Heart Rate Estimation via Frequency-Domain PPG Analysis
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Solution Overview
Problem
Existing heart rate monitoring technologies face challenges in accurately estimating heart rate due to noise from motion and other sources, leading to poor signal-to-noise ratios, especially in wearable devices that use optical signals.
Innovation Solution
The method involves obtaining a plethysmogram signal, computing a frequency-domain signal using Fourier transforms, identifying qualified features, constructing traces in a frequency-versus-time mapping, and selecting representative traces to estimate heart rate, while using accelerometer signals to distinguish motion artifacts and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If optical signals are used for heart rate monitoring in wearable devices, then ease of operation and user convenience are improved, but measurement precision deteriorates due to motion artifacts and noise
Solution Approach 1:
The patent segments the heart rate estimation problem into multiple frequency components through Fourier transform analysis. By dividing the signal into distinct frequency bands and identifying peaks corresponding to different physiological signals (heart rate, respiratory rate, motion artifacts), the system can selectively process and combine these segments to achieve accurate heart rate measurement even during motion.
Solution Approach 2:
The patent introduces accelerometer data as an intermediary signal to help distinguish motion artifacts from actual physiological signals. By comparing the frequency content of the optical signal with the accelerometer signal, the system can identify and eliminate frequency components caused by motion, thereby improving measurement precision without sacrificing the convenience of wearable optical monitoring.
2Measurement precision
If frequency domain analysis is applied to filter motion artifacts, then measurement precision is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent employs periodic Fast Fourier Transform analysis at different time epochs to track frequency components over time. By performing FFT at multiple time points and analyzing the temporal evolution of frequency peaks, the system can distinguish stable physiological signals from transient motion artifacts. This periodic analysis approach balances measurement precision with computational efficiency by processing signals in manageable time segments rather than continuously.
Solution Approach 2:
The patent creates a frequency-domain representation (power spectrum) as a copy of the time-domain optical signal. This frequency-domain copy allows the system to analyze and filter motion artifacts without directly modifying the original time-domain signal, enabling precise heart rate estimation while maintaining a relatively simple processing architecture that can be implemented on resource-constrained wearable devices.
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
This approach enhances the accuracy and reliability of heart rate estimation by filtering out motion artifacts and noise, providing a more precise measurement of heart rate even during user movement.
Implementation Method 1
heart rate may be estimated from the optical signal measured by a sensor such as a pulse oximeter using a technique called photoplethysmography (PPG)
Implementation Method 2
The ACL signal may be obtained from a sensor such as an accelerometer located on or proximate to a wearable or mobile electronic device
Data Source
AI summary
Methods, systems, computer-readable media, and apparatuses for estimating a user's heart rate using a PG signal are presented. In some implementations, the heart rate is estimated by computing a frequency-domain PG, identifying one or more features in the frequency-domain PG, selecting qualified features from the one or more features, and constructing one or more traces. In some implementations, an accelerometer signal can be used for motion cancellation to eliminate traces that are motion artifacts.