scholarly journals ALGORITMA PRINCIPAL COMPONEN ANALYSIS DALAM PEMROSESAN SINYAL ELECTROKARDIOGRAM

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Husaini Husaini ◽  
Huzaeni Huzaeni ◽  
Fahmi Fahmi
Keyword(s):  
Principal Component Analysis ◽  
Parametric Method ◽  
Principal Component ◽  
Component Analysis ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Redundant Data ◽  
The Cost ◽  
Pca Algorithm ◽  
Non Parametric

Abstrak — Principal Component Analysis (PCA) merupakan salah satu teknik yang ada dalam statistic dan merupakan metode non parametric untuk mengekstraksi informasi-informasi yang bersesuaian dari sekumpulan data yang masih diragukan dan memerlukan proses untuk menghilangkan gangguan-gangguan yang ada. Data yang dimaksud salah satunya adalah sinyal ektrokardiogram (EKG). Sinyal EKG merupakan sinyal yang diperoleh dari rekaman aktifitas elektrik dari jantung. Rekaman sinyal EKG tidak saja digunakan untuk tujuan diagnosa, tapi juga disimpan sebagai referensi dalam mengklasifikasi EKG arrhythmia. Untuk mendapatkan hasil yang lebih baik maka data-data sinyal EKG akan direduksi dimensinya dengan tujuan untuk menghilangkab data-data yang tidak sesuai, tidak relevan dan data redundant sehingga dapat menghemat biaya komputasinya dan mencegah data-data yang over-fitting. Tulisan ini memaparkan tentang ide dasar dari PCA dalam mereduksi dimensi data-data dari sinyal  EKG. Hasil yang ditampilkan adalah berupa proses-proses dalam algoritma PCA dan akurasi klasifikasi sinyal  dengan metode KNN dan Naive Bayes.Kata kunci : principal component analysisi (PCA), sinyal EKG, reduksi dimensi Abstract — The Principal Component Analysis (PCA) is one of the existing techniques in statistics and a non parametric method for extracting the information from a collection of data that still in doubt and requires a process to remove any disturbances. The data in question one of them is the signal ektrokardiogram (ECG). ECG signals are signals obtained from recording electrical activity from the heart. ECG signal recording is not only used for diagnostic purposes, but is also stored as a reference in classifying ECG arrhythmias. To get better results then the ECG signal data will be reduced the dimension. The aim to removed data that are not appropriate, irrelevant and redundant data so as to save the cost of computing and prevent data over-fitting. This paper describes the basic idea of PCA in reducing the dimensions of data from ECG signals. The results shown are the processes in PCA algorithm and signal classification accuracy by KNN and Naive Bayes methods.Keywords— Principal Component Analysis, ECG Signal, reduction dimentionality

RECOGNITION OF SMILING FACES USING NEURAL NETWORKS AND SPCA

2004 ◽  
Vol 04 (02) ◽  
pp. 153-163 ◽  
Author(s):  
MIYOKO NAKANO ◽  
FUMIKO YASUKATA ◽  
MINORU FUKUMI
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
High Speed ◽  
Principal Component ◽  
Component Analysis ◽  
Research Field ◽  
A Value ◽  
Large Matrix ◽  
The Difference ◽  
The Cost

Research on "man-machine interface" has increased in many fields of engineering and its application to facial expressions recognition is expected. The eigenface method by using the principal component analysis (PCA) is popular in this research field. However, it is not easy to compute eigenvectors with a large matrix if the cost of calculation when applying it for time-varying processing is taken into consideration. In this paper, in order to achieve high-speed PCA, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos θ is calculated using an eigenvector by SPCA as well as a gray-scale image vector of each picture pattern. By using neural networks (NNs), the difference in the value of cos θ between the true and the false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smiles, computer simulations are done with real images. Furthermore, an experiment using the self-organisation map (SOM) is also conducted as a comparison.

scholarly journals Statistical Study of Variability in Rainfall and Analysis of Etreme Rainfall Events for Hill Stations of Uttarakhand

