System for the diagnosis and monitoring of coronary artery disease, acute coronary syndromes, cardiomyopathy and other cardiac conditions

Cardiac electrical data are received from a patient, manipulated to determine various useful aspects of the ECG signal, and displayed and stored in a useful form using a computer. The computer monitor displays various useful information, and in particular graphically displays various permutations of reduced amplitude zones and kurtosis that increase the rapidity and accuracy of cardiac diagnoses. New criteria for reduced amplitude zones are defined that enhance the sensitivity and specificity for detecting cardiac abnormalities

Noninvasive Diagnosis of Coronary Artery Disease Using 12-Lead High-Frequency Electrocardiograms

A noninvasive, sensitive method of diagnosing certain pathological conditions of the human heart involves computational processing of digitized electrocardiographic (ECG) signals acquired from a patient at all 12 conventional ECG electrode positions. In the processing, attention is focused on low-amplitude, high-frequency components of those portions of the ECG signals known in the art as QRS complexes. The unique contribution of this method lies in the utilization of signal features and combinations of signal features from various combinations of electrode positions, not reported previously, that have been found to be helpful in diagnosing coronary artery disease and such related pathological conditions as myocardial ischemia, myocardial infarction, and congestive heart failure. The electronic hardware and software used to acquire the QRS complexes and perform some preliminary analyses of their high-frequency components were summarized in Real-Time, High-Frequency QRS Electrocardiograph (MSC- 23154), NASA Tech Briefs, Vol. 27, No. 7 (July 2003), pp. 26-28. To recapitulate, signals from standard electrocardiograph electrodes are preamplified, then digitized at a sampling rate of 1,000 Hz, then analyzed by the software that detects R waves and QRS complexes and analyzes them from several perspectives. The software includes provisions for averaging signals over multiple beats and for special-purpose nonrecursive digital filters with specific low- and high-frequency cutoffs. These filters, applied to the averaged signal, effect a band-pass operation in the frequency range from 150 to 250 Hz. The output of the bandpass filter is the desired high-frequency QRS signal. Further processing is then performed in real time to obtain the beat-to-beat root mean square (RMS) voltage amplitude of the filtered signal, certain variations of the RMS voltage, and such standard measures as the heart rate and R-R interval at any given time. A key signal feature analyzed in the present method is the presence versus the absence of reduced-amplitude zones (RAZs). In terms that must be simplified for the sake of brevity, an RAZ comprises several cycles of a high-frequency QRS signal during which the amplitude of the high-frequency oscillation in a portion of the signal is abnormally low (see figure). A given signal sample exhibiting an interval of reduced amplitude may or may not be classified as an RAZ, depending on quantitative criteria regarding peaks and troughs within the reduced-amplitude portion of the high-frequency QRS signal. This analysis is performed in all 12 leads in real time

Construction of a Resting High Fidelity ECG "SuperScore" for Management and Screening of Heart Disease

Resting conventional ECG is notoriously insensitive for detecting coronary artery disease (CAD) and only nominally useful in screening for cardiomyopathy (CM). Similarly, conventional exercise stress test ECG is both time- and labor-consuming and its accuracy in identifying CAD is suboptimal for use in population screening. We retrospectively investigated the accuracy of several advanced resting electrocardiographic (ECG) parameters, both alone and in combination, for detecting CAD and cardiomyopathy (CM)

The Effect of Habitual Smoking on VO2max

VO2max is associated with many factors, including age, gender, physical activity, and body composition. It is popularly believed that habitual smoking lowers aerobic fitness. PURPOSE: to determine the effect of habitual smoking on VO2max after controlling for age, gender, activity and BMI. METHODS: 2374 men and 375 women employed at the NASA/Johnson Space Center were measured for VO2max by indirect calorimetry (RER>=1.1), activity by the 11 point (0-10) NASA Physical Activity Status Scale (PASS), BMI and smoking pack-yrs (packs day*y of smoking). Age was recorded in years and gender was coded as M=1, W=0. Pack.y was made a categorical variable consisting of four levels as follows: Never Smoked (0), Light (1-10), Regular (11-20), Heavy (>20). Group differences were verified by ANOVA. A General Linear Models (GLM) was used to develop two models to examine the relationship of smoking behavior on VO2max. GLM #1(without smoking) determined the combined effects of age, gender, PASS and BMI on VO2max. GLM #2 (with smoking) determined the added effects of smoking (pack.y groupings) on VO2max after controlling for age, gender, PASS and BMI. Constant errors (CE) were calculated to compare the accuracy of the two models for estimating the VO2max of the smoking subgroups. RESULTS: ANOVA affirmed the mean VO2max of each pack.y grouping decreased significantly (p<0.01) as the level of smoking exposure increased. GLM #1 showed that age, gender, PASS and BMI were independently related with VO2max (R2 = 0.642, SEE = 4.90, p<0.001). The added pack.y variables in GLM #2 were statistically significant (R2 change = 0.7%, p<0.01). Post hoc analysis showed that compared to Never Smoked, the effects on VO2max from Light and Regular smoking habits were -0.83 and -0.85 ml.kg- 1.min-1 respectively (p<0.05). The effect of Heavy smoking on VO2max was -2.56 ml.kg- 1.min-1 (p<0.001). The CE s of each smoking group in GLM #2 was smaller than the CE s of the smoking group counterparts in GLM #1. CONCLUSIONS: After accounting for the effects of gender, age, PASS and BMI the effect of habitual smoking on reducing VO2max is minimal, about 0.85 ml/kg/min, until the habit exceeds 20 pack.y at which point an additional decrease of 1.71 ml/kg/min is noted. Adding pack.y data improves the accuracy of predicting the VO2max of smokers