Heart Rate Variability Classification using Support Vector  Machine and Genetic Algorithm

Ashtiyani, Meghdad; Lavasani, Saeedeh Navaei; Alvar, Amin Asgharzadeh; Deevband, Mohammad Reza

doi:10.31661/jbpe.v0i0.614

Cited by 19 publications

(15 citation statements)

References 26 publications

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“…The 24 selected features are described in the table 1. Poincare plot SD1, SD2 and SD1SD2ratio, approximate entropy (ApEn) [49] , [50] The data was divided into two datasets: 80% for training data and 20% for test data. We made a k fold (k= 5) so the final obtained value is an average of the 5 iteration inside the SVM model.…”

Section: Resultsmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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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Section: Resultsmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

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“…Training data divides into two groups and SVM makes a model to assign the new given data to one of two groups. It divides these two as far as possible from each other so that it could rise the resolution of groups [18][19][20][21][22].…”

Section: Classi Ersmentioning

confidence: 99%

A comparison of the analysis of methods for feature extraction and classification by Wavelet transform in SSVEP BCIs

Heidari¹,

Einalou²,

Dadgostar³

et al. 2021

Preprint

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Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods which includes 1) feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) and wavelet transform magnitude, 2) feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), 3) classification step applying k nearest neighbor (k-NN), support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down. The highest level of accuracy was obtained 91.39%.

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“…Íèçêàÿ âàðèàáåëüíîñòü ñåðäå÷íîãî ðèòìà ïðî÷íî àññîöèèðóåòñÿ ñ ïîâûøåííûì ðèñêîì âíåçàïíîé ñìåðòè îò îñòàíîâêè ñåðäöà [Huikuri et al, 2000;La Rovere et al, 2003;Maheshwari et al, 2016]; çíà÷èòåëüíûå èçìåíåíèÿ â ÂÐÑ ðåãèñòðèðóþòñÿ òàêaeå ïðè ãèïåðòîíèè, àðèòìèè, ãèïåðòðîôèè ëåâîãî aeåëóäî÷êà è ò.ä. [Áîíäàðåíêî ñ ñîàâò., 2018; Ashtiyani et al, 2018]. Ïîêàçàòåëè ÂÐÑ èñïîëüçóþòñÿ êàê èíôîðìàòèâíûå ìàðêåðû òå÷åíèÿ ïîñëåîïåðàöèîííîé ôàçû â êàðäèîõèðóðãèè, â ÷àñòíîñòè, ïîñëå òðàíñïëàíòàöèè [Nenna et al, 2017;Takakura et al, 2017].…”

Section: истоки метода космическая и клиническая медицинаunclassified