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Published on: February 14, 2017
A machine learning strategy for fast prediction of cardiac function based on peripheral pulse wave
Sirui Wang1, Dandan Wu1, Gaoyang Li2
1Graduate School of Engineering, Chiba University, Chiba, 263-8522, Japan.
Insights
Machine learning accurately predicts cardiovascular function using pulse wave data. This approach shows potential for non-invasive health monitoring and cardiovascular disease diagnosis.
Area of Science:
- Cardiovascular physiology
- Biomedical engineering
- Machine learning applications
Background:
- Pulse waves serve as crucial indicators of cardiovascular system (CVS) health.
- Predicting cardiovascular function parameters is vital for diagnosing cardiovascular diseases (CVDs).
- Machine learning (ML) offers powerful feature-abstraction for pulse wave analysis, yet remains understudied for clinical applications.
Purpose of the Study:
- To develop and validate an ML-based strategy for fast and accurate prediction of key cardiovascular function parameters.
- To assess the model's performance in distinguishing between healthy subjects and those with CVDs.
- To explore the clinical significance of pulse wave analysis for health monitoring and CVD diagnosis.
Main Methods:
- An ML model utilizing a multi-layered, fully connected network was developed.
- Two high-quality pulse wave datasets were curated: one healthy, one CVD-subject group (412 subjects total).
- The model was optimized to predict arterial compliance (AC), total peripheral resistance (TPR), and stroke volume (SV).
Main Results:
- The ML model demonstrated high accuracy in predicting TPR and SV for both healthy (85.3%, 86.9%) and CVD subjects (88.3%, 89.2%).
- Model predictions showed strong consistency with clinical measurements.
- Error analysis confirmed the model's predictive capabilities.
Conclusions:
- The developed ML strategy shows feasibility for predicting physiological and pathological CVS conditions.
- The subject groups accurately represent typical population characteristics.
- Further research with larger datasets is needed for disease-specific predictions, such as for heart failure.
Objective:
Pulse wave has been considered as a message carrier in the cardiovascular system (CVS), capable of inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Clarification and prediction of cardiovascular function by means of powerful feature-abstraction capability of machine learning method based on pulse wave is of great clinical significance in health monitoring and CVDs diagnosis, which remains poorly studied.
Methods:
Here we propose a machine learning (ML)-based strategy aiming to achieve a fast and accurate prediction of three cardiovascular function parameters based on a 412-subject database of pulse waves. We proposed and optimized an ML-based model with multi-layered, fully connected network while building up two high-quality pulse wave datasets comprising a healthy-subject group and a CVD-subject group to predict arterial compliance (AC), total peripheral resistance (TPR), and stroke volume (SV), which are essential messengers in monitoring CVS conditions.
Results:
Our ML model is validated through consistency analysis of the ML-predicted three cardiovascular function parameters with clinical measurements and is proven through error analysis to have capability of achieving a high-accurate prediction on TPR and SV for both healthy-subject group (accuracy: 85.3%, 86.9%) and CVD-subject group (accuracy: 88.3%, 89.2%).
Discussion:
The independent sample t-test proved that our subject groups could represent the typical physiological characteristics of the corresponding population. While we have more subjects in our datasets rather than previous studies after strict data screening, the proposed ML-based strategy needs to be further improved to achieve a disease-specific prediction of heart failure and other CVDs through training with larger datasets and clinical measurements.
Conclusion:
Our study points to the feasibility and potential of the pulse wave-based prediction of physiological and pathological CVS conditions in clinical application.
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