scholarly journals Detecting Cardiac Abnormalities from 12-lead ECG Signals Using Feature Extraction, Dimensionality Reduction, and Machine Learning Classification

2020 ◽  
Author(s):  
Garrett Perkins ◽  
J. Chase McGlinn ◽  
Muhammad Rizwan ◽  
Bradley Whitaker
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Machine Learning Classification ◽  
Ecg Signals ◽  
Cardiac Abnormalities

scholarly journals Music Feature Extraction and Classification Algorithm Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jingwen Zhang
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Large Scale ◽  
Rapid Development ◽  
Audio Signal ◽  
Classification Model ◽  
Machine Learning Classification ◽  
Gating Mechanism ◽  
Music Information ◽  
Sound Spectrum

With the rapid development of information technology and communication, digital music has grown and exploded. Regarding how to quickly and accurately retrieve the music that users want from huge bulk of music repository, music feature extraction and classification are considered as an important part of music information retrieval and have become a research hotspot in recent years. Traditional music classification approaches use a large number of artificially designed acoustic features. The design of features requires knowledge and in-depth understanding in the domain of music. The features of different classification tasks are often not universal and comprehensive. The existing approach has two shortcomings as follows: ensuring the validity and accuracy of features by manually extracting features and the traditional machine learning classification approaches not performing well on multiclassification problems and not having the ability to be trained on large-scale data. Therefore, this paper converts the audio signal of music into a sound spectrum as a unified representation, avoiding the problem of manual feature selection. According to the characteristics of the sound spectrum, the research has combined 1D convolution, gating mechanism, residual connection, and attention mechanism and proposed a music feature extraction and classification model based on convolutional neural network, which can extract more relevant sound spectrum characteristics of the music category. Finally, this paper designs comparison and ablation experiments. The experimental results show that this approach is better than traditional manual models and machine learning-based approaches.

scholarly journals Info_PCA: A Hybrid Technique to Improve Accuracy by Dimensionality Reduction

2021 ◽  
Vol 9 (2) ◽  
pp. 458-466
Author(s):  
Surabhi Lingwal, Et. al.
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Shannon Entropy ◽  
Information Gain ◽  
Principal Component ◽  
Hybrid Technique ◽  
Significant Information ◽  
Feature Vectors ◽  
Feature Extraction And Selection

Principal Component Analysis and Shannon Entropy are some of the most widely used methods for feature extraction and selection. PCA reduces the data to a new subspace with low dimensions by calculating the eigenvectors from eigenvalues out of a covariance matrix and thereby reduces the features to a smaller number capturing the significant information. Shannon entropy is based on probability distribution to calculate the significant information content. Information gain shows the importance of a given attribute in the set of feature vectors. The paper has introduced a hybrid technique Info_PCA which captures the properties of Information gain and PCA that overall reduces the dimensionality and thereby increases the accuracy of the machine learning technique. It also demonstrates the individual implementation of Information gain for feature selection and PCA for dimensionality reduction on two different datasets collected from the UCI machine learning repository. One of the major aims is to determine the important attributes in a given set of training feature vectors to differentiate the classes. The paper has shown a comparative analysis on the classification accuracy obtained by the application of Information Gain, PCA and Info_PCA applied individually on the two different datasets for feature extraction followed by ANN classifier where the results of hybrid technique Info_PCA achieves maximum accuracy and minimum loss in comparison to other feature extraction techniques.

scholarly journals Comparative Analysis of Machine Learning Algorithms with and without Feature Extraction

2020 ◽  
Vol 6 (12) ◽  
pp. 235-239
Author(s):  
Vatsal Gupta and Saurabh Gautam
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Image Recognition ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Machine Learning Classification ◽  
Security Services ◽  
Computational Resources

Image recognition is one of the core disciplines in Computer Vision. It is one of the most widely researched topics of the last few decades. Many advances in image recognition in the past decade, has made it one of the most efficient and powerful disciplines of all, having its applications in every sector including Finance, Healthcare, Security services, Agriculture and many more. Feature extraction is an integral part of image recognition. It helps in training the model more efficiently and with a higher accuracy, by getting rid of any unwanted or unnecessary features, thus reducing the dimensionality of the input image. This also helps in reducing the computational resources required by the algorithm to train, thus making it affordable for people with low end setups. Here we compare the accuracies of different machine learning classification algorithms, and their training times, with and without using feature Extraction. For the purpose of extracting features, a convolutional neural network was used. The model was trained and tested on the data of 12 classes containing a total of 2,175 images. For comparisons, we chose the Logistic regression, K-Nearest Neighbors Classifier, Random forest Classifier, and Support Vector Machine Classifier.

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.

scholarly journals Feature extraction of electrocardiogram signal using machine learning classification

2020 ◽  
Vol 10 (6) ◽  
pp. 6598
Author(s):  
Sumanta Kuila ◽  
Namrata Dhanda ◽  
Subhankar Joardar
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Feature Extraction ◽  
Mathematical Models ◽  
Virtual Worlds ◽  
Computer Programs ◽  
Machine Learning Classification ◽  
Movement Prediction ◽  
Electrocardiogram Signal

In this article, we'll introduce ways to build virtual worlds through different computer programs. We will show the method of rectangles for analyzing data obtained from the electroencephalogram. We will demonstrate basic mathematical models for movement prediction in a system of virtual reality. Using this data, the main transformations are possible-change of position and rotation (change of orientation).

scholarly journals Opening the black box: interpretability of machine learning algorithms in electrocardiography

2021 ◽  
Vol 379 (2212) ◽  
Author(s):  
Matteo Bodini ◽  
Massimo W. Rivolta ◽  
Roberto Sassi
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
P Wave ◽  
Qrs Complex ◽  
Ecg Signals ◽  
T Wave ◽  
Cardiac Abnormalities ◽  
Challenges And Opportunities ◽  
Clinically Significant ◽  
Electrocardiogram Ecg

Recent studies have suggested that cardiac abnormalities can be detected from the electrocardiogram (ECG) using deep machine learning (DL) models. However, most DL algorithms lack interpretability, since they do not provide any justification for their decisions. In this study, we designed two new frameworks to interpret the classification results of DL algorithms trained for 12-lead ECG classification. The frameworks allow us to highlight not only the ECG samples that contributed most to the classification, but also which between the P-wave, QRS complex and T-wave, hereafter simply called ‘waves’, were the most relevant for the diagnosis. The frameworks were designed to be compatible with any DL model, including the ones already trained. The frameworks were tested on a selected Deep Neural Network, trained on a publicly available dataset, to automatically classify 24 cardiac abnormalities from 12-lead ECG signals. Experimental results showed that the frameworks were able to detect the most relevant ECG waves contributing to the classification. Often the network relied on portions of the ECG which are also considered by cardiologists to detect the same cardiac abnormalities, but this was not always the case. In conclusion, the proposed frameworks may unveil whether the network relies on features which are clinically significant for the detection of cardiac abnormalities from 12-lead ECG signals, thus increasing the trust in the DL models. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

Sign in / Sign up

Export Citation Format

Share Document