Intelligent electronic stethoscope system based on wireless transmission and data processing method thereof

Abstract

The invention discloses an intelligent electronic stethoscope system based on wireless transmission and a data processing method thereof, and belongs to the technical field of electronic stethoscopes, the intelligent electronic stethoscope system comprises a heart sound acquisition module, a signal conditioning module, a power supply module, a wireless transmission module, an upper computer module and a database module; the microprocessor module serves as a core control unit, and the system can collect heart sound signals of a patient in real time and conduct amplification, filtering and spectrum analysis processing on the heart sound signals. The processed heart sound data is transmitted to a mobile device or a computer in real time through a wireless transmission module, so that doctors and users can check heart sound waveforms and related data conveniently; the system also supports the uploading of the data to a cloud database for remote diagnosis and query; the method not only can meet the requirements of modern medical treatment for high efficiency and convenience, but also can play an important role in improving the diagnosis precision and the remote diagnosis capability and reducing the infection risk.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic stethoscopes, and in particular to an intelligent electronic stethoscope system based on Bluetooth transmission and a data processing method thereof. Background Art

[0002] The traditional stethoscope is an important tool for doctors to diagnose cardiopulmonary diseases, but it has many limitations. The traditional stethoscope mainly relies on the doctor's hearing and experience, and makes a diagnosis by directly contacting the patient's body and transmitting sound through the air. However, this method has the disadvantages of limited information, susceptibility to environmental interference, and inability to transmit data remotely. Especially in a noisy environment, this method can easily lead to inaccurate diagnosis results. In addition, the traditional stethoscope also requires the doctor to operate face-to-face with the patient, which may cause great inconvenience and risks to elderly patients with limited mobility or during epidemics of infectious diseases.

[0003] With the development of medical technology, especially the widespread application of technologies such as artificial intelligence and the Internet of Things, the research on intelligent electronic stethoscopes has become an important direction. Intelligent electronic stethoscopes can collect patients' heart sound signals in real time, filter, amplify and spectrally analyze the data through signal processing algorithms, and use wireless communication technology to transmit the processed data to the doctor's equipment. This innovative design can not only reduce the interference of environmental noise and improve the accuracy of diagnosis, but also realize remote monitoring and diagnosis, greatly improving the utilization efficiency of medical resources.

[0004] In the current context of growing medical needs, especially with an aging population and frequent public health emergencies, smart electronic stethoscopes can provide convenient home diagnosis services for the elderly and patients with limited mobility, reducing the frequency of their visits to the hospital. In addition, doctors can obtain patients' heart sound data in real time through wireless transmission, conduct remote diagnosis and treatment recommendations, further improving the coverage and efficiency of medical services.

[0005] Therefore, the development of an intelligent electronic stethoscope system based on wireless transmission and its data processing method can not only meet the high efficiency and convenience requirements of modern medical care, but also play an important role in improving diagnostic accuracy, remote diagnostic capabilities and reducing infection risks. This system can not only provide doctors with more accurate diagnostic tools, but also bring patients a more convenient medical experience, and has broad application prospects and important social value. Summary of the invention

[0006] The technical problem solved by the present invention is to address the shortcomings of the background technology and provide an intelligent electronic stethoscope system based on Bluetooth transmission and a data processing method thereof, which effectively solves the problems of traditional stethoscopes such as small amount of information, susceptibility to environmental interference and inability to transmit data remotely, while providing a more accurate and convenient medical diagnosis method.

[0007] The present invention adopts the following technical solutions to solve the above technical problems:

[0008] The intelligent electronic stethoscope system based on Bluetooth transmission includes a heart sound acquisition module, a signal conditioning module, a single-chip computer module, a power module, a wireless transmission module, a host computer module and a database module;

[0009] Among them, the heart sound collection module is responsible for collecting the patient's heart sound signals;

[0010] The signal conditioning module is used to amplify the collected signal to obtain an analog signal, and then input it into the microprocessor module for data processing through AD conversion;

[0011] The microprocessor module is used to filter and analyze the sound signal using digital signal processing technology. It uses an embedded microcontroller system encoded with digital filtering functions and uses fast Fourier transform (FFT) processing to filter out sounds of other frequencies.

