Limb Ballistocardiography Signal Processing for Non-Invasive Cardiovascular Parameter Estimation
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Solution Overview
Problem
Current methods for measuring clinically significant cardiovascular parameters are invasive, costly, and inconvenient, necessitating the development of non-invasive and user-friendly techniques for effective cardiovascular disease prevention and treatment.
Innovation Solution
The use of a ballistocardiogram (BCG) signal, processed using a wearable armband accelerometer and a strain gauge weighing scale, to estimate cardiovascular parameters through signal transformation and machine learning analysis, enabling the derivation of parameters like diastolic and systolic pressures, stroke volume, cardiac output, and total peripheral resistance without invasive procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive arterial catheterization is used to measure arterial BP waveform, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent uses an intermediary approach by measuring body movement (ballistocardiogram) as a mediator to infer cardiovascular parameters indirectly. Instead of directly measuring arterial BP through invasive catheterization, the system captures BCG signals from body motion and uses machine learning models to estimate cardiovascular parameters, thus avoiding the invasive procedure while obtaining clinically relevant information
Solution Approach 2:
The patent replaces the mechanical invasive measurement system (arterial catheterization) with a non-invasive sensor-based system. Accelerometers and other sensors detect body movements and physiological signals, which are then processed through machine learning algorithms to substitute for traditional mechanical measurement methods, eliminating the need for invasive procedures
2Ease of operation
If non-invasive techniques like volume clamping or applanation tonometry are used, then ease of operation is improved, but device complexity and cost increase
Solution Approach 1:
The system employs self-service principles by using widely available consumer electronics (smartphones, tablets, wearables) that users already possess. These devices contain accelerometers and sensors that can capture BCG signals without requiring specialized medical equipment. The machine learning models are pre-trained and automatically process the signals, eliminating the need for trained operators to perform complex measurements
Solution Approach 2:
The patent applies universality by using multi-functional consumer devices that serve both as motion sensors and as computational platforms. The same smartphone or wearable device that users employ for general purposes also functions as a cardiovascular monitoring tool, eliminating the need for dedicated complex medical equipment and making the system accessible to general populations
3Measurement precision
If traditional whole-body BCG measurement is used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent segments the measurement system from the traditional whole-body approach. Instead of requiring whole-body BCG measurement setups, the system uses limb-mounted sensors (accelerometers on arms or legs) to capture localized BCG signals. The machine learning models are trained to infer cardiovascular parameters from these segmented, localized measurements, making the system more convenient while maintaining precision
Solution Approach 2:
The patent transitions from the traditional whole-body spatial dimension to a localized limb-based measurement dimension. By placing sensors on extremities rather than requiring whole-body instrumentation, the system changes the measurement dimension from global to local, thereby improving ease of operation while the machine learning algorithms ensure that the localized signals contain sufficient information for accurate parameter estimation
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 allows for accurate and convenient estimation of cardiovascular parameters using limb BCG signals, achieving high correlation with traditional whole-body BCG measurements, thus providing a non-invasive and cost-effective method for monitoring cardiovascular health.
Implementation Method 1
the use of a ballistocardiogram (BCG) signal, processed using a wearable armband accelerometer
Implementation Method 2
processed using a wearable armband accelerometer and a strain gauge weighing scale
Data Source
AI summary
Aspects of the disclosure relate to estimation of cardiovascular parameters based on a ballistocardiogram signal. In one example, an apparatus for estimating cardiovascular parameters includes a BCG sensor for producing a BCG signal of a user, a processor, a display, and a memory communicatively coupled to the processor. The processor and the memory are configured to transform the BCG signal to a synthetic whole-body BCG signal by integrating the BCG signal in time twice and zero-phase filtering the BCG signal, estimate the cardiovascular parameters based on the synthetic whole-body BCG signal, and display the cardiovascular parameters to the display. Other aspects, embodiments, and features are also claimed and described.