Study Of ECG Signal Real-time Detection For The Client Of Wearable Telemedicine System

Telemedicine system have provided an effective solution to tackle the problem of scarcity in medical resources for the society. Due to the inconvenient and uncomfortable process of measurement in system's client, which make telemedicine system cannot be widely put into practice. In this paper, a new medical device that called wearable telemedicine system is introduced. Without going to hospital, patient can take daily care service under the help of this system. Meanwhile, the ECG signal detection algorithms on the client of wearable telemedicine systems are discussed, and then a R-wave detection and P-wave detection algorithm are proposed for real-time detecting.First, the pre-processing procedure include: a smooth filter to filter 50 Hz frequency interference, wavelet transform to remove baseline drift and high frequency noise (mainly through set high-scale approximation coefficient and low-scale detail coefficient to reconstruct signal).Then this paper focus on the ECG feature extraction methods. After analyzing the principle of wavelet transform in ECG signal singular point detection, db3 wavelet is used to decompose the signal, and then self-adaptive threshold, refractory period and compensation strategies are put into practice to detect R wave on 22 and 23 wavelet coefficients. The experiments'results show that average correct detection rate of this algorithm is above 99%.Based on this algorithm, P wave can be detected by self-adaptive threshold estimation after attenuating the amplitude of QRS-T peak in order to highlight the energy of P wave. Similarly, the experiments results show that detection rate of this algorithm in some signals can reach 90%. Besides, this paper dive into automatic analysis technology of some arrhythmia cases. The indicators of bradycardia and tachycardia have been introduced.In this project the ECG signal have been processed on the client of wearable telemedicine system, in which kernel chip is TMS320VC5509DSP processor. This processor with low power and strong calculation ability has been regarded as suitable for wearable medical device. Finally, the method which used to test algorithm in CCS IDE have been elaborated. Profile tool is able to calculate the operating time from which the operation efficiency of algorithm can be obtained. Experiment results show that R-wave and P-wave detection algorithm can be operated in TMS320VC5509DSP processor efficiently.