The Kalman filter based research of T-wave alternans detection algorithm
As the second largest cause of death in human beings only behind cancer, sudden cardiac death (SCD - Sudden Cardiac Death) is featured as sudden onset and difficult to rescue. Therefore, for these patients, early diagnosis, early intervention is the most effective treatment. TWA (T wave alternans) as the detection of SCD-like illness is an important indicator. How to obtain accurate data in a TWA research is focused on in recent years. This paper presents a Kalman filter-based (non-steady state) TWA detection algorithm. Firstly, we preprocess, denose and baseline drift of ECG. Secondly, using wavelet modulus extremum to detect the positions of feature point, which belong to the QRS and T waves. Further to align T wave and extracts the T wave matrix. And re-group the T wave matrix according to the strategy (in accordance with the odd and even). Kalman filter is used on two groups of T wave matrix. After filtering, we calculate the difference matrix between the two matrix above, and get the absolute value of difference matrix. Finally, we use a serious of tactics, such as sorting average moving window to get the TWA value. According to simulation result, the correlation coefficient between the TWA detection values and real values reaches 0.97, and not only can it test the value of T wave alternans, but also can identify short-term T wave alternans.
TWA Kalman Wavelet modulus maxima Sudden Cardiac Death
She LiHuang Tong Mengmeng Zhang Shi Guo BingGang
School of Information Science and Engineering Northeastern University Shenyang, China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
2226-2229
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）