Sleep Signal Encoding for Smaller Physiological Data Storage
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Solution Overview
Problem
Consumer sleep technologies face challenges due to the large size of data generated by monitoring physiological signals during sleep sessions, making effective and efficient sleep monitoring impractical.
Innovation Solution
A system and method that encode and decode brain activity signals to reduce data size by determining sleep features, generating encoding data, and storing the encoded user data, which is smaller in size than the original data, while retaining the quality of the information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physiological signals are collected during sleep sessions for monitoring, then sleep monitoring capability is enabled, but data size becomes substantially large making effective monitoring difficult
Solution Approach 1:
The patent segments the continuous physiological signal data into discrete sleep features (such as sleep stages, movement events, heart rate variations) that can be independently identified and encoded. This segmentation transforms the raw continuous data stream into discrete categorical representations, dramatically reducing data size while preserving monitoring capability.
Solution Approach 2:
The patent extracts only the essential sleep monitoring features from the complete physiological signal dataset. By identifying and extracting key sleep parameters (sleep stage classifications, arousal events, respiratory patterns) rather than storing all raw sensor data, the system achieves effective sleep monitoring with substantially reduced data storage requirements.
2Loss of information
If raw physiological data is stored to maintain data quality, then information completeness is preserved, but storage efficiency decreases
Solution Approach 1:
The patent changes the parameter representation from continuous raw signal values to discrete encoded categories. By transforming physiological measurements into standardized sleep feature classifications with predefined value ranges and encoding schemes, the system maintains all necessary information for sleep analysis while reducing storage requirements by orders of magnitude.
Solution Approach 2:
The patent creates compressed representations (copies) of the original physiological data that capture all essential sleep monitoring information. The encoded sleep features serve as efficient copies that retain complete diagnostic value while occupying minimal storage space, enabling both information completeness and storage efficiency.
3Productivity
If encoding is applied to reduce data size, then storage efficiency improves, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary encoding of physiological signals into sleep features at the point of data collection. By applying the encoding transformation immediately when data is generated rather than performing complex compression algorithms later, the system achieves efficient storage with minimal additional processing complexity. The encoding scheme is designed to be computationally simple and straightforward to implement.
Data Source
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AI summary
The present disclosure pertains to systems and methods for encoding and/or decoding brain activity signals for data reduction. In a non-limiting embodiment, first user data associated with a first sleep session of a user is received. The first user data is determined to include at least a first instance of a first sleep feature being of a first data size. A first value representing the first instance during a first temporal interval is determined. First encoding data representing the first value is determine, the first encoding data being of a second data size that is less than the first data size. Second user data is generated by encoding the first user data using the first encoding data to represent the first instance in the second user data, and the second user data is stored.