Detection of valvular heart diseases using impedance cardiography ICG

Chabchoub, Souhir; Mansouri, S.; Salah, R. Ben

doi:10.1016/j.bbe.2017.12.002

Cited by 17 publications

(15 citation statements)

References 32 publications

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“…The accuracy, precision, and recall are all slightly higher compared to the values in 22 . In addition, the general performance is higher than 5 and has closer performance to 7 . In conclusion, there is no significant benefit in multi-class classification based on the current database.…”

Section: Results From 2-d Cnnmentioning

confidence: 95%

Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

Yang

Ojha

Aranoff

et al. 2020

Sci Rep

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This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT). Performance comparisons were conducted among several machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neural network, and extreme gradient boosting. In addition, a two-dimensional convolutional neural network (2D-CNN) was developed using the CWT coefficients as images. The 2D-CNN was made with a custom-built architecture and a CNN based on Mobile Net via transfer learning. After the reduction of features by 95.47%, the results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 by XGBoost. Via the 2D-CNN framework, the transfer learning of Mobile Net shows an accuracy of 0.91, while the custom-constructed classifier reveals an accuracy of 0.89. Our results validate the effectiveness of the feature selection and classification framework. They also show a promising potential for the implementation of deep learning tools on the classification of AS.

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Section: Results From 2-d Cnnmentioning

confidence: 95%

Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

Yang

Ojha

Aranoff

et al. 2020

Sci Rep

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“…At this stage, predictive models were built from significant input data by using ML algorithms for CVD risk classification. The six state-of-the-art methods with the most widely used algorithms related to CVD classification were ANN [25], LDA [19], linear and quadratic SVMs [21][22], DT [23], and kNN [24].…”

Section: Automatic Classificationmentioning

confidence: 99%

“…The well-known ML algorithms have four types: supervised, unsupervised, semi supervised, and reinforcement learning. The supervised learning methods, which include linear discriminant analysis (LDA) [19], support vector machine (SVM) [20][21][22], decision tree (DT) [23], k-nearest neighbor (kNN) [24], artificial neural network (ANN) [9; 25], logistic regression [26], and fuzzy logic [27], are widely used for group classification. ANN is widely applied in predicting CHD [28], whereas SVM is frequently adopted in classifying arrhythmia [29].…”

mentioning

confidence: 99%

Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

Nayan¹,

Hamid²,

Suboh³

et al. 2020

Preprint

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Background: Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. Results: This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. Conclusions: In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

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“…Measures of peripheral physiological signals (biosignals), including the electrocardiogram (ECG), impedance cardio- gram (ICG), electromyogram (EMG), electrodermal activity (EDA) and continuous, noninvasive arterial blood pressure (BP) can provide rich information about peripheral physiological functioning of the body with the potential for providing insights on phenomena such as health status, sleep quality, and the affective properties of psychological experience. These measures are thus widely used as the input data for machine learning and pattern recognition algorithms focused on preventive care, diagnostics, and telemedicine, including guiding therapy [1] – [8] . For example, a support vector machine (SVM) classifier was trained on ECG signal features, such as inter-beat interval (IBI) to predict ischaemic heart disease in [2] , and an SVM was trained on EDA signal features to corroborate coding by adult experimenters of groups of children who were either easy or hard to engage socially during a set of lab interaction tasks [3] .…”

Section: Introductionmentioning

confidence: 99%

“…In [4] , an inference model trained on EMG signal features was used to aid the diagnosis of neuromuscular disorders, and in [5] , a model trained on BP signal features was used to aid in diagnosing diabetes. Valvular heart diseases have been predicted by models using ICG signal features [6] and physiological responding under stress has been studied by analyzing multimodal data comprising ECG, EMG, EDA, and ICG signal features [7] . These are just a few examples of the rapidly expanding research utilizing the information present in biosignals for machine learning and pattern recognition in healthcare and psychological science [7] , [9] .…”

Section: Introductionmentioning

confidence: 99%

An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data

Nabian

Yin

Wormwood

et al. 2018

IEEE J. Transl. Eng. Health Med.

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Electrocardiogram, electrodermal activity, electromyogram, continuous blood pressure, and impedance cardiography are among the most commonly used peripheral physiological signals (biosignals) in psychological studies and healthcare applications, including health tracking, sleep quality assessment, disease early-detection/diagnosis, and understanding human emotional and affective phenomena. This paper presents the development of a biosignal-specific processing toolbox (Bio-SP tool) for preprocessing and feature extraction of these physiological signals according to the state-of-the-art studies reported in the scientific literature and feedback received from the field experts. Our open-source Bio-SP tool is intended to assist researchers in affective computing, digital and mobile health, and telemedicine to extract relevant physiological patterns (i.e., features) from these biosignals semi-automatically and reliably. In this paper, we describe the successful algorithms used for signal-specific quality checking, artifact/noise filtering, and segmentation along with introducing features shown to be highly relevant to category discrimination in several healthcare applications (e.g., discriminating patterns associated with disease versus non-disease). Further, the Bio-SP tool is a publicly-available software written in MATLAB with a user-friendly graphical user interface (GUI), enabling future crowd-sourced modification to these tools. The GUI is compatible with MathWorks Classification Learner app for inference model development, such as model training, cross-validation scheme farming, and classification result computation.

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Detection of valvular heart diseases using impedance cardiography ICG

Cited by 17 publications

References 32 publications

Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data

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