A wireless transmission-based medical monitor electrode line signal transmission method

Abstract

The application relates to the technical field of medical monitoring, and provides a medical monitor electrode line signal transmission method based on wireless transmission; the method first collects bioelectric signals of a patient through a multichannel electrode line, and adopts a denoising technology based on a multi-scale wavelet transform and a novel differential calculation in a signal preprocessing stage to remove environmental noise and sensor noise, so that high-quality preprocessed signals are obtained. Then, wireless transmission is carried out through a GAO-FHWT method; the GAO-FHWT method combines a genetic algorithm to dynamically optimize a frequency hopping sequence, not only improves the anti-interference ability of signals, but also realizes stable signal transmission in a complex environment. Through the above steps, the method effectively improves the monitoring precision and response speed of the medical monitor, and provides strong technical support for the intellectualization and safety of medical monitoring equipment.

Description

TECHNICAL FIELD

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

[0002] With the progress of medical technology, medical monitoring devices play a crucial 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, such as the restriction of patients' freedom of movement by electrode lines, and the inconvenience caused by messy lines. In addition, wired transmission is susceptible to electromagnetic interference, affecting the accuracy of the signal. Existing wireless transmission technologies have solved these problems to some extent, but there is still room for improvement in signal quality, transmission stability, and anti-interference ability. Therefore, there is an urgent need for a new signal transmission method to achieve efficient and stable wireless signal transmission, thereby improving the convenience and reliability of medical monitoring systems. SUMMARY

[0003] The present application provides a medical monitor electrode line signal transmission method based on wireless transmission, which aims 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 through multi-channel electrode lines, uses a denoising method based on multi-scale wavelet transform and new differential calculation for signal preprocessing, and transmits the signal wirelessly through the GAO-FHWT method, thereby achieving efficient decoding and display of the signal at the receiving end. The present application not only improves the transmission quality and stability of bioelectric signals, but also enhances the anti-interference ability of the system, ensuring the real-time and accuracy of the monitoring data, and providing more reliable vital sign monitoring services for patients.

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

[0005] Step S1: Signal acquisition: arrange multi-channel electrode lines on the patient's body to collect raw bioelectric signals;

[0006] Step S2: Signal preprocessing: use a denoising method based on multi-scale wavelet transform and new differential calculation to denoise, amplify, and filter the raw bioelectric signals to obtain preprocessed signals;

[0007] Step S3: Signal encoding: perform analog-to-digital conversion on the preprocessed signals, then compress and encode them to obtain encoded signals;

[0008] Step S4: Wireless transmission: embed a wireless receiving module in the medical monitor as the receiving end, and transmit the encoded signals 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: the decoded signal is analyzed and processed, and the real-time state of the patient is displayed through the medical monitor.

[0011] Further, the step S2 specifically comprises the following steps:

[0012] Step S21: difference calculation: using a new difference calculation formula, the original bioelectric signal is processed to obtain a difference signal, the new difference calculation formula is as follows:

[0013] ;

[0014] wherein, denotes the difference signal of the i th channel at the time point t; is a weighting factor, denotes the total number of channels, denotes the specific channel currently calculating the difference signal, denotes different channels; denotes the average of the difference values of all channels, denotes the original bioelectric signal of the i th channel at the time t; denotes the original bioelectric signal of the i th channel at the time t; denotes the original bioelectric signal of the i th channel at the time t, denotes the original bioelectric signal of the i th channel at the time t; denotes the original bioelectric signal of the i th channel at the time t, denotes the original bioelectric signal of the i th channel at the time t; denotes the original bioelectric signal of the i th channel at the time t, denotes the original bioelectric signal of the i th channel at the time t; denotes the original bioelectric signal of the i th channel at the time t, denotes the original bioelectric signal of the i th channel at the time t; denotes the original bioelectric signal of the i th channel at the time t, denotes the original bioelectric signal of the i th channel at the time t; denotes the original bioelectric signal of the i th channel at the time t, denotes the original bioelectric signal of the i th channel at the time t;

[0015] Step S22: noise feature extraction: the difference signal is subjected to fast Fourier transform to extract the noise spectrum, and the standard deviation of the difference signal is used as the noise amplitude;

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

[0017] Further, the step S23 specifically comprises the following steps:

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

[0019] Step S2311: Constructing noise spectrum model:

[0020] Filtering noise component using FLR low-pass filter;

[0021] Step S2312: Constructing noise amplitude model:

