PPG Pulse Morphology Analysis for Real-Time Physiological State Prediction
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Solution Overview
Problem
Existing methods for predicting clinical and non-clinical outcomes using heart rate variability (HRV) are inaccurate due to low sampling rates, high power consumption, and reliance on ECG or PPG, which do not consider pulse morphology, leading to increased error and limited application in detecting subtle physiological and pathological states.
Innovation Solution
An electronic method that analyzes photoplethysmography (PPG) data by normalizing and breaking down pulse cycles, applying kernel density estimates (KDE) to individual pulses, and integrating imaging data for precise prediction of physiological, pathological, and emotional states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the sampling rate is decreased to reduce power consumption, then battery life is extended, but the accuracy of HRV calculation and pulse peak detection deteriorates significantly
Solution Approach 1:
The patent segments the continuous pulse signal into individual pulse cycles, analyzing each pulse's morphology (upstroke time, downstroke time, peak amplitude, area under curve) independently. This segmentation allows accurate HRV calculation from discrete pulse events even at lower sampling rates, as each pulse contains sufficient information for analysis without requiring continuous high-rate sampling.
Solution Approach 2:
The system performs preliminary detection and identification of pulse peaks and morphological features before conducting full HRV analysis. By pre-identifying key pulse characteristics (peak timing, amplitude, shape parameters) at a lower computational burden, the system prepares data structures that enable accurate HRV calculation without requiring sustained high-power processing throughout the entire signal analysis pipeline.
2Productivity
If conventional PPG or ECG methods are used to monitor heart rate variability, then basic HRV parameters can be obtained, but pulse morphology information and subtle physiological states are missed
Solution Approach 1:
The patent adds morphological dimensions to traditional HRV analysis by extracting multiple pulse shape parameters (upstroke time, downstroke time, peak amplitude, area under curve, inflection points) in addition to timing intervals. This transforms the analysis from one-dimensional (time intervals only) to multi-dimensional (time + shape characteristics), enabling detection of subtle physiological states that manifest as morphological changes rather than just rate variations.
Solution Approach 2:
The system applies local quality analysis by examining specific portions of each pulse waveform (upstroke phase, peak region, downstroke phase) with dedicated morphological metrics. Different pulse segments are analyzed with appropriate parameters (e.g., upstroke time for arterial stiffness, dicrotic notch for vascular resistance), allowing localized physiological assessment that complements global HRV measures and improves overall monitoring capability without requiring additional sensors.
3Measurement precision
If ECG is used for accurate heart rate monitoring, then electrical activity can be precisely measured, but electrical sensors and wire connections create inconvenience and safety issues
Solution Approach 1:
The patent replaces the electrical sensing system (ECG with electrodes and wires) with an optical sensing system (PPG using light sources and photodetectors). This substitution eliminates the need for skin contact electrodes and wire connections while maintaining measurement capability through detection of blood volume changes via light absorption, thereby improving user comfort and safety without sacrificing essential monitoring functionality.
Solution Approach 2:
The system creates an optical copy of the electrical heart activity by using PPG to detect hemodynamic changes that mirror cardiac electrical events. While not measuring electrical potential directly, the PPG waveform replicates the essential temporal and morphological characteristics of cardiac cycles (systole, diastole, pulse wave propagation) through optical detection of blood flow, providing a safe wireless alternative that preserves the information needed for HRV and pulse morphology analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of predicting clinical and non-clinical outcomes by utilizing detailed pulse morphology analysis, enabling real-time detection of various health parameters and emotional states with reduced power consumption.
Implementation Method 1
The pulse reading involves acquiring an input of a photoplethysmography (PPG) sample in the form of peak(s) representing a pulse(s) on an X-axis and a Y-axis
Data Source
AI summary
The present embodiment discloses an electronic method (100) for predicting physiological states of a subject. The embodiment involves predicting the physiological states instantly based on each peak of the pulses during heart beating. The physiological states may be clinical such as heart related, stress related problems, drowsiness, and so on. In some instances, the physiological states may be non-clinical such as behavioral-anger, anxiety, and so on.