Increase In Number Of Measurements Research Articles

Sample size and the estimation of physiological parameters in the field

Summary Estimates of basal rate of metabolism in fruit pigeons and flying foxes, derived from the mean of individual means, were compared to the mean basal rate obtained by pooling all data into a single estimate. Pooled estimates of basal rate differ by ≤1% of the basal rates obtained from the mean of individual means in 7 of 10 species of pigeons and 4 of 11 species of flying foxes. The coefficient of variation of the mean of individual mean basal rates decreased with an increase in the mean number of measurements per individual, which, given that the number of measurements per individual was 8·4 in pigeons and 3·8 in flying foxes, partially accounts for the smaller coefficients in pigeons. The maximal absolute difference between individual mean basal rates and the mean of individual means within a species decreased with an increase in the number of measurements per individual to 8, beyond which the difference remained constant. Repeated measurements on a single individual usually gave an estimate of basal rate within 2–8% of the species’ mean when eight or more measurements were made. . The analysis of limited samples most often occurs in the study of elusive or endangered species, when the availability of individuals for study is greatly limited.

Computer classifications of electrocardiograms

Several statistical classification techniques were applied to orthogonal electrocardiographic record samples, obtained from normal subjects and patients with left ventricular hypertrophy. A simple method based on addition of QRS amplitude measurements was used as representative of ECG analysis methods in present clinical use. The procedures to be evaluated and compared consisted of vector differences with and without weight factors, and a class-separating and a class-clustering transformation. They were tested both with sets of three and eight amplitude measurements of the QRS complex by using different record samples of 100 each. Best results were obtained with weighted vector differences based on eight amplitudes (84% correct classifications). The class-separating procedure followed with 80%. The conventional method of summing amplitudes led to 67% separation. When amplitude measurements were decreased from eight to three, the differentiation of records deteriorated by 5–10%. From the results it was concluded that improvement of ECG record classification can be achieved mainly through increase in number of measurements. More complex statistical classification methods lead only to modest improvements with small numbers of measurements but to a substantial enhancement when more measurements become available. These results indicate a need for automatic means for ECG data analysis because larger numbers of ECG measurements and more efficient classification methods are not practical without access to computer facilities.