[0012] A power module, used for providing the electric energy required by the intelligent electronic stethoscope system;

[0013] A wireless transmission module is used to transmit the processed heart sound data to the host computer module in real time;

[0014] The host computer module is used to send the collected heart sound waveform to a mobile device or computer, so that doctors and users can view the heart sound waveform and related data;

[0015] The database module is used to build a MySQL database, upload data to the cloud for remote query and diagnosis, and analyze the patient's condition based on the collected data.

[0016] A data processing method for an intelligent electronic stethoscope system based on wireless transmission specifically comprises the following steps:

[0017] Step 1, ADC sampling: When performing ADC acquisition of the microprocessor module, connect a 1K to 10K pull-down resistor to the ground at the AO end of the signal acquisition and amplification module, perform ADC operation on the AO end of this module to collect signals, connect it to the serial port PA6 of the microprocessor module, connect the DO end to the PA4 pin, connect the VCC to the 3.3V of the microprocessor module, and connect the GND to the GND pin of the microprocessor module to realize signal acquisition input;

[0018] Step 2, digital processing of the signal: the collected sound is filtered using an FFT filtering algorithm, which specifically includes the following steps;

[0019] Step 2.1, collecting time domain signals: using an ADC analog-to-digital converter or other sensors to collect time domain signals;

[0020] Step 2.2, perform FFT: perform FFT on the collected time domain signal to convert it into a frequency domain signal;

[0021] Step 2.3, filter design: design the filter according to the application requirements; used to retain or eliminate specific frequency components in the frequency domain; applying the filter is to multiply the filter transfer function with the FFT result in the frequency domain to eliminate or attenuate unwanted frequency components;

[0022] Step 2.4, perform inverse FFT, i.e. IFFT: convert the filtered frequency domain signal back to the time domain signal;

[0023] Step 2.5, output or further process the time domain signal: output the IFFT result to a DAC digital-to-analog converter or other device, or perform further processing;

[0024] Step 3, intelligent cloud analysis: specifically includes the following steps;

[0025] Step 3.1, it is necessary to define and extract the key features of the heart sound signal, including frequency, amplitude, and duration;

[0026] Step 3.2: Use Fourier transform, wavelet transform and other signal processing methods to extract more complex features, use known heart sound data sets to train and verify the algorithm, and use the training model to identify the association between these heart sound features and different diseases, thereby assisting doctors in diagnosis and evaluating the algorithm's performance indicators such as accuracy, sensitivity and specificity;

[0027] Step 3.3, build a cloud server, implement the voice auscultation algorithm on the microprocessor module, and then automatically upload the collected data to the server in the cloud to eliminate noise and analyze it; set up corresponding algorithms and models on the cloud server, use signal processing technology, machine learning algorithms and other methods to process and interpret the auscultation data, compare the key features of the extracted heart sound signals with the preset thresholds or reference data, and judge the patient's heart condition based on the algorithm analysis results. After the analysis is completed, the server will send the diagnosis results and treatment recommendations back to the user's mobile phone.

[0028] As a further preferred embodiment of the data processing method of the intelligent electronic stethoscope system based on wireless transmission of the present invention, the frequency Fn corresponding to the nth point in the FFT result, i.e., n counting from 1, is (n-1)*Fs / N, where Fs is the sampling frequency; FFT evenly divides the frequency axis into N intervals, and the width of each interval is Fs / N;

[0029] The implementation of FFT includes the following three functions:

[0030] cr4_fft_64_stm32: implements 64-point FFT;

[0031] cr4_fft_256_stm32: implements 256-point FFT;

[0032] cr4_fft_1024_stm32: implements 1024-point FFT;

[0033] Determine the number of sampling points, the input and output arrays of FFT, and the amplitude of each harmonic. The output array of FFT and the array of the amplitude of each harmonic only have half of the number of sampling points because the data calculated by FFT is symmetrical, so only half of the data is taken; use the for loop to store the data collected by ADC into the input array of FFT operation.

