Arrhythmia Detection and Classification Using Wavelet and ICA

2008 ◽  
pp. 115-121
Author(s):  
Vahid R. Sabzevari ◽  
Asad Azemi ◽  
Morteza Khademi ◽  
Hossein Gholizade ◽  
Armin Kiani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Cardiac Arrhythmias ◽  
Extraction Process ◽  
Arrhythmia Detection ◽  
Learning Schemes ◽  
Simulation Scheme

The goal of this article is to optimize the feature extraction process by using ICA and wavelet transform, apply the obtained set to several different machine learning schemes, and compare their performances. The article is structured as follows. Section 2.0 describes our proposed method for cardiac arrhythmias detection. Section 3.0 covers an overview of different classifier types that were used in this work. Sections 4.0 and 5.0 summarize our simulation scheme and results. Finally, section 6.0 presents the concluding remarks.

scholarly journals Machine Learning Classification and Feature Extraction of Arrhythmic ECG Data

2020 ◽  
Vol 9 (2) ◽  
pp. 6-12
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Nearest Neighbor ◽  
Extraction Process ◽  
Support Vector ◽  
Ecg Signal ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Artificial Neural Network Ann

Electrocardiogram (ECG) is the analysis of the electrical movement of the heart over a period of time. The detailed information about the condition of the heart is measured by analyzing the ECG signal. Wavelet transform, fast Fourier transform are the different methods to disorganize cardiac disease. The paper elaborates the survey on ECG signal analysis and related study on arrhythmic and non arrhythmic data. Here we discuss the efficient feature extraction process for electrocardiogram, where based on position and priority six best P-QRS-T fragments are studied. This survey examines the the outcome of the system by using various Machine learning classification algorithms for feature extraction and analysis of ECG Signals. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN) are the most important algorithms used here for this purpose. There are several publicly available data sets which are used for arrhythmia analysis and among them MIT-BIH ECG-ID database is mostly used. The drawbacks and limitations are also discussed here and from there future challenges and concluding remarks can be done.

Feature Extraction

2017 ◽  
pp. 48-105
Author(s):  
Anindita Das Bhattacharjee
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Extraction Process ◽  
Measured Data ◽  
Data Set ◽  
Content Domain ◽  
Accessibility Problem ◽  
The Common

Accessibility problem is relevant for audiovisual information, where enormous data has to be explored and processed. Most of the solutions for this specific type of problems point towards a regular need of extracting applicable information features for a given content domain. And feature extraction process deals with two complicated tasks first deciding and then extracting. There are certain properties expected from good features-Repeatability, Distinctiveness, Locality, Quantity, Accuracy, Efficiency, and Invariance. Different feature extraction techniques are described. The chapter concentrates of taking a survey on the topic of Feature extraction and Image formation. Here both image and video are considered to have their feature extracted. In machine learning, pattern recognition and in image processing has significant contribution. The feature extraction is one of the common mechanisms involved in these two techniques. Extracting feature initiates from an initial data set of measured data and constructs derived informative values which are non redundant in nature.

scholarly journals 3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks

Healthcare ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 34
Author(s):  
Sabyasachi Chakraborty ◽  
Satyabrata Aich ◽  
Hee-Cheol Kim
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Extraction ◽  
Dopaminergic Neurons ◽  
Extraction Process ◽  
Machine Learning Algorithms ◽  
Subcortical Structures ◽  
Progressive Loss ◽  
Mri Scans

