Medical monitor electrode wire signal transmission method based on wireless transmission

Abstract

The invention relates to the technical field of medical monitoring, and provides a medical monitor electrode wire signal transmission method based on wireless transmission. According to the method, firstly, bioelectricity signals of a patient are collected through a multi-channel electrode wire, a denoising technology based on multi-scale wavelet transform and novel differential calculation is adopted in the signal preprocessing stage, environment noise and sensor noise are removed, and high-quality preprocessed signals are obtained. Afterwards, wireless transmission is carried out through a GAO-FHWT method, and the GAO-FHWT method is combined with a genetic algorithm to dynamically optimize a frequency hopping sequence, so that the anti-interference capability of signals is improved, and stable signal transmission is realized in a complex environment. Through the above steps, the method effectively improves the monitoring precision and response speed of the medical monitor, and provides powerful technical support for the intelligence and safety of medical monitoring equipment.

Description

Technical Field

[0001] The invention relates to the technical field of medical monitoring, in particular to a medical monitor electrode line signal transmission method based on wireless transmission. Background Art

[0002] With the advancement of medical technology, medical monitoring equipment plays a vital role in monitoring patients' vital signs. Traditional medical monitoring systems usually rely on wired connections to transmit bioelectric signals. Although this method is reliable, it has many limitations. For example, the electrode wires restrict the patient's freedom of movement and the cluttered lines make it inconvenient to use. In addition, wired transmission is susceptible to electromagnetic interference, which affects the accuracy of the signal. Although the existing wireless transmission technology has solved these problems to a certain extent, there is still room for improvement in signal quality, transmission stability and anti-interference ability. Therefore, a new signal transmission method is urgently needed to achieve efficient and stable wireless signal transmission, thereby improving the convenience and reliability of medical monitoring systems. Summary of the invention

[0003] The present invention provides a medical monitor electrode line signal transmission method based on wireless transmission, aiming to solve the problems of poor signal transmission stability, weak anti-interference ability and complex signal processing in the prior art; the method collects bioelectric signals by arranging multi-channel electrode lines, pre-processes the signals by a denoising method based on multi-scale wavelet transform and novel differential calculation, and wirelessly transmits the signals through the GAO-FHWT method, thereby realizing efficient decoding and display of the signals at the receiving end; the present invention not only improves the transmission quality and stability of bioelectric signals, but also enhances the anti-interference ability of the system, ensures the real-time and accuracy of monitoring data, and provides patients with more reliable vital signs monitoring services.

[0004] The present invention provides a medical monitor electrode line signal transmission method based on wireless transmission, the method comprising the following steps:

[0005] Step S1: signal acquisition: placing multi-channel electrode wires on the patient to collect raw bioelectric signals;

[0006] Step S2: signal preprocessing: using a denoising method based on multi-scale wavelet transform and novel differential calculation to denoise, amplify and filter the original bioelectric signal to obtain a preprocessed signal;

[0007] Step S3: signal encoding: converting the preprocessed signal into digital form, and then performing compression encoding to obtain an encoded signal;

[0008] Step S4: wireless transmission: a wireless receiving module is built into the medical monitor as a receiving end, and the coded signal is transmitted to the receiving end through the GAO-FHWT method;

[0009] Step S5: Signal decoding: The receiving end decodes the encoded signal to obtain a decoded signal;

[0010] Step S6: Signal processing and display: Analyze and process the decoded signal, and display the patient's real-time status through a medical monitor.

[0011] Furthermore, the step S2 specifically includes the following steps:

[0012] Step S21: differential calculation: using a new differential calculation formula to process the original bioelectric signal to obtain a differential signal. The new differential calculation formula is as follows:

[0013] ;

[0014] in, Indicates time point No. Differential signal of channels; is the weighting factor, Indicates the total number of channels. Indicates the specific channel for which the differential signal is currently being calculated. Indicates different channels; Indicates that the differences of all channels are averaged. Indicates at time No. The original bioelectric signal of each channel, Indicates at time No. The original bioelectric signal of each channel; Indicates at time No. The original bioelectric signal of each channel, Indicates at time No. The original bioelectric signal of each channel;

[0015] Step S22: Noise feature extraction: Perform fast Fourier transform on the differential signal, extract the noise spectrum, and use the standard deviation of the differential signal as the noise amplitude;

[0016] Step S23: Noise separation and signal recovery: According to the noise spectrum and noise amplitude, multi-scale wavelet technology is introduced into the traditional noise separation and signal recovery method to perform noise separation and signal recovery to obtain a preprocessed signal.

