OGC Color Channel PPG Sensor Diastolic Inflection Detection
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Solution Overview
Problem
Photoplethysmographic (PPG) sensors face challenges in accurately measuring diastolic inflections due to their small size and difficulty in detection, which affects the estimation of inter-beat intervals and other physiological parameters.
Innovation Solution
Computing the second derivative of the estimated BVP wave and inverting it to detect systolic and diastolic peaks, using a combination of orange, green, and cyan color channels for improved accuracy in remote PPG measurements, and applying Independent Component Analysis (ICA) to disambiguate signal and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PPG sensors measure light intensity variations directly, then the measurement process is simple, but the diastolic inflection detection accuracy is poor due to small signal magnitude
Solution Approach 1:
The patent transforms the BVP wave signal through mathematical operations (differentiation and inversion) to change its parameters. By computing the second derivative and inverting the signal, the diastolic inflection points become prominent peaks that are easier to detect accurately, resolving the contradiction between measurement precision and processing complexity
Solution Approach 2:
The patent applies preliminary signal processing steps (smoothing filter, second derivative computation, and inversion) before peak detection. These preliminary actions prepare the signal in advance to enhance the visibility of diastolic inflections, making the subsequent detection process more accurate without requiring complex real-time processing
2Reliability
If multiple color channels are used for remote PPG measurements, then the signal quality improves, but the data processing complexity increases
Solution Approach 1:
The patent combines data from multiple color channels (orange, green, and cyan) by computing their product to generate a single enhanced BVP wave signal. This merging approach improves measurement reliability by utilizing complementary information from different channels while simplifying the processing architecture compared to handling each channel separately
3Measurement precision
If the diastolic inflection is detected as a small peak in the BVP wave, then the detection method is straightforward, but the measurement precision is poor due to the small signal magnitude
Solution Approach 1:
The patent inverts the second derivative of the BVP wave signal, which transforms small inflection points into prominent peaks. This inversion technique reverses the signal characteristics so that previously undetectable features become the most prominent elements, solving the contradiction between measurement precision and detection difficulty
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 systolic and diastolic peak detection, leading to precise estimation of heart rate, heart rate variability, and breathing rate, with the OGC color channel combination outperforming traditional RGB and other combinations in trials.
Implementation Method 1
A photoplethysmographic (PPG) sensor measures variations of intensity of light. The light is reflected from, or transmitted through, skin of a human. The intensity of the light varies due to a cardiac blood volume pulse (BVP) wave.
Data Source
AI summary
In illustrative implementations of this invention, a photoplethysmographic device measures variations of light that is reflected from, or transmitted through, human skin. In some implementations, the device includes a camera that takes the measurements remotely. In others, the device touches the skin during the measurements. The device includes a camera or other light sensor, which includes at least orange, green and cyan color channels. In some cases, such as a contact device, the device includes three or more colors of active light sources, including at least orange, green and cyan light sources. A computer analyzes the sensor data, in order to estimate a cardiac blood volume pulse wave. For each cardiac pulse, a computer detects the systolic peak and diastolic inflection of the wave, by calculating a second derivative of the wave. From the estimated wave, a computer estimates heart rate, heart rate variability and respiration rate.