AI-Enhanced Multimodal Framework for Early Diagnosis of Neuro-Cardiac Disorders

Abstract

The early and precise identification of neuro-cardiac conditions is one of the greatest clinical problems as the interactions between cardiovascular and the nervous systems are intricate. The paper introduces an AI-Enhanced Multimodal Framework, which combines electrocardiogram (ECG), electroencephalogram (EEG), and clinical textual information, to help to diagnose neuro-cardiac abnormalities in the early stages. The suggested system uses deep multimodal learning to derive discriminative representation of heterogeneous biomedical signals and textual metadata and then a fusion mechanism is used to infer inter-modal correlation with disease progression. High-level preprocessing and signal improvement algorithms are used to process noise and variability of bio signals, and contextual embeddings are generated based on clinical narratives by transformer-based language models. The experimental findings on benchmark data sets indicate that multimodal fusion model performs significantly better as compared to unimodal baselines in accuracy and sensitivity in identifying comorbid neuro-cardiac conditions. The framework offers the basis to smart, data-driven health care systems, which can produce diagnostic information in real time and in interpretable forms, which will potentially result in better patient outcomes because of proactive clinical decision support.

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