[0017] Furthermore, the step S23 specifically includes the following steps:

[0018] Step S231: noise model construction: constructing a noise spectrum model and a noise amplitude model through the noise spectrum and the noise amplitude;

[0019] Step S2311: construct a noise spectrum model:

[0020] Use FLR low-pass filter to filter out noise components;

[0021] Step S2312: construct a noise amplitude model:

[0022] ;

[0023] in, represents the modulation signal, represents the time domain signal, represents the modulation factor, represents the noise amplitude;

[0024] Step S232: wavelet transform: performing wavelet transform on the original bioelectric signal to decompose it into multiple scale levels;

[0025] Step S233: noise separation: applying the noise spectrum model to filter at each scale level to separate the noise component and obtain a filtered signal;

[0026] Step S234: Inverse wavelet transform: perform inverse wavelet transform on the filtered signal to obtain a time domain signal;

[0027] Step S235: Amplitude modulation: modulate the time domain signal using the noise amplitude model to further suppress noise and obtain a modulated signal;

[0028] Step S236: signal reconstruction: recombining the modulated signals of each channel to obtain a reconstructed bioelectric signal;

[0029] Step S237: Signal smoothing: Use a nonlinear noise suppression exponential smoothing algorithm to smooth the reconstructed bioelectric signal to obtain a preprocessed signal. The nonlinear noise suppression exponential smoothing formula is as follows:

[0030] ;

[0031] in, represents the reconstructed bioelectric signal value at the current moment, is the reconstructed bioelectric signal value of the previous moment, is the exponential smoothing coefficient, is the nonlinear noise suppression coefficient, Reconstruct the bioelectric signal value for the current moment.

[0032] Furthermore, the step S4 specifically includes the following steps:

[0033] Step S41: setting frequency hopping sequence rules, including frequency range and hopping sequence length, to generate a set of initial frequency hopping sequences;

[0034] Step S42: gradually optimizing the frequency hopping sequence through a genetic algorithm to obtain a frequency hopping sequence with the highest fitness;

[0035] Step S43: applying the frequency hopping sequence with the highest adaptability to wireless transmission, monitoring the performance of the signal transmission process in real time, including the signal-to-noise ratio, the bit error rate and the actual transmission rate, and fine-tuning the frequency hopping sequence according to the performance of the signal transmission process;

[0036] Step S44: After the transmission is completed, the coding signal at the receiving end is verified for stability, including detecting whether there is data loss and signal attenuation;

[0037] Despite the use of frequency hopping, there is still a high bit error rate during transmission, and in high-interference environments, data loss and retransmission are more common;

[0038] Optimize frequency hopping sequences to reduce bit error rates, mitigate interference effects, and improve data transmission reliability;

[0039] The optimal frequency combination is selected by genetic algorithm after comprehensively considering signal-to-noise ratio, bit error rate and transmission delay.

[0040] Further, step S42 specifically includes the following steps:

[0041] Step S421: Generate an initial population: take each frequency hopping sequence as an individual, and randomly generate a group of frequency hopping sequence individuals;

[0042] Step S422: Designing a fitness function: According to the stability, anti-interference ability and transmission rate index of the coded signal, a fitness function is designed to calculate the individual fitness value:

[0043] ;

[0044] in, represents the signal-to-noise ratio, represents the bit error rate, Indicates the actual transmission rate. Indicates the maximum rate that the system can achieve. , and is the weight coefficient;

[0045] Step S423: Calculate individual fitness value: Calculate the fitness value of each frequency hopping sequence individual and evaluate its performance;

[0046] Step S424: Selection operation: selecting frequency hopping sequence individuals according to individual fitness values;

[0047] Step S425: crossover operation: performing a crossover operation on the selected frequency hopping sequence individuals to generate a new frequency hopping sequence;

[0048] Step S426: mutation operation: mutating the frequency hopping sequence individuals after the crossover to obtain mutated frequency hopping sequence individuals;

[0049] Step S427: Generate a new generation population: Use selection operation, crossover operation and mutation operation to generate a new generation population, iteratively evaluate the fitness of the new generation population, gradually optimize the frequency hopping sequence individuals, and obtain the frequency hopping sequence with the highest fitness.

