Consciousness Indicator via Electrocardiographic Signal Analysis
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Solution Overview
Problem
Current methods for diagnosing disorders of consciousness in non-communicating patients, such as vegetative state/unresponsive wakefulness syndrome and minimally conscious state, are challenging due to the complexity of neuroimaging techniques and the sensitivity of neurophysiological signals to noise, making accurate diagnosis and prediction of clinical outcomes difficult.
Innovation Solution
A method using electrocardiographic signals, specifically analyzing heart rate and heart rate variability in response to auditory stimulation, to generate a consciousness indicator, which is noise robust and can be analyzed at the bedside, combining features from both electrocardiographic and electroencephalographic signals with machine learning classifiers for accurate assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neuroimaging techniques (functional MRI) are used to evaluate consciousness, then measurement precision is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent extracts the essential diagnostic information from complex neuroimaging by using only electrocardiographic signals, which can be obtained through simple bedside monitoring. This extraction approach maintains diagnostic accuracy while eliminating the need for complex MRI facilities and patient transport.
Solution Approach 2:
The patent replaces complex mechanical neuroimaging systems (MRI, CT) with a simpler physiological signal-based system using electrocardiographic monitoring. This substitution maintains diagnostic capability while dramatically reducing device complexity and operational burden.
2Measurement precision
If high density EEG is used to acquire neurophysiological signals, then measurement precision is improved, but device complexity increases and loss of time increases
Solution Approach 1:
The patent extracts diagnostic information from readily available electrocardiographic signals rather than requiring high-density EEG data acquisition and transfer. This extraction from simpler signals eliminates the time loss associated with transferring large volumes of EEG data while maintaining diagnostic precision.
3Measurement precision
If neurophysiological techniques are used to detect brain electric signal, then measurement precision is improved, but object-affected harmful factors increase
Solution Approach 1:
The patent substitutes direct brain electric signal detection (which is highly sensitive to electromagnetic noise) with electrocardiographic signal analysis. This substitution maintains the ability to detect cognitive processing while avoiding the harmful sensitivity to electromagnetic interference that plagues direct EEG measurements.
4Ease of operation
If bedside evaluation based on motor and oculomotor behaviors is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent uses electrocardiographic signals as an intermediary marker that reflects cognitive processing. This intermediary provides objective, quantifiable data about consciousness level that is more precise than direct behavioral observation, while still being obtainable through simple bedside monitoring.
Data Source
AI summary
Disclosed is a method for the generation of a consciousness indicator for a non-communicating subject, including the steps of generating an auditory stimulation, receiving an electrocardiographic signal of the subject obtained from a recording during the generation of the auditory stimulation, extracting at least one feature from the electrocardiographic signal and deducing a consciousness indicator from an analysis of the electrocardiographic feature.