Research On EEG Signal Transmission Technology In Medical Internet Of Things

EEG signals can be used for brain disease diagnosis,sleep research,cognitive research,and depth monitoring of anesthesia.It is an important biomedical signal.Traditional EEG signal transmission methods use short-range wireless communication technologies such as low-power Bluetooth,Zig Bee,Wi-Fi,which are difficult to meet the needs of future telemedicine communications.This paper improves the filtered orthogonal frequency division multiplexing(FOFDM)system and proposes an adaptive threshold compression method for EEG signals,which saves system transmission energy consumption and meets the needs of communication networks.Communication technology is one of the key technologies to ensure the normal operation of the medical Internet of Things(Io T).The cyclic prefix-based OFDM technology is difficult to meet the requirements of the new generation of wireless communications due to the shortcomings of high out-of-band radiation,unified parameter configuration and precise synchronization.This paper researches the FOFDM system and configures the waveform parameters reasonably for the medical Io T multi-device connection and low-latency scenarios,and verifies the system performance from the aspects of bit error rate,guard band setting and asynchronous transmission.On this basis,a FOFDM model based on polar code is proposed.The input data is sent to the modulator after polar coding.Analysis system performance under the QPSK modulation and additive white Gaussian noise channel.The results show that the proposed model is better than FOFDM in terms of error vector magnitude,peak-to-average power ratio and bit error rate.Apply the above-mentioned polar code-based FOFDM system to the EEG signal transmission and add a threshold compression module and a vector reconstruction module to solve the burden of system power consumption caused by a large number of real-time EEG data acquisition and transmission in the medical Io T.In the threshold compression module,the inherent characteristics of the EEG signal are analyzed.The generated EEG data is decomposed into multiple symbol streams and different thresholds are applied for compression,which improves the compression ratio while ensuring the application service quality.The results show that the proposed method exhibits excellent performance in terms of compression ratio,signal quality,and complexity.Compared with the wavelet transform threshold compression,the proposed method can obtain a compression ratio of 50% and the percentage root mean square difference(PRD)can be reduced by 12%.When a compression ratio of 80% is obtained,the PRD can be reduced by 4%.In addition,the method proposed in this paper can also be used for lossless compression with a compression ratio of up to 50%,which shows a significant advantage over wavelet transform in applications that require zero distortion and high-quality vital sign analysis.