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.

scholarly journals Development of a Sensory Flavor Lexicon for Mushrooms and Subsequent Characterization of Fresh and Dried Mushrooms

Foods ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 980 ◽  
Author(s):  
SoonSil Chun ◽  
Edgar Chambers ◽  
Injun Han
Keyword(s):  
Principal Component Analysis ◽  
Food Industry ◽  
Principal Component ◽  
Component Analysis ◽  
Sensory Panel ◽  
Mean Values ◽  
Profile Method ◽  
Medicinal Properties ◽  
Selection Of

Mushrooms are a nutritious versatile ingredient in many food products. They are low in calories and have various potential medicinal properties as well. Surprisingly, little research on their descriptive sensory properties has been conducted. The objectives of this study were to a) establish a descriptive sensory flavor lexicon for the evaluation of fresh, dried, and powdered mushrooms and 2) use that lexicon to compare a selection of different mushrooms of various species and in fresh dried and powdered forms. A lexicon for describing mushroom was developed using a consensus profile method. A highly trained, descriptive sensory panel identified, defined, and referenced 27 flavor attributes for commercially available mushroom samples prepared as “meat” and broth. Attributes could be grouped in categories such as musty (dusty/papery, earthy/humus, earthy/damp, earthy/potato, fermented, leather (new), leather (old), mold/cheesy, moldy/damp, mushroomy), and other attributes such as fishy, shell fish, woody, nutty, brown, green, cardboard, burnt/ashy, potato, umami, protein (vegetable), yeasty, bitter, salty, sweet aromatics, sour, and astringent. Samples were then tested in three replications and mean values were compared statistically. In addition, principal component analysis was used to understand the characteristics of mushrooms evaluated. Dried mushrooms showed bitter, burnt, musty/dusty, astringent, old leather, and fresh mushroom characteristics and fresh mushroom showed umami, sweet, earthy/potato, earthy/damp, yeasty, and fermented. Mushrooms were grouped and differentiated in similar ways regardless of whether they were tested as broth or “meat”. Mushroom growers, product developers, chefs and other culinary professionals, sensory scientists, researchers, the food industry, and ultimately consumers will benefit from this lexicon describing a wide variety of mushroom flavor properties.

scholarly journals Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Wenjing Zhao ◽  
Yue Chi ◽  
Yatong Zhou ◽  
Cheng Zhang
Keyword(s):  
Principal Component Analysis ◽  
Standard Deviation ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Principal Component ◽  
Estimation Algorithm ◽  
Component Analysis ◽  
Noise Estimation ◽  
Noise Standard Deviation ◽  
Estimation Algorithms

SGK (sequential generalization of K-means) dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA) noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1) The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2) The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3) Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.

scholarly journals Improved Weighted Page Ranking Algorithm Based on Principal Component Analysis and Map Reduce Frame work for Web Access

2019 ◽  
Vol 8 (2) ◽  
pp. 32-39
Author(s):  
T. Mylsami ◽  
B. L. Shivakumar
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Web Mining ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Value ◽  
Ranking Algorithm ◽  
Mean Values ◽  
Page Ranking ◽  
The Web

In general the World Wide Web become the most useful information resource used for information retrievals and knowledge discoveries. But the Information on Web to be expand in size and density. The retrieval of the required information on the web is efficiently and effectively to be challenge one. For the tremendous growth of the web has created challenges for the search engine technology. Web mining is an area in which applies data mining techniques to deal the requirements. The following are the popular Web Mining algorithms, such as PageRanking (PR), Weighted PageRanking (WPR) and Hyperlink-Induced Topic Search (HITS), are quite commonly used algorithm to sort out and rank the search results. In among the page ranking algorithm uses web structure mining and web content mining to estimate the relevancy of a web site and not to deal the scalability problem and also visits of inlinks and outlinks of the pages. In recent days to access fast and efficient page ranking algorithm for webpage retrieval remains as a challenging. This paper proposed a new improved WPR algorithm which uses a Principal Component Analysis technique called (PWPR) based on mean value of page ranks. The proposed PWPR algorithm takes into account the importance of both the number of visits of inlinks and outlinks of the pages and distributes rank scores based on the popularity of the pages. The weight values of the pages is computed from the inlinks and outlinks with their mean values. But in PWPR method new data and updates are constantly arriving, the results of data mining applications become stale and obsolete over time. To solve this problem is a MapReduce (MR) framework is promising approach to refreshing mining results for mining big data .The proposed MR algorithm reduces the time complexity of the PWPR algorithm by reducing the number of iterations to reach a convergence point.

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.

scholarly journals Dynamic heart rate estimation using principal component analysis

2015 ◽  
Vol 6 (11) ◽  
pp. 4610 ◽  
Author(s):  
Yong-Poh Yu ◽  
P. Raveendran ◽  
Chern-Loon Lim ◽  
Ban-Hoe Kwan
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Rate Estimation ◽  
Heart Rate Estimation
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