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Bimal Pande ◽  
Sneh Joshi ◽  
Seema Pande
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Statistical Study ◽  
Parametric Method ◽  
Principal Component ◽  
Extreme Rainfall ◽  
Component Analysis ◽  
Rainfall Pattern ◽  
Composite Matrix ◽  
Rainfall Events

Statistical analysis of rainfall pattern and its variability for 20 years (1990-2010) data is performed for two mountainous urban centres of Uttarakhand i.e. Almora (29.60 N, 79.670 E and altitude 1,204m asl) and Nainital (29.40 N, 79.470 E and altitude 2,020m asl). Non Parametric method of Principal Component Analysis (PCA) gives the correlation between different extreme rainfall indices. It is concluded that PCA suggest 90% of the variance in composite matrix of extreme rainfall indices.

SEGMENT CLASSIFICATION OF ECG DATA AND CONSTRUCTION OF SCATTER PLOTS USING PRINCIPAL COMPONENT ANALYSIS

2008 ◽  
Vol 08 (03) ◽  
pp. 421-458 ◽  
Author(s):  
M. P. S. CHAWLA
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Feature Selection ◽  
Order Statistics ◽  
Principal Component ◽  
Component Analysis ◽  
Ecg Signal ◽  
Characteristic Points ◽  
Scatter Plots ◽  
Ecg Data

In many medical applications, feature selection is obvious; but in medical domains, selecting features and creating a feature vector may require more effort. The wavelet transform (WT) technique is used to identify the characteristic points of an electrocardiogram (ECG) signal with fairly good accuracy, even in the presence of severe high-frequency and low-frequency noise. Principal component analysis (PCA) is a suitable technique for ECG data analysis, feature extraction, and image processing — an important technique that is not based upon a probability model. The aim of the paper is to derive better diagnostic parameters for reducing the size of ECG data while preserving morphology, which can be done by PCA. In this analysis, PCA is used for decorrelation of ECG signals, noise, and artifacts from various raw ECG data sets. The aim of this paper is twofold: first, to describe an elegant algorithm that uses WT alone to identify the characteristic points of an ECG signal; and second, to use a composite WT-based PCA method for redundant data reduction and better feature extraction. PCA scatter plots can be observed as a good basis for feature selection to account for cardiac abnormalities. The study is analyzed with higher-order statistics, in contrast to the conventional methods that use only geometric characteristics of feature waves and lower-order statistics. A new algorithm — viz. PCA variance estimator — is developed for this analysis, and the results are also obtained for different combinations of leads to find correlations for feature classification and useful diagnostic information. PCA scatter plots of various chest and augmented ECG leads are obtained to examine the varying orientations of the ECG data in different quadrants, indicating the cardiac events and abnormalities. The efficacy of the PCA algorithm is tested on different leads of 12-channel ECG data; file no. 01 of the Common Standards for Electrocardiography (CSE) database is used for this study. Better feature extraction is obtained for some specific combinations of leads, and significant improvement in signal quality is achieved by identifying the noise and artifact components. The quadrant analysis discussed in this paper highlights the filtering requirements for further ECG processing after performing PCA, as a primary step for decorrelation and dimensionality reduction. The values of the parameters obtained from the results of PCA are also compared with those of wavelet methods.

CARDIAC ARRHYTHMIA DIAGNOSIS BY HRV SIGNAL PROCESSING USING PRINCIPAL COMPONENT ANALYSIS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240032 ◽  
Author(s):  
S. VINITHA SREE ◽  
DHANJOO N. GHISTA ◽  
KWAN-HOONG NG
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Nervous System ◽  
Standard Deviation ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Cardiac Cells ◽  
Ecg Signal ◽  
Mean Values