[0034] As a further preferred solution of the data processing method of the intelligent electronic stethoscope system based on wireless transmission of the present invention, the program will read the data transmitted by DMA, that is, the result of ADC conversion, and save it to the corresponding memory address; then further perform FFT, call the corresponding function for filtering, and sample the filter function, that is, rectangular filter filtering, to obtain the required data; its filtering principle is to fit the signal into a rectangular window with the same width, and the signal mean in the window replaces the original signal value, so as to achieve the effect of smoothing processing, and enable all signals below the cutoff frequency to pass losslessly, while all signals above the cutoff frequency should be infinitely attenuated; immediately after the FFT operation, the amplitude calculation is performed, and the amplitude calculation function is as follows:

[0035] arm_cfft_radix4_f32(&scfft,fft_inputbuf); / / FFT transformation;

[0036] arm_cmplx_mag_f32(fft_inputbuf,fft_outputbuf,FFT_LENGTH); / / Calculate the amplitude.

[0037] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0038] The intelligent electronic stethoscope system based on wireless transmission and its data processing method of the present invention include a heart sound acquisition module, a signal conditioning module, a power module, a wireless transmission module, a host computer module and a database module; with the microprocessor module as the core control unit, the system can acquire the patient's heart sound signals in real time, and amplify, filter and perform spectrum analysis on them; the processed heart sound data is transmitted to a mobile device or computer in real time through the wireless transmission module, so that doctors and users can view heart sound waveforms and related data; the system also supports uploading data to a cloud database for remote diagnosis and query, aiming to solve the problems of traditional stethoscopes with small amount of information, susceptibility to environmental interference, and inability to transmit data remotely, while providing more accurate and convenient medical diagnosis means. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following briefly introduces the drawings required in the embodiments or the prior art description. Obviously, the following drawings are only part of the embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative work.

[0040] Figure 1 is a block diagram of the stethoscope system of the present invention;

[0041] Figure 2 It is a signal digital processing flow chart of the present invention;

[0042] Figure 3 It is a flow chart of the wireless communication algorithm of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings:

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The present invention is described in detail below based on the drawings and preferred embodiments, and the purpose and effect of the present invention will become clearer. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0046] This system is mainly composed of two parts: hardware design and software design. It mainly includes the following parts: heart sound acquisition module, signal conditioning module, power module, wireless transmission module and host computer module and database establishment. The heart sound acquisition module is responsible for collecting the patient's heart sound signal; the microprocessor module is used as the core of the main control system, and the heart sound signal is collected through the pickup, amplified and filtered by the conditioning circuit and then transmitted to the microprocessor module. The sound signal is filtered and spectrum analyzed using digital signal processing technology, and the processed heart sound data is transmitted to the mobile device or computer in real time through the wireless transmission module, and the heart sound waveform and data are displayed, and the power module is powered. The host computer receives the data processed by the microprocessor module, which can be used by the user to view its heart sound waveform in real time and obtain the corresponding information of the heart.

[0047] Since the signal of heart sound is very small, only tens of millivolts, it is inconvenient for signal observation and further processing. Therefore, the collected signal needs to be amplified first to obtain an analog signal, and then input into the microprocessor module for data processing through AD conversion. Therefore, an embedded microprocessor system with digital filtering function is used, and fast Fourier transform FFT processing is used to filter out sounds of other frequencies. ADC acquisition is performed through the microprocessor module, and the transmission of the wireless transmission module is realized through programming. The collected heart sound waveform is sent to the corresponding APP of the mobile phone for corresponding analysis, and a mysql database is built. The data can be uploaded to the cloud for remote query and diagnosis, and the patient's condition is analyzed based on the collected data.

[0048] The stethoscope system implementation block diagram is as follows Figure 1 As shown: This stethoscope adopts a handheld appearance design. In the appearance design of the stethoscope, the signal processing module is usually located inside the main body of the stethoscope. The main body is compact and streamlined, and provides convenient operation buttons and interfaces so that users can easily access and operate the signal processing module;

[0049] Heart sound acquisition module: The principle of selecting sensors in the design of this pickup module is: small size, simple structure, good electroacoustic performance, high sensitivity, strong anti-interference ability, and high cost performance. After comprehensive comparison, the electret microphone was finally selected as the pickup, which can convert weak sound vibrations into tiny pulsating electrical signals.

