R-peak Adaptive Recognition Of ECG Signals And Remote Monitoring Implementation

The ECG signal is a direct response to the state of the human heart and contains important physiological information.With advances in technology,the treatment of cardiovascular disease is becoming increasingly diverse,but the analysis and interpretation of ECG remains an important basis for diagnosis and treatment.ECG waveforms are complex and cannot be directly interpreted by the general public,and long-term clinical monitoring can be a significant burden on both patients and healthcare professionals.Based on the above problems,this paper investigates the method of ECG signal recognition,proposes an R-peak adaptive recognition algorithm,and designs an ECG remote monitoring system to realize the monitoring and recognition of ECG signals.The main research work of this paper is as follows:For the problem that the signal is easily disturbed by noise in the acquisition process,a3 rd order Butterworth bandpass filter is designed to remove the baseline drift with low frequency characteristics and the industrial frequency interference noise;using the wavelet transform method,the ECG signal is decomposed in 4 layers and then soft threshold denoising is applied to remove the myoelectric interference with high frequency and random characteristics.Based on the study of R-peak signal characteristics,an adaptive differential thresholding algorithm that can accurately detect R-peak signals is proposed.The algorithm is designed with a dual update mechanism of signal threshold and signal cache value,which enables the threshold value to change flexibly and the R-peak detection to be more accurate.At the same time,a backtracking mechanism is set up to reduce the occurrence of missed and false detection problems and to improve the sensitivity and applicability of the algorithm.The R-peak features identified are used as a benchmark to localise QRS wave clusters and to complete the calculation of heart rate and heart rate anisotropy.The ECG arrhythmia classification algorithm was designed to achieve abnormal rhythm classification using the acquired feature logic for determination.To realise remote monitoring of ECG signals,the design of an ECG remote monitoring system was completed.The system uses the STM32G070 CB microprocessor to realise the control of the functions of each part of the circuit and the processing of ECG data.The 5G communication circuit and the front-end acquisition circuit based on ADS1292 were designed to realize the 2-channel high-speed acquisition of ECG data.The 5G communication circuit uploads the data to the cloud platform through the MQTT protocol.The 5G communication circuit uploads the data to the cloud platform via the MQTT protocol.The monitoring end uses visual studio for front-end design and reads the data results sent from the cloud server to achieve remote monitoring of ECG waveforms.The experimental results show that the built ECG monitoring system achieves waveform display and heart rhythm analysis of ECG signals.The system achieved an accuracy rate of 98.15% for R-peak signal recognition and 99.10% for rhythm measurement.It was able to complete the classification of abnormal heart rhythms with a combined accuracy of 96.33% for the classification algorithm.