ArticleComputational and mathematical methods in medicine2019
Multiscale Fine-Grained Heart Rate Variability Analysis for Recognizing the Severity of Hypertension.
Article in Computational and mathematical methods in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated Detection of Hypertension Using Physiological Signals: A Review.International journal of environmental research and public health · 2021Pooled it
- Assessment of Hypertension Using Clinical Electrocardiogram Features: A First-Ever Review.Frontiers in medicine · 2020Pooled it
- A Deep Convolution Method for Hypertension Detection from Ballistocardiogram Signals with Heat-Map-Guided Data Augmentation.Bioengineering (Basel, Switzerland) · 2025Article
- Non-invasive enhanced hypertension detection through ballistocardiograph signals with Mamba model.PeerJ. Computer science · 2025Article
- Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals.International journal of environmental research and public health · 2022Article
- Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank.International journal of environmental research and public health · 2019Article
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Authors and funding
6 authors.
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Abstract
Hypertension is a common and chronic disease and causes severe damage to patients' health. Blood pressure of a human being is controlled by the autonomic nervous system. Heart rate variability (HRV) is an impact of the autonomic nervous system and an indicator of the balance of the cardiac sympathetic nerve and vagus nerve. HRV is a good method to recognize the severity of hypertension due to the specificity for prediction. In this paper, we proposed a novel fine-grained HRV analysis method to enhance the precision of recognition. In order to analyze the HRV of the patient, we segment the overnight electrocardiogram (ECG) into various scales. 18 HRV multidimensional features in the time, frequency, and nonlinear domain are extracted, and then the temporal pyramid pooling method is designed to reduce feature dimensions. Multifactor analysis of variance (MANOVA) is applied to filter the related features and establish the hypertension recognizing model with relevant features to efficiently recognize the patients' severity. In this paper, 139 hypertension patients' real clinical ECG data are applied, and the overall precision is 95.1%. The experimental results validate the effectiveness and reliability of the proposed recognition method in the work.
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