Causal Inference for Hypertension Prediction With Wearable E lectrocardiogram and P hotoplethysmogram Signals: Feasibility Study
Abstract Background Hypertension is a leading cause of cardiovascular disease and premature death worldwide, and it puts a heavy burden on the healthcare system. Therefore, it is very important to detect and evaluat...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2025-01-01
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| Series: | JMIR Cardio |
| Online Access: | https://cardio.jmir.org/2025/1/e60238 |
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| author | Ke Gon g Yifan Chen Xinyue Song Zhizhong Fu Xiaorong Ding |
| author_facet | Ke Gon g Yifan Chen Xinyue Song Zhizhong Fu Xiaorong Ding |
| author_sort | Ke Gon g |
| collection | DOAJ |
| description |
Abstract
Background
Hypertension is a leading cause of cardiovascular disease and premature death worldwide, and it puts a heavy burden on the healthcare system. Therefore,
it is
very important to detect and evaluate hypertension and related cardiovascular events
to enable early prevention, detection, and management. Hypertension can be detected in a timely manner with cardiac signals, such as through an electrocardiogram (ECG) and photoplethysmogram (PPG) ,
which can be observed via wearable sensors. Most previous studies predicted hypertension from ECG and PPG signals with extracted features that are correlated with hypertension. However, correlation is sometimes unreliable and may be affected by confounding factors
.
Objective
The aim of this study was to investigate the feasibility of predicting the risk of hypertension by exploring features that are causally related to hypertension via causal inference methods. Additionally, we paid special attention to and verified the reliability and effectiveness of causality compared to correlation.
Methods
We used a large public dataset from the Aurora Project
, which was conducted by Microsoft Research. The dataset included diverse individuals who were balanced in terms of gender, age, and the condition of hypertension, with their ECG and PPG signals simultaneously acquired with wrist
-worn wearable devices. We first extracted 205 features from the ECG and PPG signals, calculated
6 statistical metrics for these 205 features, and selected some valuable features out of the 205 features under each statistical metric. Then,
6 causal graphs of the selected features for each kind of statistical metric and hypertension were constructed with the equivalent greedy search algorithm. We further fused the
6 causal graphs into
1 causal graph and identified features that were causally related to hypertension from the causal graph
.
Finally, we
used these features to detect hypertension via machine learning algorithms.
Results
We validated the proposed method on 405 subjects.
We identified
24 causal features
that were
associated with hypertension. The causal features
could detect hypertension with an accuracy of 89%, precision of 92
% ,
and recall of 82%, which outperformed detection with correlation features (accuracy of 85%, precision of 88
% ,
and recall of 77%).
Conclusions
The results indicated that the causal inference
–based approach can potentially clarify the mechanism of hypertension detection with noninvasive signals and effectively detect hypertension. It also reveal
ed that causality can be more reliable and effective than correlation for hypertension detection and other application scenarios. |
| format | Article |
| id | doaj-art-84edb2aec94c4b4196438144ba157353 |
| institution | Kabale University |
| issn | 2561-1011 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Cardio |
| spelling | doaj-art-84edb2aec94c4b4196438144ba1573532025-01-30T15:09:35ZengJMIR PublicationsJMIR Cardio2561-10112025-01-019e60238e6023810.2196/60238 Causal Inference for Hypertension Prediction With Wearable E lectrocardiogram and P hotoplethysmogram Signals: Feasibility Study Ke Gon ghttp://orcid.org/0009-0008-8467-6062Yifan Chenhttp://orcid.org/0000-0002-2776-9456Xinyue Songhttp://orcid.org/0009-0006-9921-3410Zhizhong Fuhttp://orcid.org/0009-0007-0742-1130Xiaorong Dinghttp://orcid.org/0000-0002-3269-2852 Abstract Background Hypertension is a leading cause of cardiovascular disease and premature death worldwide, and it puts a heavy burden on the healthcare system. Therefore, it is very important to detect and evaluate hypertension and related cardiovascular events to enable early prevention, detection, and management. Hypertension can be detected in a timely manner with cardiac signals, such as through an electrocardiogram (ECG) and photoplethysmogram (PPG) , which can be observed via wearable sensors. Most previous studies predicted hypertension from ECG and PPG signals with extracted features that are correlated with hypertension. However, correlation is sometimes unreliable and may be affected by confounding factors . Objective The aim of this study was to investigate the feasibility of predicting the risk of hypertension by exploring features that are causally related to hypertension via causal inference methods. Additionally, we paid special attention to and verified the reliability and effectiveness of causality compared to correlation. Methods We used a large public dataset from the Aurora Project , which was conducted by Microsoft Research. The dataset included diverse individuals who were balanced in terms of gender, age, and the condition of hypertension, with their ECG and PPG signals simultaneously acquired with wrist -worn wearable devices. We first extracted 205 features from the ECG and PPG signals, calculated 6 statistical metrics for these 205 features, and selected some valuable features out of the 205 features under each statistical metric. Then, 6 causal graphs of the selected features for each kind of statistical metric and hypertension were constructed with the equivalent greedy search algorithm. We further fused the 6 causal graphs into 1 causal graph and identified features that were causally related to hypertension from the causal graph . Finally, we used these features to detect hypertension via machine learning algorithms. Results We validated the proposed method on 405 subjects. We identified 24 causal features that were associated with hypertension. The causal features could detect hypertension with an accuracy of 89%, precision of 92 % , and recall of 82%, which outperformed detection with correlation features (accuracy of 85%, precision of 88 % , and recall of 77%). Conclusions The results indicated that the causal inference –based approach can potentially clarify the mechanism of hypertension detection with noninvasive signals and effectively detect hypertension. It also reveal ed that causality can be more reliable and effective than correlation for hypertension detection and other application scenarios.https://cardio.jmir.org/2025/1/e60238 |
| spellingShingle | Ke Gon g Yifan Chen Xinyue Song Zhizhong Fu Xiaorong Ding Causal Inference for Hypertension Prediction With Wearable E lectrocardiogram and P hotoplethysmogram Signals: Feasibility Study JMIR Cardio |
| title |
Causal Inference for Hypertension Prediction
With Wearable E
lectrocardiogram and P
hotoplethysmogram Signals: Feasibility Study
|
| title_full |
Causal Inference for Hypertension Prediction
With Wearable E
lectrocardiogram and P
hotoplethysmogram Signals: Feasibility Study
|
| title_fullStr |
Causal Inference for Hypertension Prediction
With Wearable E
lectrocardiogram and P
hotoplethysmogram Signals: Feasibility Study
|
| title_full_unstemmed |
Causal Inference for Hypertension Prediction
With Wearable E
lectrocardiogram and P
hotoplethysmogram Signals: Feasibility Study
|
| title_short |
Causal Inference for Hypertension Prediction
With Wearable E
lectrocardiogram and P
hotoplethysmogram Signals: Feasibility Study
|
| title_sort | causal inference for hypertension prediction with wearable e lectrocardiogram and p hotoplethysmogram signals feasibility study |
| url | https://cardio.jmir.org/2025/1/e60238 |
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