Heart-Rate Detection Using Wavelet Thresholding and Hampel Filtering
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Solution Overview
Problem
Wearable heart-rate sensors face challenges in accurately detecting heart rates due to high noise levels and motion artifacts, especially when the electrodes slide against the skin during physical activity, leading to false detection of R-peaks in ECG signals.
Innovation Solution
A noise-reduction process that applies a lowpass filter, wavelet transform, and Hampel filtering to the heartbeat data, using wavelet coefficients and Hilbert transforms to identify R-peaks and generate accurate heart rate curves, effectively reducing noise and motion artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wearable heart-rate sensors are used during physical activity, then heart rate monitoring is enabled, but noise and motion artifacts increase causing false R-peak detection
Solution Approach 1:
The patent applies preliminary signal processing actions (lowpass filtering, wavelet transform, thresholding) to the ECG signal before R-peak detection to remove noise and motion artifacts. This preliminary cleaning of the signal ensures that subsequent detection algorithms work with purified data, resolving the contradiction between maintaining monitoring capability during exercise and ensuring detection accuracy.
Solution Approach 2:
The patent introduces intermediate processing steps (filtering, wavelet decomposition, thresholding) as mediators between the raw noisy ECG signal and the final R-peak detection. These intermediary processes act as a bridge that transforms the unreliable raw signal into a reliable processed signal suitable for accurate heart rate measurement during physical activity.
2Measurement precision
If multiple filtering and processing steps are applied to reduce noise, then detection accuracy improves, but processing complexity increases
Solution Approach 1:
The patent segments the noise reduction process into distinct sequential steps: lowpass filtering, wavelet transform, thresholding, and inverse wavelet transform. Each segment addresses a specific aspect of noise removal, allowing the system to achieve high detection accuracy through modular processing rather than a single complex operation.
Solution Approach 2:
The patent replaces simple mechanical filtering approaches with advanced digital signal processing techniques (wavelet transform, Hilbert transform). This substitution enables more effective noise removal and accurate R-peak detection while maintaining computational efficiency through algorithmic optimization rather than hardware complexity.
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 method provides robust and accurate heart rate measurements even during motion, improving the reliability of wearable heart-rate sensors by filtering out noise and accurately locating R-peaks in ECG signals.
Implementation Method 1
generating a set of filtered heartbeat data by applying a lowpass filter with a cutoff frequency to the heartbeat data
Implementation Method 2
generating a set of wavelet coefficients by applying a wavelet transform to the set of filtered heartbeat data
Implementation Method 3
generating a reduced set of wavelet coefficients by applying a threshold to the set of wavelet coefficients, the threshold being configured to reduce noise and motion artifacts
Implementation Method 4
generating an inverse wavelet signal by applying an inverse wavelet transform to the reduced set of wavelet coefficients
Implementation Method 5
determining instantaneous amplitudes of data points in the inverse wavelet signal, the instantaneous amplitudes being determined using a Hilbert transform
Implementation Method 6
generating a modified heart rate curve by applying a Hampel filter to the heart rate curve
Data Source
AI summary
A heart-rate detection system can receive heartbeat data generated by a wearable heart-rate sensor worn by a wearer. The system can then execute a noise-reduction process for reducing noise in the heartbeat data. The noise-reduction process can involve applying a lowpass filter to the heartbeat data, generating wavelet coefficients by applying a wavelet transform to the filtered heartbeat data, and generating a reduced set of wavelet coefficients by thresholding the wavelet coefficients. An inverse wavelet signal can then be generated by applying an inverse wavelet transform to the reduced set of wavelet coefficients. R-peaks can be identified by performing peak detection on the instantaneous amplitudes of the data points in the inverse wavelet signal. A heart rate curve can then be generated based on the R-peaks and modified by applying a Hampel filter. Heartbeat data can then be generated based on the modified heart rate curve for output.