Cardiopulmonary Signal Heart Rate Determination via Adaptive Processing
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Solution Overview
Problem
Accurate determination of heart rate from cardiopulmonary signals is challenging due to signal quality issues and overlapping components, with conventional systems requiring additional techniques or hardware to improve signal quality and separate heart rate signals effectively.
Innovation Solution
A processor-implemented method and system that receive cardiopulmonary signals, split them into sub-signals, filter using a bandpass filter, and apply spectrum analysis and signal processing techniques like sliding window based additive spectra, mean peak to peak time difference, and minimum variance sweep based on signal quality to determine breathing and heart rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional signal processing techniques are used to determine heart rate from cardiopulmonary signals, then the determination process is simple, but the accuracy is insufficient due to signal quality issues and overlapping components
Solution Approach 1:
The cardiopulmonary signal is segmented into multiple sub-cardiopulmonary signals based on a predefined window duration. This segmentation allows separate processing of different signal portions, enabling accurate heart rate determination from specific segments while filtering out noise and overlapping components from other segments.
Solution Approach 2:
Different signal processing techniques are selectively applied to different sub-cardiopulmonary signals based on their individual quality characteristics. The system evaluates signal quality metrics for each sub-signal and chooses the most appropriate processing method (spectrum analysis, peak-to-peak time difference, or minimum variance sweep) for each segment, optimizing accuracy for local signal conditions.
2Measurement precision
If additional techniques or hardware are used to improve signal quality and separate heart rate signals, then the accuracy improves, but the device complexity and processing time increase
Solution Approach 1:
The system performs preliminary evaluation of signal quality metrics for each sub-cardiopulmonary signal before applying the full signal processing pipeline. By pre-assessing signal characteristics, the system can quickly identify high-quality segments that require minimal processing and focus computational resources only on segments needing more sophisticated analysis, reducing overall processing time.
Solution Approach 2:
The system dynamically adjusts processing parameters based on signal quality evaluations. For high-quality signals, simpler and faster processing methods are used, while for lower-quality signals, more robust but computationally intensive techniques are applied. This adaptive parameter adjustment optimizes the balance between accuracy and processing speed.
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
The method effectively determines heart rate with high accuracy, exceeding 92% in experimental trials, and is adaptable for long-term health monitoring in various settings, including medical and fitness tracking.
Implementation Method 1
filtering, by a bandpass filter implemented via the one or more hardware processors, the pre-processed sub-cardiopulmonary signal of each sub-cardiopulmonary signal, to obtain a first filtered signal and a second filtered signal, wherein the first filtered signal having frequencies between a predetermined first range of frequencies corresponding to breathing associated with the subject being monitored, and the second filtered signal having frequencies between a predetermined second range of frequencies corresponding to heartbeats associated with the subject being monitored
Implementation Method 2
determining, via the one or more hardware processors, a breathing rate of the subject being monitored, from the first filtered signal, by using a spectrum analysis technique
Implementation Method 3
post-processing, via the one or more hardware processors, the second filtered signal to obtain a post-processed signal, wherein the post-processing comprises removing low frequency components underlying in the second filtered signal
Data Source
AI summary
The disclosure generally relates to determining a breathing rate and a heart rate from cardiopulmonary signal. Conventional systems use additional hardware to improve signal quality or different signal processing techniques to calculate the heart rate from the cardiopulmonary signal. However accurately determining the heart rate is always a continuous area of an improvement. The present methods and systems solve the problem of determining the heart rate accurately, from the cardiopulmonary signals, by determining the signal quality of the cardiopulmonary signal and the signal associated with the heart rate, comprised in the cardiopulmonary signal. A signal processing technique that best performs, out of a set of signal processing techniques is identified based on the signal quality to determine the heart rate. A long-term and an effective health monitoring of healthy as well as patient and infant subjects is achieved by the present disclosure.