[0050] By adopting the above scheme, the beneficial effects achieved by the present invention are as follows:

[0051] The present invention provides a medical monitor electrode line signal transmission method based on wireless transmission, which realizes efficient and stable wireless transmission of bioelectric signals. By introducing multi-channel electrode lines and a denoising method based on multi-scale wavelet transform and novel differential calculation in the signal acquisition and preprocessing stages, the signal acquisition accuracy and denoising effect are effectively improved. The multi-channel electrode lines can accurately capture the patient's bioelectric signals, and after signal decomposition and denoising processing by multi-scale wavelet transform, the interference of environmental noise and sensor noise is significantly reduced, thereby obtaining a purer preprocessed signal. The application of this technology improves the reliability of bioelectric signals and ensures the signal quality during wireless transmission. In addition, in the data smoothing stage, a nonlinear noise suppression exponential smoothing algorithm is used to process the reconstructed bioelectric signals, and the responsiveness of the signal is appropriately retained by setting the smoothing coefficient. This adjustable parameter provides flexibility for different scenarios in practical applications. Unlike some traditional linear smoothing methods, the formula dynamically adjusts the smoothing degree through nonlinear terms to ensure that the key features and morphology of the signal are retained while removing noise, reducing signal distortion, especially when the signal contains important peak information.

[0052] During the signal transmission process, the present invention adopts the GAO-FHWT method to achieve efficient wireless signal transmission through optimized frequency hopping sequences; this method combines the genetic algorithm to optimize the frequency hopping sequence, which not only improves the anti-interference ability of the transmission signal, but also achieves stable signal transmission in complex environments; the application of the genetic algorithm can dynamically optimize the frequency hopping sequence according to the actual transmission conditions, ensure the stability and accuracy of the signal during the transmission process, solve the signal loss and attenuation problems that are prone to occur in traditional wireless transmission methods, thereby enhancing the overall transmission performance and reliability.

[0053] Combined with the above analysis, this method can present the patient's vital signs in real time, improving the monitoring accuracy and response speed of medical monitors; this innovative signal transmission method not only enhances the intelligence level of medical monitoring equipment, but also makes outstanding contributions to improving the safety and reliability of medical monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 : is the FFT spectrum diagram in step B2 of the second embodiment of the present invention;

[0055] Figure 2 : is a schematic diagram of Haar wavelet decomposition in step B32 of embodiment 3 of the present invention;

[0056] Figure 3 : A schematic diagram of Haar inverse wavelet transform reconstruction in step B34 of the third embodiment of the present invention. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0058] Embodiment 1, the present invention provides a medical monitor electrode line signal transmission method based on wireless transmission, the method comprising the following steps:

[0059] Step S1: signal acquisition: placing multi-channel electrode wires on the patient to collect raw bioelectric signals;

[0060] Step S2: signal preprocessing: using a denoising method based on multi-scale wavelet transform and novel differential calculation to denoise, amplify and filter the original bioelectric signal to obtain a preprocessed signal;

[0061] Step S3: signal encoding: converting the preprocessed signal into digital form, and then performing compression encoding to obtain an encoded signal;

[0062] Step S4: wireless transmission: a wireless receiving module is built into the medical monitor as a receiving end, and the coded signal is transmitted to the receiving end through the GAO-FHWT method;

[0063] Step S5: Signal decoding: The receiving end decodes the encoded signal to obtain a decoded signal;

[0064] Step S6: Signal processing and display: Analyze and process the decoded signal, and display the patient's real-time status through a medical monitor.

