Hybrid SVM classification technique to detect mental stress in human beings using ECG signals

Vanitha, L.; Suresh, G.

doi:10.1109/icaccs.2013.6938735

Cited by 25 publications

(12 citation statements)

References 7 publications

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“…With this aim, supervised methods such as naive Bayes ( 170 ), random forest ( 171 ), genetic algorithms ( 128 ), linear and quadratic discriminants ( 172 ), SVM ( 173 , 174 ), decision trees ( 175 ), discriminant analysis ( 138 ), and ANN ( 173 , 174 ) have been used. In the same way, unsupervised methods such as hierarchical clustering ( 161 ), Gaussian mixture models ( 176 ), self-organising maps ( 177 ), and kNN ( 178 ) have been used. Modern methods of deep learning ( 179 ) such as CNN ( 180 ), long short-term memory (LSTM) ( 181 ), deep neural network (DNN) ( 182 ), robust deep dictionary learning (RDDL) ( 183 ) and restricted Boltzmann machine (RBM) ( 184 ) have been implemented.…”

Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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“…Support vector machine (SVM) is a supervised machine learning classifier and it works by defining a separating a hyperplane with the help of support vectors. Some of the human stress recognition studies involving SVM classifier include (Saeed et al, 2018(Saeed et al, , 2020Vanitha and Suresh, 2013;Attallah, 2020). k-nearest neighbors (kNN) is a type of supervised and non-linear machine learning algorithm using for classification tasks.…”

Section: Objective Stress Detectionmentioning

confidence: 99%

Mental Stress Detection using Data from Wearable and Non-wearable Sensors: A Review

Arsalan¹,

Anwar²,

Majid³

2022

Preprint

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This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using data from wearable and non-wearable sensors. In particular, wearable sensor-based methods commonly use data from electroencephalography, electrocardiogram, galvanic skin response, electromyography, electrodermal activity, heart rate, heart rate variability, and photoplethysmography both individually and in multimodal fusion strategies. Whereas, methods based on non-wearable sensors include strategies such as analyzing pupil dilation and speech, smartphone data, eye movement, body posture, and thermal imaging. Whenever a stressful situation is encountered by an individual, physiological, physical, or behavioral changes are induced which help in coping with the challenge at hand. A wide range of studies has attempted to establish a relationship between these stressful situations and the response of human beings by using different kinds of psychological, physiological, physical, and behavioral measures. Inspired by the lack of availability of a definitive verdict about the relationship of human stress with these different kinds of markers, a detailed survey about human stress detection methods is conducted in this paper. In particular, we explore how stress detection methods can benefit from artificial intelligence utilizing relevant data from various sources. This review will prove to be a reference document that would

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“…Stres adalah keadaan ketika individu berada dalam situasi yang penuh tekanan atau ketika individu merasa tidak sanggup mengatasi tuntutan yang dihadapinya. Stress dapat menyebabkan berbagai penyakit fisik seperti serangan jantung, artrithis, sakit kepala yang kronis atau penyakit psikologi seperti perubahan konsentrasi, marah, cemas, susah tidur dan depresi [1]. Diperkirakan 280 juta orang di dunia mengalami depresi [2].…”

Section: Pendahuluan Dan Tinjauan Pustakaunclassified

Alat Akuisisi Data 5 Parameter Sinyal Fisiologis Sebagai Penciri Stress Pada Manusia Berbasis Arduino MEGA

Madona

2022

elementer

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Stress is a non-specific response of the body to any demands on the body. It refers to events that are felt to be harmful to a person's physical and psychological well-being. One of the effects of stress on humans is depression. Depression that is allowed to continue can cause a decrease in work productivity, impaired social relationships to suicidal ideation. Increased stress due to lifestyle and current conditions has prompted many studies to be able to perform early detection of stress by measuring physiological signals. This study makes a tool to acquire data from 5 physiological signals of the body to later be used as variables for analyzing stress levels in humans. This tool uses an MPX5050DP sensor for blood pressure, a pulse sensor for heart rate detection, a DS18b20 sensor for body temperature, a sound sensor to calculate respiration frequency and a GSR sensor to measure the resistance value of human skin. The sensor will take data from the measured physiological parameters which will then be displayed on the LCD screen. The test was carried out on 5 test subjects with each subject taking data 5 times. The error rate of each parameter is obtained as follows: heart rate sensor 1.14%, temperature sensor 0.36%, blood pressure sensor 11.2% for systolic measurement and 4.9% for diastolic measurement, and respiration sensor 22.08 %.

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Hybrid SVM classification technique to detect mental stress in human beings using ECG signals

Cited by 25 publications

References 7 publications

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Mental Stress Detection using Data from Wearable and Non-wearable Sensors: A Review

Alat Akuisisi Data 5 Parameter Sinyal Fisiologis Sebagai Penciri Stress Pada Manusia Berbasis Arduino MEGA

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