[0050] The signal processing flow is as follows Figure 2 As shown. Digital processing of the signal:

[0051] The collected sound needs to be further filtered. Next, further digital filtering algorithm will be used for processing, and FFT filtering algorithm will be used.

[0052] FFT is an efficient algorithm for calculating the discrete Fourier transform (DFT) and its inverse transform. FFT is widely used in digital signal processing, communications, image processing and other fields because it can significantly reduce the time and complexity required to calculate the DFT. In image processing, it can be used for tasks such as image denoising, image enhancement, image smoothing and image restoration. For example, high-frequency noise in an image can be removed by low-pass filtering, thereby improving image quality; by selectively enhancing certain frequency components, specific features in the image can be made more prominent. In audio signal processing, FFT filtering also plays an important role and can be used for audio signal denoising, noise reduction and clarity improvement.

[0053] Introduction of FFT: In digital signal processing, discrete Fourier transform (DFT) is widely used because it can obtain the frequency domain characteristics of the signal. However, the traditional DFT algorithm faces the challenges of large amount of calculation and long time consumption when processing large-scale data, which is unacceptable for real-time signal processing systems. In order to overcome this limitation, the fast Fourier transform (FFT) emerged. By cleverly utilizing the symmetry and periodicity in the DFT operation, FFT can reduce the computational complexity N^2 to N*log2(N), where N is the length of the signal sequence. When the value of N is large, this optimization significantly improves the computational efficiency.

[0054] The correspondence between the FFT transform and the original signal: After FFT processing, we get N FFT result points. These points actually represent different frequency components, and the modulus value of each point also reflects the amplitude characteristics at that frequency. Specifically, if the peak value of the original signal is A, the modulus value of each point in the FFT result will be N / 2 times of A (under normalized conditions). The first point of the FFT result is the DC component, and its modulus value is N times that of the DC component. In addition to amplitude information, the FFT result also provides phase information for each frequency component.

[0055] FFT processing: Regarding the specific calculation of frequency points in the FFT result, we can understand it this way: the frequency Fn corresponding to the nth point (n starts counting from 1) in the FFT result is (n-1)*Fs / N, where Fs is the sampling frequency. This means that FFT divides the frequency axis evenly into N intervals, and the width of each interval is Fs / N. Therefore, if we want to improve the frequency resolution, that is, to distinguish different frequency components more accurately, we need to increase the number of sampling points N, which usually means increasing the sampling time. If we double the sampling time to 2 seconds, the number of sampling points will also double accordingly. This once again emphasizes the direct relationship between the number of sampling points (or sampling time) and frequency resolution.

[0056] In digital signal processing, FFT can be used to implement frequency domain filtering. The basic steps are as follows:

[0057] (1) Collecting time domain signals: Use ADC (analog-to-digital converter) or other sensors to collect time domain signals.

[0058] (2) Perform FFT: Perform FFT on the collected time domain signal to convert it into a frequency domain signal.

[0059] (3) Design filters: Design filters based on application requirements. Filters can be low-pass, high-pass, band-pass, or band-stop types, used to retain or eliminate specific frequency components in the frequency domain. Applying a filter is to multiply the filter transfer function with the FFT result in the frequency domain to eliminate or attenuate unwanted frequency components.

[0060] (4) Perform inverse FFT (IFFT): convert the filtered frequency domain signal back to the time domain signal.

[0061] (5) Output or further process the time domain signal: Output the IFFT result to a DAC (digital-to-analog converter) or other device, or perform further processing.

[0062] The method used in the present invention is the application of the ST official compilation FFT library. Although the design of this algorithm is based on the FFT implementation of the base 4, which means that the number of FFT points must be a power of 4, which limits its flexibility to a certain extent, its computational efficiency is undeniable. In the field of audio acquisition and analysis, the sampling theorem is a crucial principle. The sampling frequency must be at least twice the highest frequency of the sampled signal.

[0063] The implementation of FFT mainly includes the following three functions:

[0064] cr4_fft_64_stm32: implements 64-point FFT;

[0065] cr4_fft_256_stm32: implements 256-point FFT;

[0066] cr4_fft_1024_stm32: implements 1024-point FFT;

[0067] First, we need to determine the number of sampling points and the input and output arrays of the FFT, as well as the amplitudes of each harmonic. The FFT output array and the array of the amplitudes of each harmonic only have half the number of sampling points because the data calculated by the FFT is symmetrical. Error！Reference source not found . , so only half of the data is taken. Use the for loop to store the data collected by ADC into the input array of FFT operation.

