PPG Signal De-corruption via Multi-stage Anomaly Detection
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Solution Overview
Problem
Current methods for removing corruption from photoplethysmogram (PPG) signals, particularly in mobile devices, are inadequate due to high false alarms and false negatives, and existing techniques are computationally heavy, limiting their applicability in real-time cardiac health monitoring.
Innovation Solution
A system comprising an image capturing device coupled with a mobile communication device, including an extrema elimination module, segmentation module, inconsistency identification module, anomaly detection module, and anomaly analytics module, using dynamic time warping and multi-level cluster-based anomaly detection to identify and remove corruption from PPG signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computationally heavy machine learning algorithms and multivariate voting threshold mechanism are used for PPG signal denoising, then measurement precision is improved, but device complexity and processing time increase making it unsuitable for real-time smartphone applications
Solution Approach 1:
The patent replaces complex, resource-intensive machine learning algorithms with lightweight, computationally efficient signal processing techniques that can be executed on mobile devices with limited processing power and battery capacity, achieving real-time performance without sacrificing denoising effectiveness
Solution Approach 2:
The patent implements adaptive signal processing that dynamically adjusts processing parameters based on real-time signal characteristics and quality metrics, enabling the system to maintain high measurement precision while optimizing computational resource usage for real-time operation on smartphones
2Device complexity
If single-stage error detection is used for PPG signal corruption detection, then device complexity is reduced, but measurement precision deteriorates due to high false negatives
Solution Approach 1:
The patent divides the PPG signal processing into multiple sequential stages: initial quality assessment, intermediate anomaly detection, and final corruption verification. This multi-stage approach systematically identifies and eliminates different types of corruptions while maintaining computational efficiency and reducing false negatives compared to single-stage methods
Solution Approach 2:
The patent introduces intermediate quality metrics and threshold-based filtering mechanisms between the raw signal and final corruption detection, serving as mediators that refine the detection process and improve accuracy without requiring overly complex algorithms
3Measurement precision
If high false alarm rates occur due to noise presence, then measurement precision deteriorates, but alarm fatigue increases affecting clinical utility
Solution Approach 1:
The patent implements feedback mechanisms where detected anomalies and quality metrics are continuously monitored and used to adjust detection thresholds and processing parameters in real-time, reducing false alarms while maintaining sensitivity to true corruptions and preserving clinical utility
Solution Approach 2:
The patent dynamically changes detection parameters such as thresholds, window sizes, and processing gains based on signal characteristics and environmental conditions, optimizing the balance between false alarm reduction and maintaining reliable detection of actual corruptions for clinical decision-making
Data Source
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AI summary
A method and system for removing corruption in photoplethysmogram (PPG) signals for monitoring cardiac health of patients is provided. The method is performed by extracting photoplethysmogram signals from the patient, detecting and eliminating corruption caused by larger and transient disturbances in the extracted photoplethysmogram signals, segmenting photoplethysmogram signals post detection and elimination of corruption caused by larger and transient disturbances, identifying of inconsistent segments from the segmented photoplethysmogram signals, detecting anomalies from the identified inconsistent segments of the photoplethysmogram signals, analysing the detected anomalies of the photoplethysmogram signals and identifying photoplethysmogram signal segments corrupted by smaller and prolonged disturbances.