Process and method for novel biomarker discovery using electromagnetic beta signal waveforms for prediction and diagnosis of neurological disorders and brain states

Dissecting beta waveforms using convolutional dictionary learning across sensory perception, aging, and disease

OVERVIEW

This technology is a system and method for analyzing brain wave patterns, specifically beta waves (13-30 Hz), to identify biomarkers that can predict neurological conditions and brain states. The technology leverages advanced signal processing and machine learning techniques to detect and analyze specific features within beta waveforms captured through MEG/EEG recordings. The system focuses on examining time-domain characteristics of these waveforms across multiple scenarios including tactile detection, aging, and conditions like Alzheimer's and Parkinson's disease. This comprehensive approach to brain wave analysis represents a significant step forward in neurological diagnostics.

MARKET OPPORTUNITY

The technology addresses several critical needs in the healthcare market, particularly given the growing aging population and increasing prevalence of neurodegenerative diseases. There is a pressing need for early detection and monitoring of conditions like Alzheimer's and Parkinson's, yet current tools for accurate prediction and diagnosis of neurological disorders remain limited. Globally, the Alzheimer’s Disease Diagnostic Market was estimated to be $7.54 billion in 2023 and was expected to grow from 2024 to 2030 at a CAGR of 11.4%. Parkinson’s diagnostics and treatment market was $6.32 billion in 2023 and is projected to grow at 8.9% CAGR between 2024 and 2032. The system has potential applications across multiple domains, from clinical diagnostics and disease monitoring to personalized therapy development. It could prove valuable in research into aging-related cognitive decline, assessment of sensory processing and cognitive function, and even drug development and clinical trials. The breadth of these applications suggests a substantial market opportunity across both clinical and research settings.

INNOVATION & MEANINGFUL ADVANTAGES

The technology introduces several groundbreaking innovations while offering meaningful advantages over existing approaches. At its core, the technical innovation centers on the novel use of convolutional dictionary learning to analyze beta waveforms, complemented by advanced feature extraction techniques that capture previously undetected waveform characteristics. The system integrates multiple analysis methods, including machine learning and statistical analysis, while maintaining real-time processing capabilities for immediate clinical feedback. These innovations translate into practical advantages: the approach is non-invasive, using standard MEG/EEG equipment, while offering more precise characterization of brain states and conditions. It has the potential to detect neurological disorders early and to track disease progression and treatment effectiveness. The system identifies stereotypical waveform features that persist across subjects and detects systematic alterations in brain circuit mechanisms, providing detailed analysis of peak/trough timing and amplitude. This enables sophisticated comparison across different clinical and functional states while generating empirical distributions for statistical analysis. Perhaps most importantly, it creates standardized measurements for clinical assessment, representing a significant advance in neurological diagnostics and monitoring, with the potential to transform our understanding of brain function and disease progression through detailed analysis of beta wave patterns.

Collaboration Opportunity: We are interested in exploring research collaborations and licensing opportunities.

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