Application of principal component analysis to ECG signals for automated diagnosis of cardiac health

Martis, Roshan Joy; Acharya, U. Rajendra; Mandana, Kayapanda; Ray, Ajoy Kumar; Chakraborty, Chandan

doi:10.1016/j.eswa.2012.04.072

Cited by 263 publications

(90 citation statements)

References 26 publications

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“…For example, the hear rate variability is such a quantity of interest [25] • First and second order statistics -Mean and variance [26] • Discrete Wavelet Transform (DWT) -This feature extraction technique is closely related to spectrum techniques. Spectrum techniques provide only frequency restitution, whereas DWT provides both time and frequency resolution [27,28,27,29] • Independent Component Analysis (ICA) -The technique separates multivariate signals into their additive subcomponents [27,15,28] • Principal Component Analysis (PCA) -The statistical procedure is based on orthogonal transformation which produces linearly uncorrelated parameters known as the principal components [30,29,28,31,32] • Linear Discriminant Analysis (LDA) -Yields features that characterizes two or more signal classes [28] • Discrete Cosine Transform (DCT) -Spectrum technique based on cosine waves [15,33] Nonlinear methods are based on the more recent concepts of chaos and fuzzy logic [34,35,36]. The novelty of these methods is reflected in the fact that only a few recent CAD systems employ them, as indicated in the following list.…”

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Computer Aided Diagnosis for Cardiovascular Diseases Based on Ecg Signals: A Survey

Faust

2016

J. Mech. Med. Biol.

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The interpretation of Electroencephalography (ECG) signals is difficult, because even subtle changes in the waveform can indicate a serious heart disease. Furthermore, these waveform changes might not be present all the time. As a consequence, it takes years of training for a medical practitioner to become an expert in ECG-based cardiovascular disease diagnosis. That training is a major investment in a specific skill. Even with expert ability, the signal interpretation takes time. In addition, human interpretation of ECG signals causes interoperator and intraoperator variability. ECG-based Computer-Aided Diagnosis (CAD) holds the promise of improving the diagnosis accuracy and reducing the cost. The same ECG signal will result in the same diagnosis support regardless of time and place. This paper introduces both the techniques used to realize the CAD functionality and the methods used to assess the established functionality. This survey aims to instill trust in CAD of cardiovascular diseases using ECG signals by introducing both a conceptional overview of the system and the necessary assessment methods.

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Section: Feature Extractionmentioning

confidence: 99%

“…the signal classes a linearly separable. For nonlinear data, nonlinear kernel functions are used [49,28,29,31,32].…”

Section: Classificationmentioning

confidence: 99%

Computer Aided Diagnosis for Cardiovascular Diseases Based on Ecg Signals: A Survey

Faust

2016

J. Mech. Med. Biol.

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“…The PCA technique includes the calculation and decomposition of a covariance matrix (obtained from data) into eigenvectors and eigenvalues [21,23]. The eigenvectors are organized in descending order of eigenvalues and finally the data are projected in the directions of sorted eigenvectors [23,35]. The general steps involved in PCA are described below [8]:…”

Section: Principal Component Analysismentioning

confidence: 99%

Electrocardiogram signal analysis for R-peak detection and denoising with hybrid linearization and principal component analysis

Kaur¹,

Rajni²

2017

Turk J Elec Eng & Comp Sci

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Abstract:In the areas of biomedical and healthcare, electrocardiogram (ECG) signal analysis is one of the major aspects of research. The accuracy in the detection of subtle characteristic features in ECG is of great significance. This paper deals with an algorithm based on hybrid linearization and principal component analysis for ECG signal denoising and R-peak detection. The ECG data have been taken from the MIT-BIH Arrhythmia Database for performance evaluation.The signal is denoised by applying the hybrid linearization method, which is an arrangement of the extended Kalman filter along with discrete wavelet transform, and then principal component analysis is employed to detect R waves and the QRS complex. The reported work has been implemented in the MATLAB environment for 25 different ECG records yielding 99.90% sensitivity, 99.97% positive predictivity, and a detection error rate of 0.120%. The achieved performance outperforms the recent research done in the area of interest.

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“…This length can cover the range for PQRST waves. After detection of QRS complex, 99 samples were chosen from the left side of QRS mid-point and 100 samples after QRS mid-point and the QRS mid-point itself as a beat of 200 samples [15]. After preprocessing, an ECG beat complex is represented by a sequence of 200 discrete samples.…”

Section: Materials and Preprocessingmentioning

confidence: 99%

A novel approach for arrhythmia diagnosis: Self-adaptive and distribution-free mode

Zheng

Zhao

2015

BME

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Abstract. Arrhythmia diagnosis is very significant to ensure human health. In this paper, a new model is developed for arrhythmia diagnosis. A salient feature of the algorithm is a synergistic combination of statistical and fuzzy set-based techniques. It is distribution-free and is realized in an unsupervised mode. Arrhythmia diagnosis is viewed as a certain statistical hypothesis testing. 'Abnormal' is typically a much complex concept, so it can be described with the technology of fuzzy sets which bring a facet of robustness to the overall scheme and play an important role in the successive step of hypothesis testing. Intensive fuzzification is engaged in parameters determination which is self-adaptive and no parameter needs to be specified by the user. The algorithm is validated with a number of experiments, which prove its effectiveness for arrhythmia diagnosis.

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Application of principal component analysis to ECG signals for automated diagnosis of cardiac health

Cited by 263 publications

References 26 publications

Computer Aided Diagnosis for Cardiovascular Diseases Based on Ecg Signals: A Survey

Computer Aided Diagnosis for Cardiovascular Diseases Based on Ecg Signals: A Survey

Electrocardiogram signal analysis for R-peak detection and denoising with hybrid linearization and principal component analysis

A novel approach for arrhythmia diagnosis: Self-adaptive and distribution-free mode

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