[0065] Embodiment 2: This embodiment is based on the above embodiment, and step S2 specifically includes the following steps:

[0066] Step S21: differential calculation: using a new differential calculation formula to process the original bioelectric signal to obtain a differential signal. The new differential calculation formula is as follows:

[0067] ∆ x t,i = 1 n ∑ j=1 n [( x t,j - x t-1,j )] -α·( x t,i - x t-1,i ) ;

[0068] This embodiment uses 3 channels, and the original bioelectric signal data is as follows:

[0069] ;

[0070] (3 channels), , the differential signal is calculated as follows:

[0071] ;

[0072] Step S22: Noise feature extraction: Perform fast Fourier transform on the differential signal, extract the noise spectrum, and use the standard deviation of the differential signal as the noise amplitude;

[0073] The noise spectrum and noise amplitude are extracted based on the differential signal as follows:

[0074] The differential signals are combined and the fast Fourier transform is applied to convert them from the time domain to the frequency domain. The FFT spectrum is obtained by applying the fast Fourier transform. Figure 1 , it can be seen that 50Hz is the strongest frequency component and the corresponding noise amplitude is 0.1mV (in this embodiment, due to the large amount of data, it is inconvenient to expand and only provide some example data);

[0075] It follows that:

[0076] Noise spectrum: 50Hz;

[0077] Noise amplitude: 0.1mV;

[0078] Step S23: Noise separation and signal recovery: According to the noise spectrum and noise amplitude, multi-scale wavelet technology is introduced into the traditional noise separation and signal recovery method to perform noise separation and signal recovery to obtain a preprocessed signal.

[0079] Embodiment 3: This embodiment is based on the above embodiment, and step S23 specifically includes the following steps:

[0080] Step S231: noise model construction: constructing a noise spectrum model and a noise amplitude model through the noise spectrum and the noise amplitude;

[0081] Step S2311: construct a noise spectrum model:

[0082] Design an FLR low-pass filter to filter out noise components with frequencies above 50Hz:

[0083] pseudocode:

[0084] # Define filter parameters

[0085] Fs = 1000 # sampling frequency (Hz)

[0086] Fc = 50 # Cutoff frequency (Hz)

[0087] N = 101 # filter order

[0088] # Define window function

[0089] def hamming_window(n, N):

[0090] return 0.54 - 0.46 * cos(2 * pi * n / (N - 1))

[0091] # Design filter coefficients (low-pass filter uses ideal Sinc function)

[0092] def design_lowpass_filter(Fc, Fs, N):

[0093] h = []

[0094] for n in range(N):

[0095] if n == (N - 1) / 2:

[0096] h.append(2 * Fc / Fs)

[0097] else:

[0098] h.append(sin(2 * pi * Fc / Fs * (n - (N - 1) / 2)) / (pi* (n - (N - 1) / 2)))

[0099] # Apply window function

[0100] h[n] *= hamming_window(n, N)

[0101] return h

[0102] # Apply the filter to the signal

[0103] def apply_filter(signal, filter_coefficients):

[0104] filtered_signal = []

[0105] for i in range(len(signal)):

[0106] filtered_value = 0

[0107] for j in range(len(filter_coefficients)):

[0108] if i - j >= 0:

[0109] filtered_value += signal[i - j] * filter_coefficients[j]

[0110] filtered_signal.append(filtered_value)

[0111] return filtered_signal

[0112] # Main program

[0113] def main():

[0114] # Input signal, assuming the signal is obtained from a source

[0115] input_signal = get_input_signal()

[0116] # Design a low-pass filter

[0117] filter_coefficients = design_lowpass_filter(Fc, Fs, N)

[0118] # Apply the filter to the input signal

[0119] filtered_signal = apply_filter(input_signal, filter_coefficients)

[0120] # Output or save the filtered signal

[0121] save_filtered_signal(filtered_signal)

[0122] # Call the main program

[0123] main();

[0124] Step S2312: construct a noise amplitude model:

[0125] .

