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.

The neurocomputational model ‘Directions into Velocities of Articulators’ (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.

Biological aging clocks offer estimations of aging and dementia, yet scalability is limited. We introduce a large-scale, cross-national speech clock derived from 2928 individuals across five Latin American countries, spanning healthy controls (HCs), mild cognitive impairment (MCI), Alzheimer’s disease (AD), and non-language/language-dominant frontotemporal dementia (nldFTD/ldFTD). Multimodal acoustic and linguistic features were trained with supervised models to estimate chronological age, generating speech age gaps (SAGs) as cross-sectional markers of deviations from chronological age, with positive values interpreted as relatively older-appearing speech profiles. SAGs differentiated diagnostic groups (HCs < patient groups, with AD < nldFTD < ldFTD). This pattern was associated with clinical/cognitive domains. SAGs correlated with phosphorylated tau (p-Tau217) in AD and social exposome in HCs and AD. Brain clocks (structural/functional/combined) were associated with SAG in AD, nldFTD, and ldFTD. Epigenetic age correlated with SAGs in HCs and AD across Hannum, Retroclock, and OMICmAge, whereas ldFTD associations were limited to Retroclock and OMICmAge. These results indicate that SAGs capture cross-sectional multilevel aging-related variation and may offer a scalable, culturally adaptable, low-cost biomarker candidate for research in underrepresented global settings.