Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor

Park, Dajeong; Lee, Miran; Park, Sunghee Estelle; Seong, Joon Kyung; Youn, Inchan

doi:10.3390/s18072387

Cited by 31 publications

(18 citation statements)

References 32 publications

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“…Feature selection was performed on the MRI data of brain tissue, and the classification using USVM-RFE showed better results than the one using SVR-RFE. The implementation of RFE was also done in the study [18], where RFE-SVM was used to determine the best feature among the various heart rate variability (HRV) data. The study showed that RFE-SVM could identify the HRV feature and detect the stress level better.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment

et al. 2021

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Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data with high-dimensional features. In contrast, not all features are needed by machine learning, and in many cases, high-dimensional features decrease the performance of machine learning. The feature selection method is one of the appropriate approaches to reducing the features to ensure machine learning works efficiently. Various selection methods have been proposed, but research to determine the essential subset feature in psychosocial education has not been established thus far. This research investigated and proposed methods to determine the best feature selection method in the domain of psychosocial education. We used a multi-criteria decision system (MCDM) approach with Additive Ratio Assessment (ARAS) to rank seven feature selection methods. The proposed model evaluated the best feature selection method using nine criteria from the performance metrics provided by machine learning. The experimental results showed that the ARAS is promising for evaluating and recommending the best feature selection method for psychosocial education data using the teacher’s psychosocial risk levels dataset.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment

et al. 2021

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“…Therefore, a master feature vector is created and GridSearch-based Recursive Feature Elimination with Cross-validation (RFECV) scheme followed by stacked generalization is introduced to select, rank, extract suitable feature subset with higher classification prediction performance with reduced generalization errors. RFECV and stacked generalization have previously been proven best in various related application domains [25,26].…”

Section: Related Workmentioning

confidence: 99%

“…Recursive Feature Elimination (RFE) is a scheme that excludes features based on its irrelevancy and low data integrity to a specified class distribution [25,35]. The elimination process is a continuous process until a complete list of deterministic feature subset is reached.…”

Section: Gridsearch-based Rfecv + Ig Feature Selection Schemementioning

confidence: 99%

Diagnosis and Prediction of Large-for-Gestational-Age Fetus Using the Stacked Generalization Method

Akhtar

Pei

et al. 2019

Applied Sciences

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An accurate and efficient Large-for-Gestational-Age (LGA) classification system isdeveloped to classify a fetus as LGA or non-LGA, which has the potential to assist paediatricians andexperts in establishing a state-of-the-art LGA prognosis process. The performance of the proposedscheme is validated by using LGA dataset collected from the National Pre-Pregnancy and ExaminationProgram of China (2010–2013). A master feature vector is created to establish primarily datapre-processing, which includes a features’ discretization process and the entertainment of missingvalues and data imbalance issues. A principal feature vector is formed using GridSearch-basedRecursive Feature Elimination with Cross-Validation (RFECV) + Information Gain (IG) featureselection scheme followed by stacking to select, rank, and extract significant features from the LGAdataset. Based on the proposed scheme, different features subset are identified and provided tofour different machine learning (ML) classifiers. The proposed GridSearch-based RFECV+IG featureselection scheme with stacking using SVM (linear kernel) best suits the said classification processfollowed by SVM (RBF kernel) and LR classifiers. The Decision Tree (DT) classifier is not suggestedbecause of its low performance. The highest prediction precision, recall, accuracy, Area Underthe Curve (AUC), specificity, and F1 scores of 0.92, 0.87, 0.92, 0.95, 0.95, and 0.89 are achievedwith SVM (linear kernel) classifier using top ten principal features subset, which is, in fact higherthan the baselines methods. Moreover, almost every classification scheme best performed with tenprincipal feature subsets. Therefore, the proposed scheme has the potential to establish an efficientLGA prognosis process using gestational parameters, which can assist paediatricians and experts toimprove the health of a newborn using computer aided-diagnostic system.

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“…Lanata [41] used this algorithm to identify horses' response to human fear and happiness. Mirhoseini [42] used it to detect early cardiac death while this pattern recognition classifier was also used for health care applications based on Heart Rate Variability [45][46][47][48][49][50] The main use of this supervised algorithm is to distinguish between two classes (or more). Having a set of points in a feature space, each point is associated to a label.…”

Section: Support Vector Machinementioning

confidence: 99%

Stress Level Classification Using Heart Rate Variability

Tlija¹,

Istrate²,

Badii³

et al. 2019

Adv. sci. technol. eng. syst. j.

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The research programme reported in this paper is set within the framework of our research under the theme of ICT support for Active Healthy Ageing (AHA). This longitudinal empirical research is focused on the study of the impact on the management of cardiovascular disease if supported by sustained health monitoring using wearable connected devices. One of the key objectives is stress monitoring and its classification during the daily routine of life thus enabling psycho-physiological monitoring to study the correlation between emotional states including variable stress levels and the evolution and prognosis of cardiovascular disease. In this paper, the calibration phase will be studied in order to distinguish between two emotional states: i) meditation and ii) stress condition. For this, the Heart Rate Variability (HRV) features are used as extracted from the RR interval and a support vectors machine (SVM) classifier deployed which resulted in 74% and 87% recognition accuracy based on HRV data for the recognition of the two emotional states, namely meditative, and, stressed, respectively. The main objective is to prevent Cardio-Vascular Disease (CVD) in healthy people and to treat those who already suffer from it. Creating a reference database was our first step in this research project. The sensor choice was made based on doctors' recommendations. The work methodology was as follows: first validate the « objective data » issuing from the calibration state. Second, set up the automatic algorithm and detect automatically the patient's emotional states during the experimentation period (subjective data). Third analyse the physical activities correlated to the blood pressure and emotions. This study has involved the challenge of distinguishing the influence of stress versus relaxation on the Cardio-Vascular function and in particular on the risk of exacerbation of pre-existing Cardio-Vascular Disease.

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Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor

Cited by 31 publications

References 32 publications

Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment

Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment

Diagnosis and Prediction of Large-for-Gestational-Age Fetus Using the Stacked Generalization Method

Stress Level Classification Using Heart Rate Variability

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