[0022]

[0023] wherein, denotes modulated signal, denotes time-domain signal, denotes modulation factor, denotes noise amplitude;

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

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

[0026] Step S234: Inverse wavelet transform: performing inverse wavelet transform on filtered signal to obtain time-domain signal;

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

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

[0029] Step S237: Signal smoothing: performing smoothing processing on reconstructed bioelectric signal using nonlinear noise suppression exponential smoothing algorithm to obtain preprocessed signal, and the nonlinear noise suppression exponential smoothing formula is as follows:

[0030]

[0031] wherein, denotes reconstructed bioelectric signal value at current time, denotes reconstructed bioelectric signal value at previous time, is exponential smoothing coefficient, is nonlinear noise suppression coefficient, is reconstructed bioelectric signal value at current time.

[0032] Further, the step S4 specifically comprises the following steps:

[0033] ​​Step S41: Set the frequency hopping sequence rule, including the frequency range and the hopping sequence length, and generate a set of initial frequency hopping sequences;

[0034] Step S42: Optimize the frequency hopping sequence step by step through the genetic algorithm to obtain the highest fitness frequency hopping sequence;

[0035] Step S43: Apply the highest fitness frequency hopping sequence to wireless transmission, monitor the performance in real time during signal transmission, including signal-to-noise ratio, bit error rate, and actual transmission rate, and fine-tune the frequency hopping sequence according to the performance during signal transmission;

[0036] Step S44: At the end of transmission, verify the stability of the encoded signal at the receiving end, including detecting whether there is data loss and signal attenuation;

[0037] Although frequency hopping is used, there is still a high bit error rate during transmission, and in a high interference environment, data loss and retransmission are more common;

[0038] Optimize the frequency hopping sequence to reduce the bit error rate, reduce the interference effect, and improve the reliability of data transmission;

[0039] Through the genetic algorithm, the optimal frequency combination is selected after considering the 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: each frequency hopping sequence is an individual, and a set of frequency hopping sequence individuals is randomly generated;

[0042] Step S422: Design the fitness function: according to the stability of the encoded signal, the anti-interference ability, and the transmission rate index, design a fitness function for calculating the individual fitness value:

[0043] ;

[0044] Wherein, SNR represents the signal-to-noise ratio, BER represents the bit error rate, T represents the actual transmission rate, C represents the maximum rate that the system can achieve, , and are weight coefficients;

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

[0046] Step S424: selection operation: selecting the frequency hopping sequence individual according to the individual fitness value;

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

[0048] Step S426: mutation operation: performing the mutation operation on the frequency hopping sequence individual after the crossover to obtain the frequency hopping sequence individual after the mutation;

[0049] Step S427: generating a new generation population: generating a new generation population using the selection operation, the crossover operation and the mutation operation, iteratively evaluating the fitness of the new generation population, gradually optimizing the frequency hopping sequence individual, and obtaining the frequency hopping sequence with the highest fitness.

[0050] The beneficial effects achieved by the application are as follows:

[0051] The medical monitor electrode line signal transmission method based on wireless transmission provided by the application realizes efficient and stable wireless transmission of bioelectric signals; the multi-channel electrode line and the denoising method based on multi-scale wavelet transform and a novel differential calculation are introduced in the signal acquisition and preprocessing stage, effectively improving the signal acquisition accuracy and denoising effect; the multi-channel electrode line can accurately capture the bioelectric signals of the patient, and after signal decomposition and denoising processing by multi-scale wavelet transform, the interference of environmental noise and sensor noise is significantly reduced, so that a more pure preprocessed signal is obtained; the application of this technology improves the reliability of bioelectric signals and ensures the signal quality in the wireless transmission process; in addition, the reconstructed bioelectric signal is processed by using the nonlinear noise suppression exponential smoothing algorithm in the data smoothing stage, and the responsiveness of the signal is appropriately retained by setting the smoothing coefficient, and this adjustable parameter provides flexibility for different scenarios in actual application; unlike some traditional linear smoothing methods, the formula dynamically adjusts the smoothing degree through a nonlinear term, ensures that the key features and morphology of the signal are retained while the noise is removed, reduces signal distortion, and is particularly advantageous when the signal contains important peak information.

