Sentence examples for classifying subjects from inspiring English sources

Classifying subjects as "responders" and "non-responders", or classifying single trials as "successful" or "not successful" conditioning based on autonomous measures has proven extremely useful, to exclude erroneous trials or subjects from further analysis (e.g. [28], [51]).

The PET-only methods were compared against MR-based SUVR quantification and evaluated in terms of correlation, average error, and performance in classifying subjects with low and high Aβ deposition.

We defined diastolic dysfunction according to the American Society of Echocardiography ASEE) 2009 guidelines [28], classifying subjects into grade 0 (normal) if e' ≥ 8 cm/s; grade I (impaired relaxation) if e' < 8 cm/s and E/A < 0.8; grade II (pseudonormal) if e' < 8 cm/s, E/A 0.8-1.5 0.8-1.5e' 9–12; and gradE/eII (restrictive) if e' < 8 cm/s, E/A > 2, DT < 160 ms, and E/e' ≥ 13 (Figure 1).

Mean AHI values were compared between groups that were defined by classifying subjects into normal and elevated symptom levels, using independent samples t-tests.

Finding 1066 significantly differentially expressed genes is a first indication that PBL transcript profiling is capable of classifying subjects defined by CAN biopsy histology.

Thus, a categorical dichotomous score was obtained by classifying subjects into groups of LA and HA, using the extreme lower and higher values of the continuous scores obtained from the factor analysis; subjects with scores within ±0.25 standard deviations from the mean were excluded from this classification.

Thus, the essential task of a motor imagery BCI is to extract the task-relevant ERD/ERS patterns from EEG signals for classifying subject's motor intentions.

For the visual analysis, we classified subjects into three groups.

Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data.

The parameters used to classify subjects in different groups are shown in Table 1.

We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data.