Detection of QT prolongation through approximation of the T wave on Gaussian mixture modeling.

BACKGROUND: To establish a simple and accurate method for the automated identification of the end of a T wave, we approximated electrocardiograph (ECG) traces using a Gaussian mixture model in conjunction with a split-and-merge expectation-maximization algorithm. METHODS AND RESULTS: A total of 286 ECG traces of heart beats of 50 healthy men were used as control data and ECGs from 15 subjects recorded before and after 400mg oral moxifloxacin as positive controls. An experienced cardiologist determined the reference points by visual inspection of the original ECGs. The primary estimated point for the end of the T wave was selected as the point 2 ms before the point at which the gradient of the approximated wave was not steeper than the common threshold value. This point was then adjusted by applying modification rules proposed by an experienced cardiologist. The absolute value of the average interval between the resulting final estimated point and the manually selected reference point was 1.8±7.7 ms for the control data. After treatment with moxifloxacin, the average QT interval, corrected by Bazett's formula, showed a 17.2±27.1 ms prolongation with a lower bound of the 95% confidence interval of 4.9 ms. CONCLUSIONS: When the modification rules were applied, the accuracy of QT measurement was improved, and the present system was capable of detecting QT prolongation correctly.

Oxyhemoglobin changes during cognitive rehabilitation after traumatic brain injury using near infrared spectroscopy.

To investigate cerebral reactions to cognitive rehabilitation tasks, oxyhemoglobin changes were compared in 9 patients with cognitive impairments after traumatic brain injury (TBI) and 47 healthy controls using functional near infrared spectroscopy (fNIRS) during nine cognitive rehabilitation tasks employed at Nagoya City Rehabilitation Center. Forty-seven measurement channels were placed on the frontal to temporal cortices, and organized into seven channel regions. Oxyhemoglobin changes were normalized based on the mean oxyhemoglobin value at the resting state, and integrated throughout a task. Statistical analyses of the differences between the TBI patients and controls were performed with the two-sided Mann-Whitney U test. Oxyhemoglobin changes were high for both controls and TBI patients in the lateral frontal regions. Oxyhemoglobin changes in TBI patients tended to be higher than controls in the medial frontal regions for most training tasks, and significant differences (p < 0.05) were seen for two tasks in the medial frontal regions. Different regions were activated during the tasks in TBI patients compared to controls. fNIRS measurement is useful in the evaluation of changes of neuronal activities during rehabilitation tasks in TBI patients.

Predictive model for assessing cognitive impairment by quantitative electroencephalography.

OBJECTIVE: To assess the utility of quantitative electroencephalographic analysis as an indicator of cognitive impairment, we examined the correlation between Mini-Mental State Examination (MMSE) scores and quantitative electroencephalographic (QEEG) power values in elderly patients and constructed a regression model to predict MMSE scores. BACKGROUND: Because of the growing number of elderly individuals with cognitive deficits, there is an increasing need for simple and objective methods with which to evaluate cognitive function. Although QEEG is reportedly a useful method for this purpose, few researchers have constructed a QEEG-based model for predicting the degree of cognitive impairment in clinical settings. METHOD: We evaluated brain function using QEEG in 44 elderly patients with memory complaints and compared the results with their MMSE scores. RESULTS: In the correlation analysis, no significant correlation was found between MMSE scores and QEEG power values. However, a regression model created using relative QEEG and gender for predicting MMSE scores had an adjusted R2 of 0.471. CONCLUSIONS: This finding suggests that QEEG analysis may be a useful indicator of cognitive decline in patients with memory complaints.