Qt Interval Qt Interval Detection Of Myocardial Ischemia Recurrence Quantification Analysis

Introduction:Myocardial ischemia is the significant feature of coronary heart disearse.Accurate and timely assessment of myocardial ischemia is of great importance in diagnosis and treatment.Holter is the most efficient monitoring measure of myocardial ischemia,and current research mainly focuses on the diagnosis of myocardial ischemia based on morphological changes and the evaluation of risk factors after myocardial infarction using heart rate variability(HRV).The objective of this study is to investigate the dynamic alternation of the influencing factors during the process of myocardial ischemia.Through developing a QT detecting algorithm, the interval time series were obtained from the ECG.Recurrence quantification analysis(RQA) was used to search for the parameters sensitive to myocardial ischemia and analyze the changes of nonlinear and nonstationary properties represented by QT variability(QTV).Based on the comparison of the alteration difference between QTV and HRV and the change in their correlation,the autonomic nervous control and body fluid environment influence change during the process of myocardial ischemia was discussed,which would provide new information and basis for the promising specific therapies on the prevention and relief of myocardial ischemia.Materials and methods:From 43 free download ECG records in Long-term ST-T (LTST) Database,170 myocardial ischemic episodes with the duration of 34 s～23 min18 s were selected.Two 5 min ECG episodes before and after each myocardial ischemic episode were selected respectively as the control ones.A compound QT detectiing algorithm was developed,in which wavelet transform was used for the detection of R peaks and P-Q junctions,and the T-wave ends were detected using a waveform-area algorithm independent of any threshold.The algorithm was validated by the records in Physionet QT Database and used to detect QT and RR interval series. The platform of RQA and cross recurrence analysis(KRQA) was established, integrating the function of ischemia information extraction,ischemia ECG signal extraction,QT and RR interval detection,RQA and KRQA input parameters setting, RQA and KRQA performance.The episodes selected from the LTST Database were processed by the platform to get analysis indexes,then Wilcoxon Sign-rank Test was used for the statistic analysis.Results:Validation of the the proposed QT detecting algorithm showed that the mean and standard deviation of location errors were-0.36 ms and 15.49 ms.66.52%of the detection errors were controlled no more than 20 ms and 92.12%of that no more than 40 ms.The RQA results revealed that QTV and HRV had the same changing trend during myocardial ischemia,the number of changed indexes with significant difference in QT interval series was 8,but only 4 with lower significance level in RR interval series.In the KRQA of QT and RR interval series,3 indexes related to the vertical and horizontal structures in recurrence plot(RP) significantly increased.Conclusions:The compound algorithm proposed in this study effectively enhanced the accuracy of QT detection and had a robust performance.With the same changing trend during myocardial ischemia,both QT interval series and RR interval series showed the decrease in complexity and variability.Compared with HRV,QTV had a more obvious reaction to ischemia.The weakened correlation between the QT interval and RR interval showed the decreased dependency of QT interval on the RR interval under the state of myocardial ischemia,indicating that the metabolic environment change during myocardial ischemia induced the increase of non-neural influence on QT intervals.Our investigation confirmed the effectiveness of short-time nonlinear analysis,providing useful information and a sound basis for the enhancement of the myocardial ischemia information exploration and utilization of Holter data.