Deep Neural Network ECG Cortical Arousal Detection
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Solution Overview
Problem
Current home sleep testing systems, particularly Type III sleep monitor systems, are unable to detect cortical arousals due to their lack of electroencephalogram (EEG) monitoring, leading to potential underestimation of the apnea-hypopnea index and falsely negative studies.
Innovation Solution
The development of systems and methods that utilize a pre-trained deep neural network to detect cortical arousal events from a single time-varying ECG signal, transforming it into a sequence of cortical-arousal probabilities, thereby enabling the identification of arousals without the need for EEG monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If Type III sleep monitor systems are used for home sleep testing, then the system complexity and cost are reduced, but the ability to detect cortical arousals is lost
Solution Approach 1:
The patent replaces the EEG-based mechanical/electrophysiological detection system with a deep learning-based computational system that processes ECG signals. The neural network model substitutes the need for direct brain activity monitoring by using cardiac signals as a proxy, achieving arousal detection without EEG hardware.
Solution Approach 2:
The patent introduces ECG signals as an intermediary medium to detect cortical arousals. Instead of directly monitoring brain activity with EEG, the system uses cardiac electrical activity as an indirect indicator, which is then processed through a deep neural network to infer arousal events.
2Measurement precision
If EEG monitoring is implemented to detect cortical arousals, then the measurement precision of arousal detection is improved, but the device complexity and cost increase
Solution Approach 1:
The patent makes the ECG monitor perform multiple functions: it not only detects cardiac events but also serves as a surrogate for EEG-based arousal detection. The same ECG hardware is used for both traditional cardiac monitoring and the new arousal detection function, eliminating the need for separate EEG equipment.
Solution Approach 2:
The patent changes the analytical parameters of ECG signals by applying deep learning transformations. Instead of traditional cardiac analysis, the neural network extracts temporal patterns and features from ECG waveforms that correlate with cortical arousals, effectively transforming the parameter space of the signal.
3Measurement precision
If deep neural network processing is applied to ECG signals, then the detection accuracy of cortical arousals is improved, but the computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary preprocessing steps to ECG signals before neural network processing, including noise filtering, artifact removal, and feature extraction. This preparation reduces the computational burden on the deep learning model by providing cleaner, more structured input data.
Solution Approach 2:
The patent segments the ECG signal into manageable epochs or windows for processing. The neural network analyzes discrete time segments rather than continuous streams, which reduces memory requirements and enables efficient batch processing, lowering overall computational energy consumption.
Data Source
AI summary
Systems and methods detect cortical arousal events from a single time-varying ECG signal that is obtained via single-lead ECG. A pre-trained deep neural network transforms the ECG signal into a sequence of cortical-arousal probabilities. The deep neural network includes an inception module, a residual neural network, and a long short-term memory neural network to identify structure in the ECG signal that distinguishes periods of cortical arousal from periods without cortical arousal.