ABSTRACT
Electrocardiogram (ECG) is a convenient, economic, and non-invasive detecting tool in myocardial ischemia (MI). Its clinical appearance is mainly exhibited by ST-T complex change. MI events are usually instantaneous and asymptomatic in some cases, which cannot be forecasted to have a precautionary measure in time by doctors. The automatic detection of MI by computer and a cued warning of danger in real time play an important role in diagnosing heart disease. With the help of the medical staff, some quantitative approbatory indicators, such as ST-segment deviation, the amplitude of T-wave peak and the rate of ST and heart rate (HR), were combined to judge MI using fuzzy reasoning. After MIT-BIH database and the long-term ST database (LTST) verification, sensitivity and positive predictive values reached 75% and 78% respectively, and specificity and negative predictive values were 85% and 87% respectively. In addition, the proposed method was close to human way of thinking and understanding, and easy to apply in clinical detection and engineering fields.
Subject(s)
Humans , Electrocardiography , Fuzzy Logic , Myocardial Ischemia , Diagnosis , Signal Processing, Computer-AssistedABSTRACT
Objective ST-T complex change, which represents the ventricle repolarization phase, is the main clinical indicator in detecting myocardial ischemia (MI) based on electrocardiogram (ECG) signals.However, its feature point location is not accurate due to interferences. In this paper, a new approach about myocardial ischemia analysis was proposed based on QRS complex. Methods QRS complex, representing the ventricle depolarization process, was used to analyze myocardial ischemia, and some parameters were extracted synthetically in time domain. Then they were used for statistical analysis of myocardial ischemia states and non-myocardial ischemia states. Results Five parameters had significant differences after verification of Non-MI signals in MIT-BIH database and MI signals in long-term ST database (LTST) and they were: QRS upward and downward slopes, transient heart rate, R angle and Q angle in a triangle QRS. Conclusion Five parameters extracted from QRS complex had significant differences. The proposed method provides an important basis for myocardial ischemia detection.
ABSTRACT
ST-segment is the main clinical appearance in myocardial ischemia detection based on electrocardiogram (ECG) signals. However, it is highly sensitive to interferences (baseline wandering, postural changes, electrode interference, etc.), which cause the feature points of ECG ST-segment to be difficult to detect accurately. Currently, the common detection methods of ST-segment are: R+x and J+x, but they are affected badly by T-wave morphological variability and J point location. For these reasons, firstly we proposed a convenient and accurate approach for T-wave onset in this paper. It did not need to locate T-wave peak and was robust to baseline wandering and T-wave morphology. Secondly, we proposed a squeeze approach for ST-segment detection based on R-wave peak and T-wave onset. After the Long-Term ST database (LTST) verification, the proposed method has shown a good timeliness and robustness, and the accuracy of ST-segment detection has reached above 92%.