Suppression of MR gradient artefacts on electrophysiological signals based on an adaptive real-time filter with LMS coefficient updates.

Electrocardiogram (ECG) acquisition is still a challenge as gradient artefacts superimposed on the electrophysiological signal can only be partially removed. The signal shape of theses artefacts can be similar to the QRS-complex, causing possible misinterpretation during patient monitoring and false triggering/gating of the MRI. For their real-time suppression, an adaptive filter is proposed. The adaptive filter is based on the noise-canceller configuration with LMS coefficient updates. The references of the noise canceller are the three gradient signals that are acquired simultaneously with the noisy ECG. Tests were done on patients, on volunteers and using an MR-safe ECG simulator. The noise canceller's performance was measured offline, simulating real-time processing by point-by-point operations. To create worst-case scenarios, clinical sequences with strong- and fast-switching gradients have been chosen. The noise-cancelling filter reduces the gradient artefacts' peak amplitudes by 80-99% after adaptation, without changing the desired ECG signal shape. The estimated reduction of total average power of the MR gradient artefacts is 62-98%. The proposed filter is capable of reducing artefacts due to strong- and fast-switching gradients in real-time applications and worst-case situations. The quality of the ECG is sufficiently high that a standard one-lead QRS-detector can be used for gating/triggering the MRI. For permanent patient monitoring, further improvements are needed.

Exercise-induced ST depression and ST/heart rate index to predict triple-vessel or left main coronary disease: a multicenter analysis.

The aim of this investigation was to determine the difference in accuracy between two frequently published noninvasive indicators of severity of coronary artery disease (exercise-induced ST segment depression and heart rate-adjusted ST depression [ST/HR index]). The study was designed as a survey of consecutive patients undergoing exercise electrocardiography and coronary angiography. There were a total of 2,270 patients without prior myocardial infarction or cardiac valvular disease referred for angiography from eight institutions in three countries; 401 of these patients had triple-vessel or left main coronary artery disease. The sensitivities of ST depression and ST/HR index in detecting triple-vessel or left main coronary artery disease were, respectively, 75% and 78% (p = 0.08) at cut point values where their specificities were equal (64%). This small increase in the accuracy of the ST/HR index was evident only at peak exercise heart rates below the median value of 132 beats/min, where the sensitivities of ST depression and ST/HR index were 73% and 76% (p = 0.03), respectively, at cut point values corresponding to a specificity of 60%. These results were consistent at all eight participating institutions. The increase in accuracy achieved by dividing exercise-induced ST depression by heart rate is small and confined exclusively to a low exercise heart rate. This lack of superiority cannot be generalized to all methods of heart rate adjustment.

Algorithm to predict triple-vessel/left main coronary artery disease in patients without myocardial infarction. An international cross validation.

Logistic regression was applied to the clinical, risk factor, and exercise data of consecutive angiographic referrals without prior myocardial infarction to determine an algorithm predicting the probability of triple-vessel/left main coronary artery disease. These data were obtained from a total of 1,074 such subjects from patient populations at four centers (Cleveland Clinic Foundation, Cleveland, Ohio; Hungarian Institute of Cardiology, Budapest, Hungary; the university hospitals, Zurich and Basel, Switzerland; and the Veterans Administration Medical Center, Long Beach, Calif.) and used to derive four separate probability algorithms. Each algorithm is based on patient data from study samples at three of the four centers and consists of 272 logistic functions, which are related to linear combinations of 13 variables (age, sex, type of chest pain, systolic blood pressure, resting electrocardiogram, serum cholesterol, fasting blood sugar, achieved exercise work load, achieved heart rate, exercise-induced angina and hypotension, heart rate-adjusted resting ST depression, and exercise ST slope). The four algorithms were cross validated by testing them on the populations not involved in their derivation. The resulting probabilities in the four test groups were then compared with the angiographic findings of triple-vessel/left main coronary artery disease. The discriminatory power of all the algorithms was fair to good (area under receiver operating characteristic curve, 0.68, 0.75, 0.82, 0.85) in the test groups. The algorithm did not significantly underestimate or overestimate disease probability except in one center (Long Beach).(ABSTRACT TRUNCATED AT 250 WORDS)

International application of a new probability algorithm for the diagnosis of coronary artery disease.

A new discriminant function model for estimating probabilities of angiographic coronary disease was tested for reliability and clinical utility in 3 patient test groups. This model, derived from the clinical and noninvasive test results of 303 patients undergoing angiography at the Cleveland Clinic in Cleveland, Ohio, was applied to a group of 425 patients undergoing angiography at the Hungarian Institute of Cardiology in Budapest, Hungary (disease prevalence 38%); 200 patients undergoing angiography at the Veterans Administration Medical Center in Long Beach, California (disease prevalence 75%); and 143 such patients from the University Hospitals in Zurich and Basel, Switzerland (disease prevalence 84%). The probabilities that resulted from the application of the Cleveland algorithm were compared with those derived by applying a Bayesian algorithm derived from published medical studies called CADENZA to the same 3 patient test groups. Both algorithms overpredicted the probability of disease at the Hungarian and American centers. Overprediction was more pronounced with the use of CADENZA (average overestimation 16 vs 10% and 11 vs 5%, p less than 0.001). In the Swiss group, the discriminant function underestimated (by 7%) and CADENZA slightly overestimated (by 2%) disease probability. Clinical utility, assessed as the percentage of patients correctly classified, was modestly superior for the new discriminant function as compared with CADENZA in the Hungarian group and similar in the American and Swiss groups. It was concluded that coronary disease probabilities derived from discriminant functions are reliable and clinically useful when applied to patients with chest pain syndromes and intermediate disease prevalence.

[The Sanz method of cardiogoniometry]. / Kardiogoniometrie nach E.Sanz.

Cardiogoniometry (CGM), an orthogonal lead system using four chest leads, was performed in a population of 120 healthy volunteers. The measurements were taken simultaneously with the conventional ECG at rest, after upright bicycle exercise at 50 watts, and immediately and 6 minutes after maximal symptom limited exercise. The results of these tests are the normal values for CGM. The normal values are located in a narrow band on the spherical surface of the coordinate system.