[0052] In the signal transmission process, the GAO-FHWT method is used to realize efficient wireless signal transmission through an optimized frequency hopping sequence; this method optimizes the frequency hopping sequence by combining genetic algorithm, not only improves the anti-interference ability of the transmitted signal, but also realizes stable signal transmission in complex environments; the application of genetic algorithm can dynamically optimize the frequency hopping sequence according to the actual transmission conditions, ensure the stability and accuracy of the signal in the transmission process, solve the problems of signal loss and attenuation that are prone to occur in traditional wireless transmission methods, and thus enhance the overall transmission performance and reliability.

[0053] In combination with the above analysis, the method can present the vital sign state of the patient in real time, and improves the monitoring accuracy and response speed of the medical monitor. The innovative signal transmission method not only enhances the intelligent level of the medical monitoring device, but also makes outstanding contributions to improving the safety and reliability of medical monitoring. BRIEF DESCRIPTION OF DRAWINGS

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

[0055] Figure 2 : Haar wavelet decomposition schematic diagram in step B32 in the third embodiment of the present application;

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

[0057] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0058] In the first embodiment, the present application provides a medical monitor electrode line signal transmission method based on wireless transmission, which comprises the following steps:

[0059] Step S1: signal acquisition: arranging multiple channel electrode lines on the patient to collect original bioelectric signals;

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

[0061] Step S3: signal coding: performing analog-to-digital conversion on the preprocessed signals, and then performing compression coding to obtain coded signals;

[0062] Step S4: wireless transmission: embedding a wireless receiving module in the medical monitor as a receiving end, and transmitting the coded signals to the receiving end through the GAO-FHWT method;

[0063] Step S5: signal decoding: decoding the coded signals by the receiving end to obtain decoded signals;

[0064] Step S6: signal processing and display: analyzing and processing the decoded signals, and displaying the real-time state of the patient through the medical monitor.

[0065] Embodiment two, based on the above embodiment, step S2 specifically includes the following steps:

[0066] Step S21: difference calculation: using a new difference calculation formula, the original bioelectric signal is processed to obtain a difference signal, the new difference 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 adopts 3 channels, and the original bioelectric signal data is as follows:

[0069] ;

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

[0071] ;

[0072] Step S22: noise feature extraction: the difference signal is subjected to fast Fourier transform, and the noise spectrum is extracted, and the standard deviation of the difference signal is used as the noise amplitude;

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

[0074] The difference signal is merged and subjected to fast Fourier transform to convert it from time domain to frequency domain, and the FFT spectrum is obtained by applying fast Fourier transform, according to Figure 1 It can be seen that the noise amplitude corresponding to the strongest frequency component of 50Hz is 0.1mV (in this embodiment, due to the large amount of data, only part of the example data is provided);

[0075] Therefore:

[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, introduce multi-scale wavelet technology in the traditional noise separation and signal recovery method for noise separation and signal recovery, and obtain the pretreated signal.

[0079] Example three, based on the above example, step S23 specifically includes the following steps:

[0080] Step S231: Noise model construction: construct the noise spectrum model and noise amplitude model through the noise spectrum and noise amplitude;

[0081] Step S2311: Construct the noise spectrum model:

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

[0083] Pseudo code:

[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 (ideal Sinc function is used for low-pass filter)

[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 filter to 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, assume signal is obtained from some source

[0115] input_signal = get_input_signal()

[0116] # Design lowpass filter

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

[0118] # Apply filter to 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 the noise amplitude model:

[0125] .

[0126] Step S232: Wavelet transform: According to Figure 2 , the original bioelectric signal is wavelet transformed to be decomposed into multiple scale levels;

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

[0128] Two-level decomposition of [0.5, 0.7, 0.6, 0.8, 0.9, 1.0] is performed using Haar wavelet, by convolving the original signal with a high-pass filter and then taking the index value down-sampling to obtain the detail coefficient; the original signal is convolved with a low-pass filter and then down-sampled 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: Apply the noise spectrum model on each scale level to filter out the noise component and obtain the filtered signal;

[0133] Apply the FLR low-pass filter in the construction of the noise model on each scale to filter out the noise component with a frequency of 50 Hz and above to obtain the filtered signal:

[0134] ;

[0135] Step S234: Inverse wavelet transform: According to Figure 3 , the filtered signal is inverse wavelet transformed to obtain a time-domain signal;

[0136] The filtered signal is inversely transformed using Haar wavelet to reconstruct a time-domain signal:

[0137]

[0138] Step S235: amplitude modulation: the time-domain signal is modulated using a noise amplitude model to further suppress noise, obtaining a modulated signal;

[0139] The amplitude modulation coefficient is 0.5

[0140]

[0141]

[0142] The modulated signal is:

[0143]

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

[0145] For ease of understanding, only the calculation process of a channel modulated signal is shown in this embodiment, and the calculation methods of the remaining channels are similar.