Parkinson’s disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease. Therefore, early detection of Parkinson’s disease is very important so that the progressive loss of dopaminergic neurons can be controlled to provide the patients with a better life. In this study, 3T T1-MRI scans were collected from 906 subjects, out of which, 203 are control subjects, 66 are prodromal subjects and 637 are Parkinson’s disease patients. To analyze the MRI scans for the detection of neurodegeneration and Parkinson’s disease, eight subcortical structures were segmented from the acquired MRI scans using atlas based segmentation. Further, on the extracted eight subcortical structures, feature extraction was performed to extract textural, morphological and statistical features, respectively. After the feature extraction process, an exhaustive set of 107 features were generated for each MRI scan. Therefore, a two-level feature extraction process was implemented for finding the best possible feature set for the detection of Parkinson’s disease. The two-level feature extraction procedure leveraged correlation analysis and recursive feature elimination, which at the end provided us with 20 best performing features out of the extracted 107 features. Further, all the features were trained using machine learning algorithms and a comparative analysis was performed between four different machine learning algorithms based on the selected performance metrics. And at the end, it was observed that artificial neural network (multi-layer perceptron) performed the best by providing an overall accuracy of 95.3%, overall recall of 95.41%, overall precision of 97.28% and f1-score of 94%, respectively.

scholarly journals Multi spectral classification and recognition of breast cancer and pneumonia

2020 ◽  
Vol 26 (1) ◽  
pp. 1-9
Author(s):  
Aditya Kakde ◽  
Nitin Arora ◽  
Durgansh Sharma ◽  
Subhash Chander Sharma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Process ◽  
Spectral Classification ◽  
Important Process ◽  
Specific Disease ◽  
Fully Connected

AbstractAccording to the Google I/O 2018 key notes, in future artificial intelligence, which also includes machine learning and deep learning, will mostly evolve in healthcare domain. As there are lots of subdomains which come under the category of healthcare domain, the proposed paper concentrates on one such domain, that is breast cancer and pneumonia. Today, just classifying the diseases is not enough. The system should also be able to classify a particular patient’s disease. Thus, this paper shines the light on the importance of multi spectral classification which means the collection of several monochrome images of the same scene. It can be proved to be an important process in the healthcare areas to know if a patient is suffering from a specific disease or not. The convolutional layer followed by the pooling layer is used for the feature extraction process and for the classification process; fully connected layers followed by the regression layer are used.

scholarly journals Candidate Feature Extraction and Categorization for Unstructured Text Document

2020 ◽  
pp. 81-87
Author(s):  
Prajakta P Shelke ◽  
Aditya A Pardeshi
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Extraction Process ◽  
Grammatical Structure ◽  
Part Of Speech ◽  
Text Document ◽  
Unstructured Text ◽  
Input Sentence ◽  
Crucial Information ◽  
Candidate Feature

In the phrases words contains crucial information which helps in feature extraction process. The established techniques for such has huge problem and has limitations in feature extraction process and also it ignores the grammatical structure for the phrases. So results as poor features get extracted. So to overcome this problem a system is proposed which is based on generation of parse tree for the input sentence and cut down into sub-tree subsequently. The branches of the tree are extracted using part-of-speech (POS) labelling intended for candidate phrase. To stay away from redundant phrases filtering is recommended. Finally machine learning is used for the Feature categorization progression. The result illustrates the effectiveness of the approach.

Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

2015 ◽  
Vol 38 (1) ◽  
pp. 139-149 ◽  
Author(s):  
Hafeez Ullah Amin ◽  
Aamir Saeed Malik ◽  
Rana Fayyaz Ahmad ◽  
Nasreen Badruddin ◽  
Nidal Kamel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Machine Learning Techniques ◽  
Eeg Signals ◽  
Learning Techniques

Diagnostic Framework for Automatic Classification and Visualization of Alzheimer’s Disease with Feature Extraction Using Wavelet Transform

2021 ◽  
Vol 19 (7) ◽  
pp. 84-95
Author(s):  
M. Anitha ◽  
V. Karpagam ◽  
P. Tamije Selvy
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Human Brain ◽  
Parallel Implementation ◽  
Normal Brain

