RR Interval Time Series Analysis for Cardiac Rhythm Classification
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Solution Overview
Problem
Current non-invasive cardiac rhythm detection methods for ambulatory outpatients are limited by the need for obtrusive equipment and struggle to accurately distinguish between sinus rhythm and atrial fibrillation, especially when sinus rhythm has frequent ectopy, due to noisy and distorted waveform data.
Innovation Solution
A system that uses multivariable algorithms to analyze RR interval time series data, incorporating entropy, local dynamics, and detrended fluctuation analysis to classify cardiac rhythms independently of waveform analysis, providing a more accurate differentiation between normal sinus rhythm, atrial fibrillation, and ventricular ectopy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-invasive devices use skin electrodes and digital recording devices for cardiac rhythm detection, then measurement precision is improved, but device complexity and obtrusiveness increase
Solution Approach 1:
The patent extracts the essential rhythmic information from the complex ECG waveform data and focuses analysis solely on the RR interval time series. By taking out only the necessary temporal intervals between beats, the system eliminates the need for complex waveform analysis and skin electrode-based ECG recording devices, achieving accurate rhythm detection through simplified RR interval monitoring alone
Solution Approach 2:
The patent creates a simplified copy of the cardiac rhythm information in the form of RR interval time series, which captures the essential rhythmic patterns without requiring the full ECG waveform. This copy allows for accurate rhythm classification while avoiding the complexity of complete waveform recording and analysis
2Measurement precision
If high fidelity monitoring with skin electrodes is used, then measurement precision is improved, but the monitoring duration is limited due to obtrusiveness
Solution Approach 1:
The patent extracts only the essential RR interval information from the complete ECG recording, eliminating the need for continuous skin electrode monitoring. This extraction allows for long-term ambulatory monitoring without the obtrusiveness and discomfort of continuous ECG leads, enabling extended monitoring durations while maintaining detection accuracy
Solution Approach 2:
The patent replaces the complex, obtrusive ECG monitoring system with a simpler RR interval-based approach that can be implemented with minimal equipment. This simplified system is less invasive and more comfortable for patients, allowing for longer monitoring periods without the constraints of high-fidelity ECG equipment
3Device complexity
If RR interval time series analysis is used, then device complexity is reduced, but measurement precision deteriorates due to inability to distinguish sinus rhythm with frequent ectopy from atrial fibrillation
Solution Approach 1:
The patent transforms the one-dimensional RR interval time series into multi-dimensional feature space by calculating numerous statistical and dynamical characteristics (skewness, kurtosis, entropy, fractal dimensions, etc.). This dimensional expansion creates sufficient discriminative power to distinguish between sinus rhythm with frequent ectopy and atrial fibrillation, resolving the limitation of simple unidimensional analysis
Solution Approach 2:
The patent combines multiple different types of analytical features (statistical moments, entropy measures, fractal analysis, local dynamics) into a composite classification system. This composite approach integrates the strengths of various analysis methods to achieve accurate rhythm differentiation while maintaining the simplicity of RR interval-based monitoring
4Measurement precision
If multivariable algorithms with multiple parameters are used, then measurement precision is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent performs all complex computational analyses and feature extractions in advance during the data processing stage, transforming raw RR interval data into standardized multi-dimensional features before classification. This preliminary action consolidates the computational complexity into a one-time processing step, allowing the actual rhythm classification to be performed efficiently with the prepared feature set
Data Source
AI summary
A system for classifying cardiac rhythms is disclosed. The system includes one or more processors, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors. The program instructions include first program instructions times between heartbeats. The program instructions further comprise second program instructions to segment the time series into a plurality of segments. The program instructions further comprise third program instructions to calculate a plurality of parameters corresponding to each of the 30-second segments. The program instructions further comprise fourth program instructions to analyze the obtained data and the calculated parameters using a plurality of multivariable algorithms for rhythm classification. The program instructions further comprise fifth program instructions to synthesize the results of the plurality of multivariable algorithms to formulate a single rhythm classification.