[0146] The modulated signals of the above 3 channels are weighted and summed to obtain a reconstructed bioelectric signal as follows:

[0147]

[0148] Step S237: signal smoothing: the reconstructed bioelectric signal is smoothed using a nonlinear noise suppression exponential smoothing algorithm to obtain a preprocessed signal, and the nonlinear noise suppression exponential smoothing formula is as follows:

[0149]

[0150] wherein, represents the reconstructed bioelectric signal value at the current time, represents the reconstructed bioelectric signal value at the previous time, is an exponential smoothing coefficient, is a nonlinear noise suppression coefficient, is the reconstructed bioelectric signal value at the current time.

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

[0152] The initial value of the preprocessed signal is ,

[0153] ​​​​​​​​

[0154] and so on

[0155] The results are as follows:

[0156] .

[0157] In Example Four, based on the above-mentioned examples, step S4 specifically includes the following steps:

[0158] Step S41: Set the frequency hopping sequence rule, including the frequency range and the hopping sequence length, and generate a set of initial frequency hopping sequences;

[0159] Although frequency hopping is used, there is still a high bit error rate during transmission, and in a high interference environment, data loss and retransmission are more common;

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

[0161] Frequency hopping rule:

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

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

[0164] 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 hops between these frequencies to reduce interference through frequency hopping;

[0166] Step S42: Optimize the frequency hopping sequence step by step through the genetic algorithm to obtain the frequency hopping sequence with the highest fitness;

[0167] After considering the signal-to-noise ratio, bit error rate, and transmission delay through the genetic algorithm, the optimal frequency combination is selected;

[0168] Step S43: Apply the frequency hopping sequence with the highest fitness to wireless transmission, real-time monitor the performance during signal transmission, including signal-to-noise ratio, bit error rate, and actual transmission rate, and fine-tune the frequency hopping sequence according to the performance during signal transmission;

[0169] Step S431: Set the performance standards:

[0170] ;

[0171] ;

[0172] ;

[0173] Step S432: Transmission of the encoded signal: 1011001110001111;

[0174] Step S433: Application of the frequency hopping sequence with the highest fitness:

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

[0176] Step S434: Signal transmission: transmit the encoded signal in frequency order, divide the encoded signal into 4 blocks, and transmit each block at the corresponding frequency;

[0177] 2.42GHz transmits 1011;

[0178] 2.41GHz transmits 0011;

[0179] 2.44GHz transmits 1000;

[0180] 2.47GHz transmits 1111;

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

[0182] The detection results are as follows:

[0183] When transmitting at 2.42GHz, it is monitored that: , , ;

[0184] When transmitting at 2.41GHz, it is monitored that: , , ;

[0185] When transmitting at 2.44GHz, it is monitored that: , , ;

[0186] When transmitting at 2.47GHz, it is monitored that: , , ;

[0187] All are within the acceptable range;

[0188] Step S44: Transmission ends, stability verification of the encoded signal at the receiving end, including detection of whether there is data loss and signal attenuation;

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

[0190] Signal attenuation verification: no signal attenuation.

[0191] In Example Five, based on the above-mentioned examples, step S42 specifically comprises the following steps:

[0192] Step S421: generating an initial population: randomly generating a set of frequency hopping sequence individuals, each frequency hopping sequence being an individual;

[0193] Randomly generate 3 initial population individuals:

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

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

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

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

[0198] ;

[0199] wherein, SNR represents the signal-to-noise ratio, BER represents the bit error rate, R represents the actual transmission rate, C represents the maximum rate that the system can achieve, , and are weight coefficients;

[0200] In this embodiment, the weight coefficients are determined as follows:

[0201] , , ;

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

[0203] Individual 1: , , ;

[0204] Individual 2: , , ;

[0205] Individual 3: , , ;

[0206] , calculate fitness:

[0207] Individual 1: ;

[0208] Individual 2: ;

[0209] Individual 3: ;

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

[0211] Step S424: selection operation: select the frequency hopping sequence individual according to the individual fitness value;

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

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

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

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

[0216] Step S426: mutation operation: perform mutation on the frequency hopping sequence individual after crossover to obtain the frequency hopping sequence individual after mutation;

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

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

[0219] Select the frequency hopping sequence with the highest fitness after 50 iterations:

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

[0221] The above describes the present application and its embodiments, which are not limited, and the drawings only show one of the embodiments of the present application, and the actual structure is not limited thereto. In general, if a person skilled in the art is inspired thereby, without departing from the purpose of the present application, without creative design, similar structure and embodiments of the technical solution are not creative, and should belong to the protection scope of the present application.