[0126] Step S232: Wavelet transform: According to Figure 2 , wavelet transform is performed on the original bioelectric signal to decompose it into multiple scale levels;

[0127] Original bioelectric signal: [0.5, 0.7, 0.6, 0.8, 0.9, 1.0];

[0128] Use Haar wavelet to perform two-level decomposition of [0.5, 0.7, 0.6, 0.8, 0.9, 1.0], by convolving the original signal with a high-pass filter and then downsampling the index value to obtain the detail coefficient; convolve the original signal with a low-pass filter and then downsample to obtain the approximation coefficient;

[0129] The detail coefficient captures the high frequency part, and the approximation coefficient captures the low frequency part;

[0130] After wavelet transform:

[0131] ;

[0132] Step S233: noise separation: applying the noise spectrum model to filter at each scale level to separate the noise component and obtain a filtered signal;

[0133] At each scale, the FLR low-pass filter used in the noise model construction is applied to filter out the noise components with frequencies of 50 Hz and above, and the filtered signal is obtained:

[0134] ;

[0135] Step S234: Inverse wavelet transform: According to Figure 3 , perform inverse wavelet transform on the filtered signal to obtain the time domain signal;

[0136] The filtered signal is reconstructed by inverse transforming the Haar wavelet to obtain the time domain signal:

[0137] ;

[0138] Step S235: Amplitude modulation: modulate the time domain signal using the noise amplitude model to further suppress noise and obtain a modulated signal;

[0139] The amplitude modulation coefficient is 0.5

[0140] ;

[0141] ;

[0142] The modulation signal is:

[0143] ;

[0144] Step S236: signal reconstruction: recombining the modulated signals of each channel to obtain a reconstructed bioelectric signal;

[0145] For ease of understanding, this embodiment only shows the process of obtaining the modulation signal of one channel, and the methods of obtaining the modulation signal of the other channels are similar;

[0146] The modulated signals of the above three channels are weighted and summed to obtain the reconstructed bioelectric signal as follows:

[0147] ;

[0148] Step S237: Signal smoothing: Use a nonlinear noise suppression exponential smoothing algorithm to smooth the reconstructed bioelectric signal to obtain a preprocessed signal. The nonlinear noise suppression exponential smoothing formula is as follows:

[0149] ;

[0150] in, represents the reconstructed bioelectric signal value at the current moment, is the reconstructed bioelectric signal value of the previous moment, is the exponential smoothing coefficient, is the nonlinear noise suppression coefficient, Reconstruct the bioelectric signal value for the current moment.

[0151] In this embodiment, it is set , ;

[0152] Preprocessing signal initial value ,

[0153] ;

[0154] And so on

[0155] The results are as follows:

[0156] .

[0157] Embodiment 4: This embodiment is based on the above embodiment, and step S4 specifically includes the following steps:

[0158] Step S41: setting frequency hopping sequence rules, including frequency range and hopping sequence length, to generate a set of initial frequency hopping sequences;

[0159] Despite the use of frequency hopping, there is still a high bit error rate during transmission, and in high-interference environments, data loss and retransmission are more common;

[0160] Optimize frequency hopping sequence to improve signal-to-noise ratio, reduce bit error rate, and increase transmission rate to reduce interference and improve data transmission reliability;

[0161] Frequency hopping rules:

[0162] (1) Frequency range: 2.4 GHz to 2.4835 GHz;

[0163] (2) Hopping sequence length: 5;

[0164] The initial frequency hopping sequence: {2.41GHz, 2.42GHz, 2.43GHz, 2.44GHz};

[0165] The frequency hopping sequence represents a signal transmission channel, and the signal jumps between these frequencies to reduce interference;

[0166] Step S42: gradually optimizing the frequency hopping sequence through a genetic algorithm to obtain a frequency hopping sequence with the highest fitness;

[0167] The optimal frequency combination is selected by genetic algorithm after comprehensively considering signal-to-noise ratio, bit error rate and transmission delay;

[0168] Step S43: applying the frequency hopping sequence with the highest adaptability to wireless transmission, monitoring the performance of the signal transmission process in real time, including the signal-to-noise ratio, the bit error rate and the actual transmission rate, and fine-tuning the frequency hopping sequence according to the performance of the signal transmission process;

[0169] Step S431: Setting performance standards:

[0170] ;

[0171] ;

[0172] ;

[0173] Step S432: Transmitting coded signal: 1011001110001111;

[0174] Step S433: Apply the frequency hopping sequence with the highest adaptability:

[0175] {2.42GHz, 2.41GHz, 2.44GHz, 2.47GHz};

[0176] Step S434: signal transmission: the coded signal is transmitted in frequency order, the coded signal is divided into 4 blocks, and each block is transmitted at a corresponding frequency;

[0177] 2.42GHz sends 1011;

[0178] 2.41GHz sends 0011;

[0179] 2.44GHz sends 1000;

[0180] 2.47GHz sends 1111;

[0181] Step S435: Real-time monitoring of signal transmission performance:

[0182] The test results are as follows:

[0183] Detected when sending at 2.42GHz: , , ;

[0184] Detected when sending at 2.41GHz: , , ;

[0185] Detected when sending at 2.44GHz: , , ;

[0186] Detected when sending at 2.47GHz: , , ;

[0187] All are within the acceptable range;

[0188] Step S44: After the transmission is completed, the coding signal at the receiving end is verified for stability, including detecting whether there is data loss and signal attenuation;

[0189] Data loss verification: The received signal is 1011001110001111, and there is no data loss when compared with the sent signal;

[0190] Signal attenuation verification: No signal attenuation.

[0191] Embodiment 5, based on the above embodiment, step S42 specifically includes the following steps:

[0192] Step S421: Generate an initial population: take each frequency hopping sequence as an individual, and randomly generate a group of frequency hopping sequence individuals;

[0193] Randomly generate 3 initial population individuals:

[0194] Individual 1: {2.41GHz, 2.42GHz, 2.43GHz, 2.44GHz};

[0195] Individual 2: {2.45GHz, 2.46GHz, 2.47GHz, 2.48GHz};

[0196] Individual 3: {2.40GHz, 2.41GHz, 2.42GHz, 2.43GHz};

[0197] Step S422: Designing a fitness function: According to the stability, anti-interference ability and transmission rate index of the coded signal, designing a fitness function for calculating the individual fitness value;

[0198] ;

[0199] in, represents the signal-to-noise ratio, represents the bit error rate, Indicates the actual transmission rate. Indicates the maximum rate that the system can achieve. , and is the weight coefficient;

[0200] In this embodiment, the weight coefficient is determined as follows:

[0201] , , ;

[0202] Step S423: Calculate individual fitness value: Calculate the fitness value of each frequency hopping sequence individual and evaluate its performance;

[0203] Instance 1: , , ;

[0204] Instance 2: , , ;

[0205] Instance 3: , , ;

[0206] , calculate the fitness:

[0207] Instance 1: ;

[0208] Instance 2: ;

[0209] Instance 3: ;

[0210] Individual 1>Individual 2>Individual 3;

[0211] Step S424: Selection operation: selecting frequency hopping sequence individuals according to individual fitness values;

[0212] Select individuals 1 and 2 with higher fitness;

[0213] Step S425: crossover operation: performing a crossover operation on the selected frequency hopping sequence individuals to generate a new frequency hopping sequence;

[0214] Individual 1 and individual 2 cross to generate a new individual:

[0215] New individual: {2.41GHz, 2.46GHz, 2.43GHz, 2.44GHz};

[0216] Step S426: mutation operation: mutating the frequency hopping sequence individuals after the crossover to obtain mutated frequency hopping sequence individuals;

[0217] Individual variation: {2.41GHz, 2.46GHz, 2.45GHz, 2.44GHz};

[0218] Step S427: Generate a new generation population: Use selection operation, crossover operation and mutation operation to generate a new generation population, iteratively evaluate the fitness of the new generation population, gradually optimize the frequency hopping sequence individuals, and obtain the frequency hopping sequence with the highest fitness.

[0219] After 50 iterations, the frequency hopping sequence with the highest fitness is selected:

[0220] {2.42GHz, 2.41GHz, 2.44GHz, 2.47GHz}.

