Sleep Biomarker Classifier for Neurodegenerative Disorder Risk Detection
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Solution Overview
Problem
Current tools for diagnosing neurodegenerative disorders (NDDs) are limited, and there is a need for early characterization of NDD phenotypes to guide appropriate interventions and delay dementia onset.
Innovation Solution
A system, method, and non-transitory computer-readable media for detecting and characterizing NDD risk and severity by acquiring physiological data during sleep, deriving sleep biomarkers, applying a classifier to output risk probabilities for NDDs, assigning risk severity, and generating a report indicating the risk severity for the subject.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If blood-based screening biomarkers are used for proteinopathies, then the full range of neurodegenerative disorders can be screened, but the biomarkers still await validation and are not routinely tested
Solution Approach 1:
The patent introduces sleep architecture parameters as an intermediary biomarker system that bridges the gap between unvalidated blood-based screening and clinically established diagnostic methods. Sleep parameters serve as a mediator that can be measured non-invasively and show promise for characterizing and monitoring neurodegeneration across the continuum from prodromal disease to symptomatic mild cognitive impairment and dementia.
2Loss of time
If sleep architecture parameters are used as biomarkers, then early detection of neurodegeneration is enabled, but the parameters require derivation from physiological data during sleep
Solution Approach 1:
The patent performs preliminary characterization of sleep architecture parameters during sleep monitoring, deriving biomarkers such as sleep spindle oscillations, slow-wave sleep patterns, and REM sleep characteristics before cognitive decline becomes apparent. This preliminary action enables early detection of neurodegenerative risk by establishing baseline sleep patterns and identifying deviations that precede clinical symptoms.
3Measurement precision
If multiple sleep biomarkers are derived and classified, then risk probability for specific NDD phenotypes is determined, but the classification system requires complex processing of physiological data
Solution Approach 1:
The patent segments the complex physiological data into distinct sleep architecture parameters including sleep spindle oscillations during NREM sleep, slow-wave sleep duration and density, REM sleep characteristics, and autonomic nervous system dysfunction markers. Each parameter is independently analyzed and then integrated through a classification system that outputs risk probabilities for specific neurodegenerative disorder phenotypes such as Alzheimer's disease, Parkinson's disease, and Lewy body dementia.
Solution Approach 2:
The patent creates a computational model that copies and simulates the diagnostic reasoning process, using machine learning algorithms to analyze sleep architecture patterns and generate risk assessments. This virtual copy of the diagnostic process enables automated classification of NDD risk based on sleep biomarkers without requiring manual interpretation of complex physiological data.
Data Source
AI summary
It has proven difficult to automate the detection of neurodegenerative disorders in patients. Thus, detection is currently still performed via visual inspection. Accordingly, embodiments automate the detection of neurodegenerative disorders by deriving sleep biomarker(s) from physiological data, acquired while a subject is sleeping, and applying a classifier to the sleep biomarker(s) to output a risk probability for each neurodegenerative disorder. Embodiments also characterize the neurodegenerative disorder(s) by assigning a risk severity based on the risk probability(ies), output by the classifier.