Biomarkers Derived from Neurophysiological Computational Modeling

Assessing neurological conditions often requires sophisticated, invasive methods or complex imaging studies, leading to a time-consuming and expensive process. Given the importance of early detection and the increasing prevalence of neurological conditions in society, a noninvasive, affordable, and efficient method of assessment will fill a significant unmet need. Current approaches struggle to provide real-time, dynamic assessment of neurophysiological conditions. Traditional methods that rely heavily on static symptom observation or medical imaging require several repeat evaluations and do not provide continuous monitoring. Furthermore, these techniques require professional interpretation, which could introduce subjective errors. A method that can promptly detect neurological conditions by using a noninvasive, standardized tool is desirable.

Technology Description

The proposed system and method analyze the condition of a subject on the basis of specific features extracted from speech signal.s This process incorporates a neurophysiological computational model that interprets the speech data to derive control parameters. These parameters act as biomarkers or indicators of the subject's neurophysiological condition. The model compares speech-associated features with predicted patterns and utilizes the derived error signal to update its own parameters. This innovative approach stands out because of its ability to constantly update the control parameters within the model based on the error signal it encounters in the comparison process. As each update better aligns the model predictions and actual features, it promotes more accurate biomarker extraction. Furthermore, by comparing the updated parameters with disorder-associated parameters stored in a library, it provides valuable insights into the potential presence of neurological disorders.