Automated Sleep Score Analysis System for Diagnostic Accuracy
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Solution Overview
Problem
The manual scoring of sleep disorders is time-consuming, labor-intensive, and prone to variability and bias, requiring specialized training and expertise that may not be available in all clinical settings.
Innovation Solution
A computer-implemented system that includes a polysomnography recording device, a hardware processor, and a memory with multiple modules for automatic sleep score analysis, quality control, and authentication, enabling automated scoring and reducing the need for specialized training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual scoring of sleep data is performed by trained physicians, then diagnostic accuracy is improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent introduces an automated scoring system as an intermediary between raw sleep data and final diagnosis. This system processes sleep data through multiple analytical blocks (scoring blocks, analysis blocks) that apply standardized criteria, producing preliminary scores that are then reviewed by physicians. This intermediary automation handles the time-consuming manual scoring while maintaining accuracy through quality control mechanisms.
Solution Approach 2:
The scoring process is divided into discrete analytical blocks, each handling specific aspects of sleep data analysis. These modular blocks can be independently configured and executed, allowing systematic processing of large datasets without requiring continuous manual intervention throughout the entire scoring process.
2Reliability
If manual scoring by specialized technicians is used, then scoring reliability is improved, but device complexity and training requirements increase
Solution Approach 1:
The system performs self-service through automated algorithms that apply scoring criteria without requiring specialized human expertise for the actual scoring operation. The automated blocks independently analyze sleep data, apply AASM criteria, and generate scores, eliminating the need for extensive technician training while maintaining consistent reliability across different users.
Solution Approach 2:
The automated scoring system provides universal applicability across different clinical settings, home sleep studies, and laboratory environments. The same software platform and analytical blocks can process various sleep study types (level-1, level-2, level-3) and multiple data formats, reducing the need for specialized equipment or highly trained personnel in each setting.
3Loss of information
If comprehensive multi-channel sleep data is recorded, then diagnostic completeness is improved, but data processing complexity increases
Solution Approach 1:
The system segments the complex multi-channel sleep data into distinct analytical components, with each block focusing on specific parameters (respiratory events, cardiac events, sleep stages). This segmentation allows comprehensive analysis of all channels while managing complexity through modular, organized processing of each data type separately.
Data Source
AI summary
A computer-implemented system for diagnosing sleep disorders is disclosed. The computer-implemented system includes a polysomnography recording device for interfacing with a patient to record a sleep data, a sleep score analysis module configured as a series of analytical blocks for automatically processing a data format, an evaluation module reviews and edits the sleep score for quality control by a physician, a scanning module scans the record of the sleep score within a repository, a montage specification module stores the plurality of montage specifications, a tokenization module grants a pre-determined a plurality of distributed credits in at least one of the analytical blocks, allows a key-based authentication for offline setting, sign the license via an asymmetric key cryptography, a container format module handles physiological sleep data, and includes a signature, a header, and a payload, an authentication module protects a plurality of machine learning models by using a symmetric encryption method.