Alzheimer’s Disease (AD) is a serious disease that destroys brain and is classified as the most widespread type of dementia. Manual evaluation of image scans relies on visual reading and semi-quantitative investigation of various human brain sections, leading to wrong diagnoses. Neuroimaging plays a significant part in AD detection, using image processing approaches that succeed the drawback of traditional diagnosis methods. Feature extraction is done through Wavelet Transform (WT). Feature selection is an important step in machine learning, where best features set from all possible features is determined. Mutual Information based feature selection (MI) and Correlation-based Feature Selection (CFS) captures the ‘correlation’ between random variables. Machine Learning techniques are broadly used in a classification problem, as it is simple, effective mechanisms and capability to train to contribute intelligence to the arrangement. Classifiers used in this proposed work are Artificial Neural Network (ANN), Random Forest, Convolutional Neural Network (CNN), and Wavelet-based CNN. The superior ability of ANN is high-speed processing achieved through extensive parallel implementation, and this has emphasized necessity of research in this field. CNN has encouraged tackling this issue. This work proves that wavelet-based CNN performs better with a classification accuracy of 91.87%, the sensitivity of 0.94 for normal brain and 0.88 for AD affected brain, the positive predictive value of 0.91 for normal brain and 0.92 for AD affected brain, and F measure of 0.92 for normal brain and 0.90 for AD affected brain on ADNI MRI dataset of the human brain in detecting AD.

scholarly journals Feature Extraction of Time-Series Data Using DWT and FFT for Ballscrew Condition Monitoring

2021 ◽  
Author(s):  
Nurudeen Alegeh ◽  
Munavar Thottoli ◽  
Naeem Mian ◽  
Andrew Longstaff ◽  
Simon Fletcher
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Time Series Data ◽  
Binary Classification ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Health State

This paper investigates the use of the discrete wavelet transform (DWT) and Fast Fourier Transform (FFT) to improve the quality of extracted features for machine learning. The case study in this paper is detecting the health state of the ballscrew of a gantry type machine tool. For the implementation of the algorithm for feature extraction, wavelet is first applied to the data, followed by FFT and then useful features are extracted from the resultant signal. The extracted features were then used in various machine learning algorithms like decision tree, K-nearest neighbour (KNN) and support vector machine (SVM) for binary classification of the ballscrew state. The result shows significant improvement in the classification accuracy after the wavelet transform and FFT has been performed on the data.

scholarly journals Training AI-Based Feature Extraction Algorithms, for Micro CT Images, Using Synthesized Data

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Matthew Konnik ◽  
Bahar Ahmadi ◽  
Nicholas May ◽  
Joseph Favata ◽  
Zahra Shahbazi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Learning Algorithms ◽  
Extraction Process ◽  
Machine Learning Algorithms ◽  
Ct Image ◽  
X Ray ◽  
3D Object ◽  
Automated Feature Extraction ◽  
Position And Orientation

AbstractX-ray computed tomography (CT) is a powerful technique for non-destructive volumetric inspection of objects and is widely used for studying internal structures of a large variety of sample types. The raw data obtained through an X-ray CT practice is a gray-scale 3D array of voxels. This data must undergo a geometric feature extraction process before it can be used for interpretation purposes. Such feature extraction process is conventionally done manually, but with the ever-increasing trend of image data sizes and the interest in identifying more miniature features, automated feature extraction methods are sought. Given the fact that conventional computer-vision-based methods, which attempt to segment images into partitions using techniques such as thresholding, are often only useful for aiding the manual feature extraction process, machine-learning based algorithms are becoming popular to develop fully automated feature extraction processes. Nevertheless, the machine-learning algorithms require a huge pool of labeled data for proper training, which is often unavailable. We propose to address this shortage, through a data synthesis procedure. We will do so by fabricating miniature features, with known geometry, position and orientation on thin silicon wafer layers using a femtosecond laser machining system, followed by stacking these layers to construct a 3D object with internal features, and finally obtaining the X-ray CT image of the resulting 3D object. Given that the exact geometry, position and orientation of the fabricated features are known, the X-ray CT image is inherently labeled and is ready to be used for training the machine learning algorithms for automated feature extraction. Through several examples, we will showcase: (1) the capability of synthesizing features of arbitrary geometries and their corresponding labeled images; and (2) use of the synthesized data for training machine-learning based shape classifiers and features parameter extractors.

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