ArticleInternational journal of environmental research and public health2019
Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- 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
- Non-invasive enhanced hypertension detection through ballistocardiograph signals with Mamba model.PeerJ. Computer science · 2025Article
- Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation.Heliyon · 2024Article
- Automated Hypertension Detection Using ConvMixer and Spectrogram Techniques with Ballistocardiograph Signals.Diagnostics (Basel, Switzerland) · 2023Article
- Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- 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
- Automated detection of schizophrenia using optimal wavelet-basedCognitive neurodynamics · 2021Article
- Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals.International journal of environmental research and public health · 2021Article
- Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme.Sensors (Basel, Switzerland) · 2021Article
- Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.Computer methods and programs in biomedicine · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypertension (HT) is an extreme increment in blood pressure that can prompt a stroke, kidney disease, and heart attack. HT does not show any symptoms at the early stage, but can lead to various cardiovascular diseases. Hence, it is essential to identify it at the beginning stages. It is tedious to analyze electrocardiogram (ECG) signals visually due to their low amplitude and small bandwidth. Hence, to avoid possible human errors in the diagnosis of HT patients, an automated ECG-based system is developed. This paper proposes the computerized segregation of low-risk hypertension (LRHT) and high-risk hypertension (HRHT) using ECG signals with an optimal orthogonal wavelet filter bank (OWFB) system. The HRHT class is comprised of patients with myocardial infarction, stroke, and syncope ECG signals. The ECG-data are acquired from physionet's smart health for accessing risk via ECG event (SHAREE) database, which contains recordings of a total 139 subjects. First, ECG signals are segmented into epochs of 5 min. The segmented epochs are then decomposed into six wavelet sub-bands (WSBs) using OWFB. We extract the signal fractional dimension (SFD) and log-energy (LOGE) features from all six WSBs. Using Student's
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