[0068] The microprocessor module uses the DMA+TIM method, uses the ADC timer to trigger the ADC single conversion function, and then uses DMA to move data. When the number of sampling points reaches the set number of sampling points, the DMA interrupt is triggered and the interrupt handler is called. In this handler, the program reads the data transmitted by DMA (that is, the result of ADC conversion) and saves it to the corresponding memory address. Then further perform FFT, call the corresponding function for filtering, and filter the sampling filter function (rectangular filter) to obtain the required data. Its filtering principle is to fit the signal into a rectangular window of the same width, and replace the original signal value with the signal mean in the window, so as to achieve the effect of smoothing, and enable all signals below the cutoff frequency to pass losslessly, while all signals above the cutoff frequency should be infinitely attenuated. Immediately after the FFT operation, the amplitude calculation is performed, and the amplitude calculation function is as follows:

[0069] arm_cfft_radix4_f32(&scfft,fft_inputbuf); / / FFT transformation

[0070] arm_cmplx_mag_f32(fft_inputbuf,fft_outputbuf,FFT_LENGTH); / / Amplitude

[0071] Intelligent cloud analysis: To realize intelligent auscultation to analyze the patient's condition, a voice auscultation algorithm is designed, and voice recognition technology is used to analyze the sound of the patient's heart, lungs and other organs to diagnose and monitor diseases. Through this algorithm, doctors can auscultate patients more quickly and accurately, improving the accuracy and efficiency of diagnosis.

[0072] First, it is necessary to define and extract key features in heart sound signals, such as frequency, amplitude, duration, etc. Then, Fourier transform, wavelet transform and other signal processing methods are used to extract more complex features, and the algorithm is trained and verified using known heart sound data sets. The training model is used to identify the association between these heart sound features and different diseases, thereby assisting doctors in diagnosis and evaluating the algorithm's performance indicators such as accuracy, sensitivity and specificity.

[0073] Finally, a cloud server is built to implement the voice auscultation algorithm on the microprocessor module, and then the collected data is automatically uploaded to the server in the cloud to eliminate noise and analyze it. The corresponding algorithms and models are set up on the cloud server, and the auscultation data is processed and interpreted using signal processing technology, machine learning algorithms and other methods. The key features of the extracted heart sound signals are compared with the preset thresholds or reference data, and the patient's heart condition is judged based on the algorithm analysis results. After the analysis is completed, the server will send the diagnosis results and treatment recommendations back to the user's mobile phone.

[0074] Those skilled in the art can understand that the above are only preferred examples of the invention and are not intended to limit the invention. Although the invention is described in detail with reference to the above examples, those skilled in the art can still modify the technical solutions recorded in the above examples or replace some of the technical features with equivalents. Any modification, equivalent replacement, etc. made within the spirit and principle of the invention should be included in the protection scope of the invention. All technical features in this embodiment can be freely combined according to actual needs.

Claims

1. An intelligent electronic stethoscope system based on wireless transmission, characterized in that: It includes heart sound acquisition module, signal conditioning module, microprocessor module, power module, wireless transmission module, host computer module and database module; Among them, the heart sound collection module is responsible for collecting the patient's heart sound signals; The signal conditioning module is used to amplify the collected signal to obtain an analog signal, and then input it into the microprocessor module for data processing through AD conversion; The microprocessor module is used to filter and analyze the sound signal using digital signal processing technology. It uses an embedded microcontroller system encoded with digital filtering functions and uses fast Fourier transform (FFT) processing to filter out sounds of other frequencies. A power module, used for providing the electric energy required by the intelligent electronic stethoscope system; A wireless transmission module is used to transmit the processed heart sound data to the host computer module in real time; The host computer module is used to send the collected heart sound waveform to a mobile device or computer, so that doctors and users can view the heart sound waveform and related data; The database module is used to build a MySQL database, upload data to the cloud for remote query and diagnosis, and analyze the patient's condition based on the collected data.

