphone-based tinnitus monitoring method of claim 6, wherein uploading the data to the cloud server via the Wi-Fi module comprises: the tinnitus data stored locally are sent to a cloud server through an HTTP protocol; the cloud server performs integrity check on the received data; a long-term trend report of the tinnitus condition of the patient is generated by a data visualization tool.
8. A smart headset-based tinnitus monitoring system for implementing the method of any one of claims 1-6, comprising: The operation key is used for starting the tinnitus detection module; the frequency spectrum analysis chip is connected with the operation keys and is used for collecting ear sound signals and carrying out frequency spectrum analysis; the sound pressure sensor is connected with the frequency spectrum analysis chip and used for acquiring tinnitus loudness data; The main control module is connected with the frequency spectrum analysis chip and the sound pressure sensor and is used for receiving and processing tinnitus data; The data storage module is connected with the main control module and used for storing the encrypted tinnitus data; the rehabilitation sound synthesis module is connected with the main control module and used for generating personalized control signals; the programmable waveform generator is connected with the rehabilitation sound synthesizing module and used for generating rehabilitation sound; the sounding unit is connected with the programmable waveform generator and is used for outputting rehabilitation sound; the Bluetooth module is connected with the main control module and used for transmitting tinnitus data to the mobile phone end application program; the Wi-Fi module is connected with the main control module and used for uploading data to the cloud server; The method comprises the steps of generating a personalized control signal, matching a tinnitus frequency range with a rehabilitation sound frequency through a preset mapping rule, adjusting the amplitude of the rehabilitation sound according to tinnitus loudness data to keep dynamic balance between the intensity of the rehabilitation sound and the tinnitus loudness, optimizing control signal parameters through a history rehabilitation record, and generating a personalized control signal; the method comprises the steps of synthesizing a composite signal of white noise and reverse phase masking sound through a programmable waveform generator, adjusting the amplitude proportion of the white noise and the masking sound according to a personalized control signal, and performing low-pass filtering on the synthesized signal through a digital signal processing module to remove high-frequency noise components.

Description

Tinnitus monitoring method and system based on intelligent earphone Technical Field The invention belongs to the technical field of electronic equipment, and particularly relates to a tinnitus monitoring method and system based on an intelligent earphone. Background Tinnitus, a common auditory phenomenon, has a significant negative impact on many people's daily lives. Existing tinnitus monitoring methods rely primarily on professional medical equipment and professional operations, such as pure-tone audiometers or otoacoustic emission testers, and the like. These devices are often bulky, complex to operate and need to be performed in a hospital or clinic, limiting the possibilities of daily self-monitoring of the patient. In addition, the traditional method can only provide basic information of tinnitus frequency and loudness, lacks a personalized analysis scheme, and cannot be dynamically adjusted according to the specific situation of each patient. In particular, one of the major problems in the prior art is the difficulty in achieving convenient and accurate tinnitus self-monitoring and personalized analysis. Because the traditional tinnitus monitoring device is not portable and easy to use, patients can hardly monitor the tinnitus state continuously at home, which not only affects the early discovery and intervention effects, but also limits the feasibility of long-term management. Meanwhile, most of the traditional analysis methods are general strategies, individual differences cannot be fully considered, and therefore analysis results are not accurate enough. Disclosure of Invention The invention aims to provide a tinnitus monitoring method and system based on an intelligent earphone, which are used for solving the problems in the background technology. In order to achieve the purpose, the technical scheme adopted by the invention is that the tinnitus monitoring method based on the intelligent earphone comprises the following steps: pressing an operation key to start a tinnitus detection module, acquiring ear sound signals through a frequency spectrum analysis chip, converting the acquired time domain signals into frequency domain signals through fast Fourier transform by the frequency spectrum analysis chip, and identifying a tinnitus frequency range; The frequency spectrum analysis chip acquires tinnitus loudness data through the sound pressure sensor, and transmits tinnitus frequency range and loudness data to the main control module; The main control module stores the tinnitus frequency range and the loudness data to the data storage module through an AES encryption standard, and sends the tinnitus frequency range and the loudness data to the rehabilitation sound synthesizing module; And the rehabilitation sound synthesis module generates a personalized control signal based on the tinnitus frequency range and the loudness data, transmits the personalized control signal to the programmable waveform generator, generates rehabilitation sound, and outputs the rehabilitation sound through the sounding unit. Preferably, the spectrum analysis chip converts the acquired time domain signal into a frequency domain signal through fast fourier transform, including: carrying out framing treatment on the collected ear sound signals, wherein each frame comprises a fixed number of sampling points; applying a hamming window function to each frame of signal to reduce spectral leakage; the frequency domain component of each frame of signal is calculated through fast Fourier transformation, and the frequency range with energy higher than background noise is extracted. Preferably, the spectrum analysis chip obtains tinnitus loudness data through a sound pressure sensor, including: Short-time energy calculation is carried out on the ear sound signals, and an instantaneous sound pressure value is obtained; smoothing the instantaneous sound pressure value through a moving average filter to eliminate transient noise interference; And comparing the smoothed sound pressure value with a reference sound pressure value, and calculating the relative variation amplitude of the tinnitus loudness. Preferably, the main control module stores the tinnitus frequency range and the loudness data to the data storage module through the AES encryption standard, including: dividing tinnitus frequency range and loudness data into fixed-size data blocks; applying an AES encryption algorithm to each data block to generate corresponding ciphertext data; and writing the ciphertext data into a designated storage area of the data storage module. Preferably, the generating the personalized control signal includes: matching the tinnitus frequency range with the rehabilitation sound frequency through a preset mapping rule; adjusting the amplitude of the rehabilitation sound according to the tinnitus loudness data to keep dynamic balance between the intensity of the rehabilitation sound and the tinnitus loudness; and optimizing control signal parameter