Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG‐based approach

Angelaki, Eleni; Marketou, Maria; Barmparis, Georgios D.; Patrianakos, Alexandros; Vardas, Panos; Parthenakis, Fragiskos I.; Tsironis, G. P.

doi:10.1111/jch.14200

Cited by 24 publications

(19 citation statements)

References 33 publications

(77 reference statements)

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“…We chose to include ECG measurements adjusted for BMI based on studies showing that larger body mass decreases the amplitude of the R and S waves in specific leads due to the electrical currents traveling longer distances. The most important ECG-derived features were: the BMI-adjusted Cornell criteria (BMI multiplied by RaVL+SV 3 ); [26] the BMI-modified Sokolow-Lyon voltage (BMI divided by SV 1 +RV 5 ); [12] and R wave amplitude in aVL.…”

Section: Feature Engineeringmentioning

confidence: 99%

“…The ECG is of paramount importance in the initial evaluation of a patient suspected to have a cardiovascular pathology [10,12] . In this paper we are attempting to detect whether a person is hypertensive using features from the ECG, as well as basic anthropometric features such as age, sex, and body mass index (BMI).…”

mentioning

confidence: 99%

“…The fast-growing number of applications of ML/data analysis in healthcare allows identification of diseases even in early stages and prognostication of clinical outcome, thereby increasing the efficacy of treatment options. Artificial Intelligence (AI) techniques have the potential to radically change the way we practice cardiovascular medicine, providing new tools to interpret data and make aid in clinical decisions [8][9][10][11][12] . While still a new player in Cardiology, ML has already made its mark in clinical diagnostics and research and continues to evolve rapidly [13][14][15][16] .…”

mentioning

confidence: 99%

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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: Feature Engineeringmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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

Angelaki

Barmparis

Kochiadakis

et al. 2022

Preprint

Self Cite

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show abstract

“…The fast-growing number of applications of machine learning /data analysis in healthcare allows identification of diseases even in early stages and prognostication of clinical outcome, thereby increasing the efficacy of treatment options. Artificial intelligence techniques have the potential to radically change the way we practice cardiovascular medicine by providing new tools to interpret data, therefore aiding in clinical decisions [4][5][6][7][8]. Although still a new player in Cardiology, machine learning has already made its mark in clinical diagnostics and research in the field and continues to evolve rapidly [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…The ECG is of paramount importance in the initial evaluation of a patient suspected to have a cardiovascular disease [6,8]. In this study, we are attempting to detect whether a person is hypertensive using features extracted from the ECG, as well as basic anthropometric features such as age, sex and BMI.…”

Section: Introductionmentioning

confidence: 99%

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

Angelaki

Barmparis

Kochiadakis

et al. 2022

Journal of Hypertension

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Objectives: Hypertension is a major risk factor for cardiovascular disease (CVD), which often escapes the diagnosis or should be confirmed by several office visits. The ECG is one of the most widely used diagnostic tools and could be of paramount importance in patients' initial evaluation.Methods: We used machine learning techniques based on clinical parameters and features derived from the ECG, to detect hypertension in a population without CVD. We enrolled 1091 individuals who were classified as hypertensive or normotensive, and trained a Random Forest model, to detect the existence of hypertension. We then calculated the values for the Shapley additive explanations (SHAP), a sophisticated feature importance analysis, to interpret each feature's role in the Random Forest's results.Results: Our Random Forest model was able to distinguish hypertensive from normotensive patients with accuracy 84.2%, specificity 78.0%, sensitivity 84.0% and area under the receiver-operating curve 0.89, using a decision threshold of 0.6. Age, BMI, BMI-adjusted Cornell criteria (BMI multiplied by RaVLRSV 3 ), R wave amplitude in aVL and BMI-modified Sokolow-Lyon voltage (BMI divided by SV 1 RRV 5 ), were the most important anthropometric and ECG-derived features in terms of the success of our model. Conclusion:Our machine learning algorithm is effective in the detection of hypertension in patients using ECGderived and basic anthropometric criteria. Our findings open new horizon in the detection of many undiagnosed hypertensive individuals who have an increased CVD risk.

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Cardiovascular Disease Prediction Using Deep Neural Network for Older People

Telagam¹,

Kranti²,

Devarasetti³

2022

Deep Learning for Targeted Treatments

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Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG‐based approach

Cited by 24 publications

References 33 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

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

Cardiovascular Disease Prediction Using Deep Neural Network for Older People

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