ECG Signal Processing Using Dyadic Wavelet for Mental Stress Assessment

Ranganathan, G.; Bindhu, V.; Rangarajan, R.

doi:10.1109/icbbe.2010.5516360

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…As the Fourier analysis decomposes the given signal into its frequency components, the ecg is also composed of different r=frequency component and the frequency domain analysis reveals vital information about the heart pumping. [7] After the Fourier transform analysis of ecg, the wavelet based analysis is very common. There are different wavelet families where the ecg can be decomposed into approximates and details component and that at reduced dimension.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Classifier for Mental Stress Estimation using ECG Statistical Parameters

Mittal¹,

Grewal²

2014

IJCA

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Mental Stress estimation is an important feature to be derived in health related diagnostic activity. It has been observed that the stress has a major effect on heart functioning. And therefore, ecg should be the major source of stress variation and can be analyzed in various ways in order to extract the effect of mental stress. In the presented work, the ecg is analyzed using the statistical parameters set (energy, entropy, power, standard deviation and covariance). The parameters are not directly computed form the ecg itself. The ecg is first decomposed to level-2 using BIOR-3.9 wavelet transform to reduce the dimensionality of the ecg sample size. The level-1 and level-2 parameters are used to derive the mental stress levels as normal (N), hyper-1 (H-1), hyper-2 (H-2), depression-1 (D-1) and depression-2 (D-2). On parameter analysis, it has been observed that the energy and entropy are the two parameters that show an effective variation in values when normal to depression or normal to hyper case is observed. Therefore, the energy and entropy values are used for rule making and learning of the system in order to derive the mental stress levels

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Section: Related Workmentioning

confidence: 99%

Fuzzy Classifier for Mental Stress Estimation using ECG Statistical Parameters

Mittal¹,

Grewal²

2014

IJCA

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“…[7] A wavelet approach is presented for analysis of ecg signal and denoising of the same. The denoised ecg signal is then exposed to stress assessment algorithm.…”

Section: Related Workmentioning

confidence: 99%

Stress Quantification using Fuzzy Analysis of ECG Parameters

Mittal¹,

Grewal²

2014

IJCA

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Mental stress quantification using fuzzy analysis of ecg parameters is presented here. ECG signal is decomposed using the BIOR-3.9 wavelet family upto three levels. The approximates signals are used for computation ecg parameters like energy, entropy, power, standard deviation, mean and covariance. A fuzzy classifier is designed using trimf function as associate membership in fuzzy analysis. The ecg data base is taken from MIT data base web site.

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A Novel Method for Mental Stress Assessment Based on Heart Rate Variability Analysis of Electrocardiogram Signals

Saini,

Gupta

2024

Wireless Pers Commun

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ECG Signal Processing Using Dyadic Wavelet for Mental Stress Assessment

Cited by 7 publications

References 3 publications

Fuzzy Classifier for Mental Stress Estimation using ECG Statistical Parameters

Fuzzy Classifier for Mental Stress Estimation using ECG Statistical Parameters

Stress Quantification using Fuzzy Analysis of ECG Parameters

A Novel Method for Mental Stress Assessment Based on Heart Rate Variability Analysis of Electrocardiogram Signals

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