ECG-Based Menstrual Cycle Prediction With User Feedback
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Solution Overview
Problem
Existing menstrual cycle prediction methods using body temperature are inaccurate, leading to insufficient provision of fertile phase information and menstrual disorder prevention and alleviation.
Innovation Solution
A method utilizing electrocardiogram (ECG) variable information and user question and answer information, processed by an analysis model, to predict menstrual cycles with higher accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If body temperature is used for menstrual cycle prediction, then the method is simple to implement, but the prediction accuracy is low
Solution Approach 1:
The patent combines multiple biometric parameters (ECG variables including RR intervals, heart rate, respiration rate, ST level, SDNN, RMSSD, NN50, pNN50, SDSD) with user question and answer information to create a comprehensive prediction system. This merging of diverse data sources increases measurement precision while the integrated approach is managed through a unified analysis model that processes all inputs together.
Solution Approach 2:
The analysis model serves multiple functions simultaneously: it processes ECG data, incorporates user feedback, predicts menstrual cycle phases, and provides disorder prevention information. This multi-functionality allows the system to achieve high prediction accuracy across different aspects of menstrual health without requiring separate specialized systems for each function.
2Measurement precision
If multiple ECG variables and user information are integrated, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the ECG data into distinct variables (RR intervals, heart rate, respiration rate, ST level, SDNN, RMSSD, NN50, pNN50, SDSD) and processes them through separate analysis pathways before integrating with user information. This segmentation allows complex data to be managed systematically, with each component analyzed and weighted appropriately in the final prediction model.
Solution Approach 2:
The system transforms raw ECG data into standardized parameters and metrics (converting heartbeat intervals into RR intervals, calculating standard deviations, determining heart rate variations). This parameter transformation simplifies the data structure and enables more effective processing and integration with user question and answer information, reducing overall processing complexity while maintaining precision.
Data Source
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AI summary
According to some exemplary embodiments of the present disclosure, disclosed is a method for providing menstrual related information, which is performed by a computing device. The method may include: obtaining biometric information including a plurality of predetermined electrocardiogram variable information, the plurality of electrocardiogram variable information including time series data; obtaining user question and answer information corresponding to the plurality of electrocardiogram variable information; and generating, by an analysis model, menstrual cycle prediction information based on the biometric information and the user question and answer information.