Deep Neural Network Sleep Spindle Detection System
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Solution Overview
Problem
Current methods for detecting sleep spindles are labor-intensive, prone to inter-rater variability, and lack real-time capabilities, which is critical for closed-loop brain-machine interface applications and understanding their role in memory consolidation and pain management.
Innovation Solution
A deep neural network-based system that analyzes EEG data to detect sleep spindles in real-time by calculating a spectral power ratio and using a convolutional neural network followed by a recurrent neural network to generate a probability score, allowing for timely administration of brain stimulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection methods are used for sleep spindles, then detection accuracy can be maintained through expert analysis, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual expert analysis (mechanical/human system) with an automated deep learning system comprising convolutional neural networks and recurrent neural networks. This automated system processes EEG data to detect sleep spindles, eliminating the need for labor-intensive manual scoring while maintaining high detection accuracy through trained algorithms.
Solution Approach 2:
The deep learning system performs self-service by automatically detecting and classifying sleep spindles without requiring continuous human intervention. The model is trained on labeled data and then autonomously processes new EEG recordings, generating detection results independently while experts only need to provide initial training data.
2Productivity
If traditional offline analysis methods are used, then comprehensive data processing can be performed, but real-time detection capability is lost
Solution Approach 1:
The patent applies preliminary action by pre-training deep neural network models on large datasets of labeled EEG data containing sleep spindles. This pre-training enables the system to perform real-time detection without requiring complex processing during actual use, as the detection algorithms have already learned optimal patterns during the preliminary training phase.
Solution Approach 2:
The detection system segments the EEG signal processing into distinct computational stages: initial signal filtering, feature extraction by convolutional neural networks, temporal pattern recognition by recurrent neural networks, and final classification. This segmentation allows real-time processing by breaking down the complex analysis into manageable, computationally efficient steps.
3Productivity
If automated detection algorithms are implemented, then processing speed increases, but inter-rater variability and detection accuracy may be compromised
Solution Approach 1:
The patent employs a composite detection approach by combining multiple types of neural networks (convolutional neural networks for spatial feature extraction and recurrent neural networks for temporal pattern recognition) into a unified detection system. This composite architecture leverages the strengths of different algorithm types to achieve both high speed and high accuracy, overcoming the limitations of single-method automated detection.
Data Source
AI summary
A method for administering stimulations to a sleeping subject is provided. The method includes obtaining brain wave data generated based on brain wave activity of the subject over a predetermined time frame and determining a spectral power ratio of a spindle band to delta and theta bands of the brain wave data at a time within the predetermined time frame. The spectral power ratio and brain wave data are sent to the input of a pretrained deep neural network to generate a probability score that sleep spindles are being detected in the brain wave activity. The method may continue to obtain brain wave data and analyze the data using the pretrained deep neural network. A determination that sleep spindles are detected may be made when the probability score is above a predetermined threshold score for a predetermined threshold period of time.