Claims

1. A method for transmitting signals via electrode wires in a medical monitor based on wireless transmission, characterized in that: The method includes the following steps: Step S1: Signal Acquisition: Multi-channel electrode wires are placed on the patient to acquire raw bioelectrical signals; Step S2: Signal preprocessing: The original bioelectric signal is denoised, amplified, and filtered using a denoising method based on multi-scale wavelet transform and novel differential computation to obtain a preprocessed signal; Step S3: Signal Encoding: The preprocessed signal is converted from analog to digital and then compressed and encoded to obtain the encoded signal; Step S4: Wireless transmission: A wireless receiving module is built into the medical monitor as the receiving end, and the encoded 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 the decoded signal; Step S6: Signal Processing and Display: The decoded signal is analyzed and processed, and the patient's real-time status is displayed through the medical monitor; Step S4 specifically includes the following steps: Step S41: Set the frequency jump sequence rules, including the frequency range and jump sequence length, and generate a set of initial frequency jump sequences; Step S42: Optimize the frequency jump sequence step by step using a genetic algorithm to obtain the frequency jump sequence with the highest fitness; Step S43: Apply the frequency hopping sequence with the highest fitness to wireless transmission, monitor the performance of the signal transmission process in real time, including signal-to-noise ratio, bit error rate and actual transmission rate, and fine-tune the frequency hopping sequence according to the performance of the signal transmission process. Step S44: Transmission ends. The stability of the encoded signal at the receiving end is verified, including detecting whether there is data loss and signal attenuation.

2. The method for transmitting electrode wire signals in 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: The original bioelectric signal is processed using a novel differential calculation formula to obtain a differential signal; Step S22: Noise feature extraction: Perform a fast Fourier transform on the differential signal to extract the noise spectrum, and use the standard deviation of the differential signal as the noise amplitude; Step S23: Noise separation and signal recovery: Based on 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, and a preprocessed signal is obtained.

3. The method for transmitting electrode wire signals in 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: Construct a noise spectrum model and a noise amplitude model using the noise spectrum and noise amplitude. Step S232: Wavelet transform: Perform wavelet transform on the original bioelectric signal to decompose it into multiple scale levels; Step S233: Noise separation: Apply the noise spectrum model to filter at each scale level to separate the noise components and obtain the filtered signal; Step S234: Inverse wavelet transform: Perform inverse wavelet transform on the filtered signal to obtain the time-domain signal; Step S235: Amplitude Modulation: The time-domain signal is modulated using a noise amplitude model to further suppress noise and obtain a modulated signal; Step S236: Signal reconstruction: Recombining the modulation signals of each channel to obtain the reconstructed bioelectric signal; Step S237: Signal smoothing: The reconstructed bioelectric signal is smoothed using a nonlinear noise suppression exponential smoothing algorithm to obtain a preprocessed signal.

4. The method for transmitting electrode wire signals in a medical monitor based on wireless transmission according to claim 1, characterized in that: Step S42 specifically includes the following steps: Step S421: Generate the initial population: Each frequency jump sequence is treated as an individual, and a group of frequency jump sequence individuals are randomly generated; Step S422: Design a fitness function: Based on the stability, anti-interference ability and transmission rate of the encoded signal, design a fitness function to calculate the fitness value of an individual. Step S423: Calculate individual fitness value: Calculate the fitness value of each individual in the frequency jump sequence and evaluate its performance; Step S424: Selection operation: Select individuals in the frequency jump sequence based on their fitness values; Step S425: Crossover operation: Perform a crossover operation on the selected frequency jump sequence individuals to generate a new frequency jump sequence; Step S426: Mutation operation: Mutate the frequency jump sequence individuals after crossover to obtain the mutated frequency jump sequence individuals; Step S427: Generate a new generation population: Use selection, crossover, and mutation operations to generate a new generation population, iteratively evaluate the fitness of the new generation population, and gradually optimize the frequency jump sequence individuals to obtain the frequency jump sequence with the highest fitness.

Images