A Novel Index of Sympathovagal Balance From Independent Component Analysis of Heart Rate and Blood Pressure Variability: Performance in Patients With Carotid Stenting and in the EuroBavar Data Set

2011 ◽  
Vol 28 (2) ◽  
pp. 227-232
Author(s):  
Chih-Cheng Huang ◽  
Cheng-Hsien Lu ◽  
Yi-Chun Du ◽  
Yung-Fu Chen ◽  
Yi-Shan Wu ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Independent Component Analysis ◽  
Blood Pressure Variability ◽  
Carotid Stenting ◽  
Component Analysis ◽  
Independent Component ◽  
Sympathovagal Balance ◽  
Data Set

scholarly journals Detection of Indeterminacies in Corrected ECG Signals Using Parameterized Multidimensional Independent Component Analysis

2009 ◽  
Vol 10 (2) ◽  
pp. 85-115 ◽  
Author(s):  
M. P. S. Chawla
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Complex Signals ◽  
Data Set ◽  
Noise Component ◽  
Ecg Signals ◽  
Independent Components ◽  
Statistical Measures ◽  
Ecg Data

Independent component analysis (ICA) is a new technique suitable for separating independent components from electrocardiogram (ECG) complex signals. The basic idea of using multidimensional independent component analysis (MICA) is to find stable higher dimensional source signal subspaces and to decompose each rotation into elementary rotations within all two-dimensional planes spanned by the coordinate axes useful for diagnostic information of heart. In this paper, ability of ICA for parameterization of ECG signals was felt to reduce the amount of redundant ECG data. This work aims at finding an independent subspace analysis (ISA) model for ECG analysis that allows applicability to any random vectors available in an ECG data set. For the common standards for electrocardiography (CSE) based ECG data sets, joint approximate diagonalization of eigen matrices (Jade) algorithm is used to find smaller subspaces. The extracted independent components are further cleaned by statistical measures. In this study, it is also observed that the value of kurtosis coefficients for the independent components, which represents the noise component, can be further reduced using parameterized multidimensional ICA (PMICA) technique. The indeterminacies if available in the ECG data are to be analysed also using modified version of Jade algorithm to PMICA and parameterized standard ICA (PsICA) for comparative studies. The indeterminacies if available in the ECG data are reduced in PMICA better in comparison to the analysis done using PsICA. The simulation results obtained indicate that ICA definitely improves signal–noise ratio (SNR) like the other higher order digital filtering methods like Kalman, Butterworth etc. with minimum reconstruction errors. Here, it is also confirmed that re-parameterization of the standard ICA model results into a ‘component model’ using MICA technique, which is geometric in spirit and free of indeterminacies existing in sICA model.

scholarly journals Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3238
Author(s):  
Ruisheng Lei ◽  
Bingo Wing-Kuen Ling ◽  
Peihua Feng ◽  
Jinrong Chen
Keyword(s):  
Heart Rate ◽  
Independent Component Analysis ◽  
Respiratory Rate ◽  
Empirical Mode Decomposition ◽  
Matrix Factorization ◽  
Component Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Independent Component ◽  
Mode Decomposition ◽  
Non Negative Matrix Factorization

This paper proposes a framework combining the complementary ensemble empirical mode decomposition with both the independent component analysis and the non-negative matrix factorization for estimating both the heart rate and the respiratory rate from the photoplethysmography (PPG) signal. After performing the complementary ensemble empirical mode decomposition on the PPG signal, a finite number of intrinsic mode functions are obtained. Then, these intrinsic mode functions are divided into two groups to perform the further analysis via both the independent component analysis and the non-negative matrix factorization. The surrogate cardiac signal related to the heart activity and another surrogate respiratory signal related to the respiratory activity are reconstructed to estimate the heart rate and the respiratory rate, respectively. Finally, different records of signals acquired from the Medical Information Mart for Intensive Care database downloaded from the Physionet Automated Teller Machine (ATM) data bank are employed for demonstrating the outperformance of our proposed method. The results show that our proposed method outperforms both the digital filtering approach and the conventional empirical mode decomposition based methods in terms of reconstructing both the surrogate cardiac signal and the respiratory signal from the PPG signal as well as both achieving the higher accuracy and the higher reliability for estimating both the heart rate and the respiratory rate.

MULTIDIMENSIONAL INDEPENDENT COMPONENT ANALYSIS FOR STATISTICAL ESTIMATIONS OF INDETERMINACIES IN ELECTROCARDIOGRAMS

2009 ◽  
Vol 09 (03) ◽  
pp. 345-375 ◽  
Author(s):  
M. P. S. CHAWLA
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Set ◽  
Noise Component ◽  
Ecg Signals ◽  
Higher Dimensional ◽  
Source Signal ◽  
Statistical Estimations ◽  
Ecg Data

Independent component analysis (ICA) is a technique capable of separating independent components (ICs) from complex electrocardiogram (ECG) signals. The basic intention behind using multidimensional independent component analysis (MICA) is to find stable higher dimensional source signal subspaces. This study highlights the ability of ICA for parametrization of ECG signals to reduce the amount of redundant ECG data if any in a data set. The aim of this paper is to justify the underlying theory of the use of ICA and how it can be extended to for MICA separation of the ECG signals for combinational leads to attain most useful diagnostic information, which was not discussed in other some similar previous publications in this field. It is also investigated that the value of kurtosis coefficients for the ICs, which represents the noise component, can be further reduced using parametrized multidimensional independent component analysis (PMICA) technique. The indeterminacies available in the ECG data are also analyzed using modified version of Jade algorithm for PMICA and parametrized standard independent component analysis (PSICA). For the ECG data set, Jade algorithm is applied first to find smaller subspaces for MICA analysis and can therefore be regarded as a basis algorithm for PMICA analysis. The simulation results are obtained in Matlab environment to indicate that, ICA can definitely improve signal–noise ratio (SNR) in minimizing the reconstruction errors. The future scope of MICA expected by author is that, by reconsidering the notion of ICA, a more general perspective can be envisioned: i.e. modified multidimensional independent component analysis (MMICA). It would be based on a morphological geometric parametrization (MGP) which would further reduce the indeterminacies involved in matrix-based modeling (MBM).

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