This work reports a multilead QT interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including QRS detection as well as an accurate multilead QT interval detection algorithm using support vector machines (SVMs). Two fiducial points (Qini and Tend) are estimated using the SVM algorithm on each incoming beat. This enables segmentation of the current beat for obtaining the P, QRS, and T waves. The QT interval is estimated by updating the QT interval on each lead, considering shifting techniques with respect to a valid beat template. The validation of the QT interval measurement algorithm is attained using the Physionet PTB diagnostic ECG database showing a percent error of 2.60±2.25 msec with respect to the database annotations. The usefulness of this software tool is also tested by considering the analysis of the ECG signals for a group of 60 patients acquired using our digital electrocardiograph. In this case, the validation is performed by comparing the estimated QT interval with respect to the estimation obtained using the Cardiosoft software providing a percent error of 2.49±1.99 msec.

Purpose This study aims to describe variations in acoustic and electroencephalography measures when speaking in the presence of background noise (Lombard effect) in participants with typical voice and normal hearing. Method Twenty-one participants with typical voices and normal hearing uttered simple vocal tasks in three sequential background conditions: Baseline (in quiet), Lombard (in noise), and Recovery (five minutes after removing the noise). Acoustic and electroencephalography signals were recorded in all conditions. The noise used in the Lombard condition consisted of speech-shaped noise at 80 dB SPL sent by headphones. Acoustic measure, and ERP responses were analyzed. Results During the Lombard condition, the participants increased the intensity of their voice, accompanied by an increase in CPP, and a decrease in H1-H2. The cortical response was characterized by the increased N1-P2 complex amplitude of the ERP elicited by the subject's own vocalizations in noise, The source localization showed neural activities in frontal and temporal cortical regions. Conclusions The variation in acoustic measures due to the Lombard Effect could be modulated by temporal, and cortical regions.