[0221] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A medical monitor electrode line signal transmission method based on wireless transmission, characterized in that: The method comprises the following steps: Step S1: signal acquisition: placing multi-channel electrode wires on the patient to collect raw bioelectric signals; Step S2: signal preprocessing: using a denoising method based on multi-scale wavelet transform and novel differential calculation to denoise, amplify and filter the original bioelectric signal to obtain a preprocessed signal; Step S3: signal encoding: converting the preprocessed signal into digital form, and then performing compression encoding to obtain an encoded signal; Step S4: wireless transmission: a wireless receiving module is built into the medical monitor as a receiving end, and the coded signal is transmitted to the receiving end through the GAO-FHWT method; Step S5: Signal decoding: The receiving end decodes the encoded signal to obtain a decoded signal; Step S6: Signal processing and display: Analyze and process the decoded signal, and display the patient's real-time status through a medical monitor.

2. The method for transmitting electrode line signals of a medical monitor based on wireless transmission according to claim 1, characterized in that: Step S2 specifically includes the following steps: Step S21: differential calculation: using a novel differential calculation formula to process the original bioelectric signal to obtain a differential signal; Step S22: Noise feature extraction: Perform fast Fourier transform on the differential signal, extract the noise spectrum, and use the standard deviation of the differential signal as the noise amplitude; Step S23: Noise separation and signal recovery: According to the noise spectrum and noise amplitude, multi-scale wavelet technology is introduced into the traditional noise separation and signal recovery method to perform noise separation and signal recovery to obtain a preprocessed signal.

3. The method for transmitting electrode line signals of a medical monitor based on wireless transmission according to claim 2, characterized in that: Step S23 specifically includes the following steps: Step S231: noise model construction: constructing a noise spectrum model and a noise amplitude model through the noise spectrum and the noise amplitude; Step S232: wavelet transform: performing wavelet transform on the original bioelectric signal to decompose it into multiple scale levels; Step S233: noise separation: applying the noise spectrum model to filter at each scale level to separate the noise component and obtain a filtered signal; Step S234: Inverse wavelet transform: perform inverse wavelet transform on the filtered signal to obtain a time domain signal; Step S235: Amplitude modulation: modulate the time domain signal using the noise amplitude model to further suppress noise and obtain a modulated signal; Step S236: signal reconstruction: recombining the modulated signals of each channel to obtain a reconstructed bioelectric signal; Step S237: Signal smoothing: Use a nonlinear noise suppression exponential smoothing algorithm to smooth the reconstructed bioelectric signal to obtain a preprocessed signal.

4. The method for transmitting electrode line signals of a medical monitor based on wireless transmission according to claim 1, characterized in that: The step S4 specifically comprises the following steps: Step S41: setting frequency hopping sequence rules, including frequency range and hopping sequence length, to generate a set of initial frequency hopping sequences; Step S42: gradually optimizing the frequency hopping sequence through a genetic algorithm to obtain a frequency hopping sequence with the highest fitness; Step S43: applying the frequency hopping sequence with the highest adaptability to wireless transmission, monitoring the performance of the signal transmission process in real time, including the signal-to-noise ratio, the bit error rate and the actual transmission rate, and fine-tuning the frequency hopping sequence according to the performance of the signal transmission process; Step S44: After the transmission is completed, the coding signal at the receiving end is verified for stability, including detecting whether there is data loss and signal attenuation.

5. The method for transmitting electrode line signals of a medical monitor based on wireless transmission according to claim 4, characterized in that: Step S42 specifically includes the following steps: Step S421: Generate an initial population: take each frequency hopping sequence as an individual, and randomly generate a group of frequency hopping sequence individuals; Step S422: Designing a fitness function: According to the stability, anti-interference ability and transmission rate index of the coded signal, designing a fitness function for calculating the individual fitness value; Step S423: Calculate individual fitness value: Calculate the fitness value of each frequency hopping sequence individual and evaluate its performance; Step S424: Selection operation: selecting frequency hopping sequence individuals according to individual fitness values; Step S425: crossover operation: performing a crossover operation on the selected frequency hopping sequence individuals to generate a new frequency hopping sequence; Step S426: mutation operation: mutating the frequency hopping sequence individuals after the crossover to obtain mutated frequency hopping sequence individuals; Step S427: Generate a new generation population: Use selection operation, crossover operation and mutation operation to generate a new generation population, iteratively evaluate the fitness of the new generation population, gradually optimize the frequency hopping sequence individuals, and obtain the frequency hopping sequence with the highest fitness.

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