Automated Detection of Hypertension Using Physiological Signals: A Review

Sharma, Manish; Rajput, Jaypal Singh; Tan, Ru San; Acharya, U. Rajendra

doi:10.3390/ijerph18115838

Cited by 38 publications

(17 citation statements)

References 61 publications

(164 reference statements)

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“…Second, our data were collected prospectively across real-world clinical settings and by many operators. There are limited data in the literature that attempt to associate BP level with ECG signals interpreted through ML algorithms [36][37][38] . However, the existing knowledge has derived from limited number of ECG-data acquired from previous datasets and Physionet databases.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence-based opportunistic screening for the detection of arterial hypertension through ECG signals

Angelaki

Barmparis

Kochiadakis

et al. 2022

Preprint

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BackgroundHypertension is a major risk factor for cardiovascular disease (CVD) which often escapes the diagnosis or should be confirmed by several office visits. The electrocardiogram (ECG) is one of the most widely used diagnostic tools and could be of paramount importance in patients’ initial evaluation.MethodsWe used machine learning (ML) techniques based features derived from the electrocardiogram for detecting hypertension in a population without CVD. We enrolled 1091 subjects who were classified into hypertensive and normotensive group. We trained a random forest (RF), to predict the existence of hypertension in patients based only on a few basic clinical parameters and ECG-derived features. We also calculated Shapley additive explanations (SHAP), a sophisticated feature importance analysis, to interpret each feature’s role in the random forest’s predictions.ResultsOur RF model was able to distinguish hypertensive from normotensive patients with accuracy 84.2 %, specificity 66.7%, sensitivity 91.4%, and area under the receiver-operating curve 0.86. Age, body mass index (BMI), BMI-adjusted Cornell criteria (BMI multiplied by RaVL+SV3), R wave amplitude in aVL, and BMI-modified Sokolow-Lyon voltage (BMI divided by SV1+RV5), were the most important anthropometric and ECG-derived features in terms of the success of our model.ConclusionsOur ML algorithm is effective in the detection of hypertension in patients using ECG-derived and basic anthropometric criteria. Our findings open new horizon in the detection of many undiagnosed hypertensive individuals who have an increased cardiovascular disease risk.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence-based opportunistic screening for the detection of arterial hypertension through ECG signals

Angelaki

Barmparis

Kochiadakis

et al. 2022

Preprint

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“…In this study, after the BCG signals were obtained, a scalogram plot of time versus frequency represented the absolute CWT coefficients value of a signal [ 4 , 28 , 40 ]. A scalogram better identifies low frequency BCG signals or fast changing components of BCG signals when compared to a spectrogram.…”

Section: Methodsmentioning

confidence: 99%

“…BCG is a measurement technique that records the whole-body recoil forces (or the related displacements/velocities/accelerations) due to blood circulation. It is a novel, non-invasive-based diagnostic method used for HPT detection [ 4 , 15 , 16 ]. Micro body vibrations occur in the body due to the heart’s physiological activity, which is then picked up by the BCG to generate precise physiological signals [ 15 , 17 , 18 , 19 ].…”

Section: Cardio-mechanical Signalsmentioning

confidence: 99%

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Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals

Rajput

Sharma

Kumar

et al. 2022

IJERPH

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Managing hypertension (HPT) remains a significant challenge for humanity. Despite advancements in blood pressure (BP)-measuring systems and the accessibility of effective and safe anti-hypertensive medicines, HPT is a major public health concern. Headaches, dizziness and fainting are common symptoms of HPT. In HPT patients, normalcy may be observed at one instant and abnormality may prevail during a long duration of 24 h ambulatory BP. This may cause difficulty in identifying patients with HPT, and hence there is a possibility that individuals may be untreated or administered insufficiently. Most importantly, uncontrolled HPT can lead to severe complications (stroke, heart attack, kidney disease, and heart failure), mainly ignoring the signs in nascent stages. HPT in the beginning stages may not present distinct symptoms and may be difficult to diagnose from standard physiological signals. Hence, ballistocardiography (BCG) signal was used in this study to detect HPT automatically. The processed signals from BCG were converted into scalogram images using a continuous wavelet transform (CWT) and were then fed into a 2-D convolutional neural network model (2D-CNN). The model was trained to learn and recognize BCG patterns of healthy controls (HC) and HPT classes. Our proposed model obtained a high classification accuracy of 86.14% with a ten-fold cross-validation (CV) strategy. Hence, this is the first use of a 2D-CNN model (deep-learning algorithm) to detect HPT employing BCG signals.

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“…Deep learning methods are widely used to diagnose many different diseases and have attained high performance [ 17 , 18 , 19 , 20 ]. The convolutional neural network (CNN) is one of the most widely used models in deep learning and is often preferred for OCT image analysis [ 21 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Barua

Chan

Doğan

et al. 2021

Entropy

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Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

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Automated Detection of Hypertension Using Physiological Signals: A Review

Cited by 38 publications

References 61 publications

Artificial intelligence-based opportunistic screening for the detection of arterial hypertension through ECG signals

Artificial intelligence-based opportunistic screening for the detection of arterial hypertension through ECG signals

Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

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