Evaluation of the QRS score for diagnosing coronary artery disease in women: A Finnish cardiovascular study.

BACKGROUND: Exercise electrocardiography is a widely used diagnostic modality for diagnosing coronary artery disease. This method has been used for both sexes; however, its diagnostic accuracy in women is limited. METHODS: The study analyzed 332 women participating in the Finnish Cardiovascular Study. Among 332 women, 125 with angiographically proven coronary artery disease (mean age 62.1 ± 9.5 years), 91 with a low likelihood of coronary artery disease (mean age 47.3 ± 13.5 years), and 116 without angiographically proven coronary artery disease (mean age 56.3 ± 9.9 years) were analyzed. The Q, R, S, and ST-segment changes and QRS score were determined by subtracting the Q, R, S, and ST-segment amplitudes immediately after the maximal exercise changes from their rest values (Δ). Receiver operating characteristic curve analysis was performed to evaluate the overall diagnostic performance of the parameters for predicting coronary artery disease. RESULTS: The areas under the receiver operating characteristic curve between coronary artery disease and low likelihood of coronary artery disease groups for the QRS score and ΔSTV5, ΔQaVF, and ΔRaVF were 0.75, 0.73, 0.71, and 0.71, respectively. These areas were lower (0.62, 0.57, 0.60, and 0.60, respectively) between the groups with and without angiographically proven coronary artery disease. QRS score demonstrated the highest sensitivity at 80% specificity (61.5%) and the highest specificity at 80% sensitivity (57.6%). CONCLUSIONS: This study suggests that the QRS and ST-segment depression have a moderate diagnostic ability to predict coronary artery disease in women. Q and R waves in lead aVF showed good diagnostic ability.

Independent component analysis of parameterized ECG signals.

Independent component analysis (ICA) of measured signals yields the independent sources, given certain fulfilled requirements. Properly parameterized signals provide a better view to the considered system aspects, while reducing the amount of data. It is little acknowledged that appropriately parameterized signals may be subjected to ICA, yielding independent components (ICs) displaying more clearly the investigated properties of the sources. In this paper, we propose ICA of parameterized signals, and demonstrate the concept with ICA of ST and R parameterizations of electrocardiogram (ECG) signals from ECG exercise test measurements from two coronary artery disease (CAD) patients.