2. A data processing method based on the wireless transmission intelligent electronic stethoscope system according to claim 1, characterized in that: The specific steps include: Step 1, ADC sampling: When performing ADC acquisition of the microprocessor module, connect a 1K to 10K pull-down resistor to the ground at the AO end of the signal acquisition and amplification module, perform ADC operation on the AO end of this module to collect signals, connect it to the serial port PA6 of the microprocessor module, connect the DO end to the PA4 pin, connect the VCC to the 3.3V of the microprocessor module, and connect the GND to the GND pin of the microprocessor module to realize signal acquisition input; Step 2, digital processing of the signal: the collected sound is filtered using an FFT filtering algorithm, which specifically includes the following steps; Step 2.1, collecting time domain signals: using an ADC analog-to-digital converter or other sensors to collect time domain signals; Step 2.2, perform FFT: perform FFT on the collected time domain signal to convert it into a frequency domain signal; Step 2.3, filter design: design the filter according to the application requirements; used to retain or eliminate specific frequency components in the frequency domain; applying the filter is to multiply the filter transfer function with the FFT result in the frequency domain to eliminate or attenuate unwanted frequency components; Step 2.4, perform inverse FFT, i.e. IFFT: convert the filtered frequency domain signal back to the time domain signal; Step 2.5, output or further process the time domain signal: output the IFFT result to a DAC digital-to-analog converter or other device, or perform further processing; Step 3, intelligent cloud analysis: specifically includes the following steps; Step 3.1, it is necessary to define and extract the key features of the heart sound signal, including frequency, amplitude, and duration; Step 3.2: Use Fourier transform, wavelet transform and other signal processing methods to extract more complex features, use known heart sound data sets to train and verify the algorithm, and use the training model to identify the association between these heart sound features and different diseases, thereby assisting doctors in diagnosis and evaluating the algorithm's performance indicators such as accuracy, sensitivity and specificity; Step 3.3, build a cloud server, implement the voice auscultation algorithm on the microprocessor module, and then automatically upload the collected data to the server in the cloud to eliminate noise and analyze it; set up corresponding algorithms and models on the cloud server, use signal processing technology, machine learning algorithms and other methods to process and interpret the auscultation data, compare the key features of the extracted heart sound signals with the preset thresholds or reference data, and judge the patient's heart condition based on the algorithm analysis results. After the analysis is completed, the server will send the diagnosis results and treatment recommendations back to the user's mobile phone.

3. The data processing method of the intelligent electronic stethoscope system based on wireless transmission according to claim 2, characterized in that: The frequency Fn corresponding to the nth point in the FFT result, i.e., n starting from 1, is (n-1)*Fs / N, where Fs is the sampling frequency; FFT divides the frequency axis evenly into N intervals, and the width of each interval is Fs / N; The implementation of FFT includes the following three functions: cr4_fft_64_stm32: implements 64-point FFT; cr4_fft_256_stm32: implements 256-point FFT; cr4_fft_1024_stm32: implements 1024-point FFT; Determine the number of sampling points, the input and output arrays of FFT, and the amplitude of each harmonic. The output array of FFT and the array of the amplitude of each harmonic only have half of the number of sampling points because the data calculated by FFT is symmetrical, so only half of the data is taken; use the for loop to store the data collected by ADC into the input array of FFT operation.

4. The data processing method of the intelligent electronic stethoscope system based on wireless transmission according to claim 3, characterized in that: The program will read the data transferred by DMA, i.e. the result of ADC conversion, and save it to the corresponding memory address; then further perform FFT, call the corresponding function for filtering, the sampling filter function, i.e. rectangular filter filtering, to obtain the required data; its filtering principle is to fit the signal into a rectangular window of the same width, and replace the original signal value with the mean value of the signal in the window, so as to achieve the effect of smoothing, and enable all signals below the cutoff frequency to pass losslessly, while all signals above the cutoff frequency should be infinitely attenuated; immediately after the FFT operation, the amplitude calculation is performed, and the amplitude calculation function is as follows: arm_cfft_radix4_f32(&scfft,fft_inputbuf); / / FFT transformation; arm_cmplx_mag_f32(fft_inputbuf,fft_outputbuf,FFT_LENGTH); / / Calculate the amplitude.

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