An electrocardiogram (ECG) signal represents the sum total of millions of cardiac cells' depolarization potentials. It helps to identify the cardiac health of the subject by inspecting its P-QRS-T wave. The heart rate variability (HRV) data, extracted from the ECG signal, reflects the balance between sympathetic and parasympathetic components of the autonomic nervous system. Hence, HRV signal contains information on the imbalance between these two nervous system components that results in cardiac arrhythmias. Thus in this paper, we have analyzed HRV signal abnormalities to determine and classify arrhythmias. The HRV signals are non-stationary and non-linear in nature. In this work, we have used continuous wavelet transform (CWT) coupled with principal component analysis (PCA) to extract the important features from the heart rate signals. These features are fed to the probabilistic neural network (PNN) classifier, for automated classification. Our proposed system demonstrates an average accuracy of 80% and sensitivity and specificity of 82% and 85.6%, respectively, for arrhythmia detection and classification. Our system can be operated on larger data sets. Our CWT–PCA analysis resulted in eigenvalues which constituted the HRV signal analysis parameters. We have shown and plotted the distribution of the parameters' mean values and the standard deviation for arrhythmia classification. We found some overlap in the distribution of these eigenvalue parameters for the different arrhythmia classes, which mitigates the effective use of these parameters to separate out the various arrhythmia classes. Therefore, we have formulated a HRV Integrated Index (HRVID) of these eigenvalues, and determined and plotted the mean values and standard deviation of HRVID for the various arrhythmia classifications. From this information, it can be seen that the HRVID is able to distinguish among the various arrhythmia classes. Hence, we have made a case for the employment of this HRVID as an index to effectively diagnose arrhythmia disorders.

MATLAB Implementation of Face Identification Using Principal Component Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 5402-5408
Author(s):  
Nasrul Humaimi Mahmood ◽  
Ismail Ariffin ◽  
Camallil Omar ◽  
Nur Sufiah Jaafar
Keyword(s):  
Principal Component Analysis ◽  
Minimum Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Face Identification ◽  
Human Face ◽  
Face Images ◽  
The Face ◽  
Matlab Implementation ◽  
Pca Algorithm

Face is the greatest superior biometric as the face has a complex, multidimensional and meaningful identity compared from one person to another. Face identification is executed by comparing the characteristics of the face (test image) with those of known individual images in the database. This paper describes the used of the Principal Component Analysis (PCA) algorithm for human face identification based on webcam image. The MATLAB is used as a tool for image processing and analysis. The important decision to identify the person is by the minimum distance of the face images and known face images in face space. From the results, it can be concluded that the work has successfully implemented the PCA algorithm for human face identification through a webcam.

Principal component analysis based on non-parametric maximum entropy

2010 ◽  
Vol 73 (10-12) ◽  
pp. 1840-1852 ◽  
Author(s):  
Ran He ◽  
Baogang Hu ◽  
XiaoTong Yuan ◽  
Wei-Shi Zheng
Keyword(s):  
Principal Component Analysis ◽  
Maximum Entropy ◽  
Principal Component ◽  
Component Analysis ◽  
Non Parametric

scholarly journals A Robust Procedure for Damage Detection from Strain Measurements Based on Principal Component Analysis

2013 ◽  
Vol 558 ◽  
pp. 128-138 ◽  
Author(s):  
Alfredo Guemes ◽  
J. Sierra-Pérez ◽  
J. Rodellar ◽  
L. Mujica
Keyword(s):  
Principal Component Analysis ◽  
Multivariable Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Complex Data ◽  
Data Set ◽  
Strain Measurements ◽  
Before And After ◽  
Small Cracks ◽  
Pca Algorithm

FBGs are excellent strain sensors, because of its low size and multiplexing capability. Tens to hundred of sensors may be embedded into a structure, as it has already been demonstrated. Nevertheless, they only afford strain measurements at local points, so unless the damage affects the strain readings in a distinguishable manner, damage will go undetected. This paper show the experimental results obtained on the wing of a UAV, instrumented with 32 FBGs, before and after small damages were introduced. The PCA algorithm was able to distinguish the damage cases, even for small cracks. Principal Component Analysis (PCA) is a technique of multivariable analysis to reduce a complex data set to a lower dimension and reveal some hidden patterns that underlie.

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