Sleep Signal Encoding for Physiological Data Size Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing consumer sleep technologies face challenges in effectively and efficiently monitoring sleep due to the large size of data generated by physiological signals, such as EEG signals, which hinders practical implementation.
Innovation Solution
A method and system that reduce the data size of user data associated with a sleep session by encoding sleep features using optimized encoding data, resulting in a smaller data size while maintaining the quality of the information, allowing for efficient storage and analysis.
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 the data size becomes substantially large making effective and efficient monitoring difficult
Solution Approach 1:
The patent extracts only the essential sleep features from the raw physiological signals rather than storing all signal data. This selective extraction of relevant information reduces data size while maintaining monitoring effectiveness.
Solution Approach 2:
The patent segments the continuous physiological signals into discrete sleep features and characteristics. By dividing the data into meaningful segments rather than storing raw continuous signals, the system achieves compact representation.
2Loss of information
If raw physiological signal data is stored in full detail, then complete information is preserved, but storage efficiency and practical implementation become impractical
Solution Approach 1:
The patent transforms the data from raw signal form to extracted feature form, changing the parameters of representation. This transformation maintains the essential information while dramatically reducing storage requirements through optimized encoding.
3Quantity of substance
If data encoding is applied to reduce size, then storage efficiency improves, but processing complexity increases
Solution Approach 1:
The patent performs feature extraction and encoding during the data collection phase rather than requiring complex processing during storage or analysis. This preliminary action simplifies subsequent operations.
Data Source
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.