Hemorrhagic risk prediction in coronary artery disease patients based on photoplethysmography and machine learning
Zhengling He1,2, Huajun Zhang3, Xianxiang Chen1,2
1State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
Insights
Photoplethysmography (PPG) and machine learning can assess hemorrhagic risk in coronary artery disease (CAD) patients. This novel approach offers continuous, dynamic risk evaluation for better antithrombotic treatment adjustments.
Area of Science:
- Biomedical Engineering
- Cardiology
- Artificial Intelligence
Background:
- Hemorrhagic events are a primary concern in antithrombosis therapy for coronary artery disease (CAD) patients.
- Current risk assessment methods may lack dynamic and continuous feedback capabilities.
Purpose of the Study:
- To investigate the efficacy of photoplethysmography (PPG) combined with machine learning (ML) for assessing hemorrhagic risk in CAD patients.
- To develop and compare ML models for predicting hemorrhagic events using PPG signals.
Main Methods:
- Collected PPG data from 102 CAD patients with 9933 records using a portable device and Android app.
- Extracted 30 features from PPG signals (time-domain, frequency-domain, wavelet packet decomposition).
- Developed and evaluated logistic regression, support vector regression, random forest, and XGBoost models for hemorrhagic risk prediction.
Main Results:
- 10 PPG-derived features showed statistical significance (p < 0.01) in differentiating hemorrhagic risk.
- The XGBoost model achieved the highest performance with a mean AUC of 0.762 ± 0.024.
- The XGBoost model demonstrated sensitivity of 0.679 ± 0.051 and specificity of 0.714 ± 0.014.
Conclusions:
- A PPG-based data acquisition system and ML model show promise for evaluating hemorrhagic risk in CAD patients.
- This method provides quantitative, dynamic, and continuous risk prediction, unlike traditional scores like HAS-BLED.
- The approach offers timely feedback for adjusting antithrombotic treatment plans, improving patient management.
Abstract:
Hemorrhagic events are the main focus of attention during antithrombosis therapy in patients with coronary artery disease (CAD). This study aims to investigate the potential of using photoplethysmography (PPG) and machine learning techniques to assess hemorrhagic risk in patients with CAD. A total of 1638 patients with CAD were enrolled from January 2018 to October 2019, among which 114 patients were observed to have at least one positive event. Importantly, 102 patients with 9933 records were finally retained for analysis in this study. Participants were required to collect data using the portable PPG acquisition device and the specially designed Android APP. The data was collected and uploaded to a remote server. Based on collected PPG signals, we extracted features in a total of 30 dimensions from time-domain, frequency-domain, and wavelet packet decomposition. Logistic regression, support vector regression, random forest, and XGBoost regression models were established to achieve hemorrhagic risk evaluation, and then, their performances were compared. In total, 10 features extracted from PPG showed statistical significance (p < 0.01) between negative and positive groups. The newly established XGBoost model performed best in the hemorrhagic risk evaluation experiment, wherein the mean area under the curve (AUC) with tenfold cross-validation was 0.762 ± 0.024 and the sensitivity and specificity were 0.679 ± 0.051 and 0.714 ± 0.014, respectively. We established a data acquisition system for PPG signal collection, and demonstrated that a set of features extracted from PPG and the proposed machine learning model are promising in the evaluation of hemorrhagic risk among patients with CAD. In comparison with the traditional HAS-BLED score, the proposed method can obtain the quantitative risk prediction probability from a single PPG record, which has the advantages of dynamics and continuity, and can provide timely feedback for doctors' antithrombotic treatment, which is of great significance for doctors to quickly determine the effectiveness of the treatment and adjust the timely treatment plans accordingly.
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