Electrocardiography (ECG) analysis and a new feature extraction method using wavelet transform with scalogram analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hüseyin Yanık ◽  
Evren Değirmenci ◽  
Belgin Büyükakıllı ◽  
Derya Karpuz ◽  
Olgu Hallıoğlu Kılınç ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Correct Diagnosis ◽  
Statistical Investigation ◽  
Massachusetts Institute Of Technology ◽  
Ecg Analysis ◽  
Feature Extraction Method ◽  
Ecg Signals ◽  
Detection Analysis ◽  
Institute Of Technology ◽  
New Feature

AbstractElectrocardiography (ECG) signals and the information obtained through the analysis of these signals constitute the main source of diagnosis for many cardiovascular system diseases. Therefore, accurate analyses of ECG signals are very important for correct diagnosis. In this study, an ECG analysis toolbox together with a user-friendly graphical user interface, which contains the all ECG analysis steps between the recording unit and the statistical investigation, is developed. Furthermore, a new feature calculation methodology is proposed for ECG analysis, which carries distinct information than amplitudes and durations of ECG main waves and can be used in artificial intelligence studies. Developed toolbox is tested using both Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia ECG Database and an experimentally collected dataset for performance evaluation. The results show that ECG analysis toolbox presented in this study increases the accuracy and reliability of the ECG main wave detection analysis, highly fasten the process duration compared to manual ones and the new feature set can be used as a new parameter for decision support systems about ECG based on artificial intelligence.

scholarly journals ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform

2020 ◽  
Vol 10 (14) ◽  
pp. 4741
Author(s):  
Shu Liu ◽  
Jie Shao ◽  
Tianjiao Kong ◽  
Reza Malekian
Keyword(s):  
Fourier Transform ◽  
Heart Diseases ◽  
High Order ◽  
Support Vector ◽  
Massachusetts Institute Of Technology ◽  
Feature Extraction Method ◽  
Detection Analysis ◽  
Incidence And Mortality ◽  
Institute Of Technology ◽  
Ecg Arrhythmia Classification

Heart diseases are in the front rank among several kinds of life threats, due to its high incidence and mortality. Regarded as a powerful tool in the diagnosis of the cardiac disorder and arrhythmia detection, analysis of electrocardiogram (ECG) signals has become the focus of numerous researches. In this study, a feature extraction method based on the bispectrum and 2D graph Fourier transform (GFT) was developed. High-order matrix founded on bispectrum are extended into structured datasets and transformed into the eigenvalue spectrum domain by GFT, so that features can be extracted from statistical quantities of eigenvalues. Spectral features have been computed to construct the feature vector. Support vector machine based on the radial basis function kernel (SVM-RBF) was used to classify different arrhythmia heartbeats downloaded from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) Arrhythmia Database, according to the Association for the Advancement of Medical Instrumentation (AAMI) standard. Based on the cross-validation method, the experimental results depicted that our proposed model, the combination of bispectrum and 2D-GFT, achieved a high classification accuracy of 96.2%.

ELECTROCARDIOGRAM BEAT CLASSIFICATION USING S-TRANSFORM BASED FEATURE SET

2014 ◽  
Vol 14 (05) ◽  
pp. 1450066 ◽  
Author(s):  
MANAB KUMAR DAS ◽  
SAMIT ARI
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Massachusetts Institute Of Technology ◽  
Ecg Signals ◽  
Beat Classification ◽  
Temporal Features ◽  
S Transform ◽  
Institute Of Technology ◽  
Stockwell Transform

In this paper, the conventional Stockwell transform is effectively used to classify the ECG arrhythmias. The performance of ECG classification mainly depends on feature extraction based on an efficient formation of morphological and temporal features and the design of the classifier. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not selected properly. Here, the S-transform (ST) is used to extract the morphological features which is appended with temporal features. This feature set is independently classified using artificial neural network (NN) and support vector machine (SVM). In this work, five classes of ECG beats (normal, ventricular, supra ventricular, fusion and unknown beats) from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database are classified according to AAMI EC57 1998 standard (Association for the Advancement of Medical Instrumentation). Performance is evaluated on several normal and abnormal ECG signals of MIT-BIH arrhythmias database using two classifier techniques: ST with NN classifier (ST-NN) and other proposed ST with SVM classifier (ST-SVM). The proposed method achieves accuracy of 98.47%. The performance of the proposed technique is compared with ST-NN and earlier reported technique.

scholarly journals Feature Extraction of ECG Signals using NI LabVIEW Biomedical Workbench and Classification with Artificial Neural Network

2019 ◽  
pp. 34-38
Author(s):  
Ebru Sayilgan ◽  
Savas Sahin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Beat ◽  
Massachusetts Institute Of Technology ◽  
Data Set ◽  
Ecg Signals ◽  
Multiple Classification ◽  
Sensitivity And Selectivity ◽  
Artificial Neural ◽  
Institute Of Technology

In this study, a data set containing normal and different heart beat types recorded by the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) was used for the detection of cardiac dysfunctions. In this data set, features were extracted using the LabVIEW Biomedical Workbench from the normal heartbeat and six different arrhythmia types. Obtained signals were evaluated by using Artificial Neural Network multiple classification method. Classification performances were compared before extracting the feature on the same data set. Classifier performances were evaluated by accuracy, sensitivity and selectivity performances criteria of classification. In the classifier performances, the "Normal" beat rate was found to be 99% accurate with the highest success compared to other arrhythmia types. As a result, both analysis methods are successful, but when the LabVIEW Biomedical Workbench is used, the classification results have achieved higher success.

The Pransky interview: Dr Aaron Edsinger, CEO and cofounder at Hello Robot Inc

2018 ◽  
Vol 45 (6) ◽  
pp. 710-714
Author(s):  
Joanne Pransky
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
San Francisco ◽  
Systems Engineering ◽  
Industrial Robot ◽  
Humanoid Robots ◽  
Massachusetts Institute Of Technology ◽  
Content Type ◽  
Institute Of Technology ◽  
Artificial Intelligence Laboratory

Purpose The purpose of this paper is to present a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned entrepreneur regarding the evolution, commercialization and challenges of bringing a technological invention to market. Design/methodology/approach The interviewee is Dr Aaron Edsinger, a proven entrepreneur and inventor in the field of human-collaborative robotics. Dr Edsinger shares his journey that led him from developing humanoids at Rodney Brooks’ Computer Science and Artificial Intelligence Laboratory at MIT, to cofounding four companies, two of which got purchased by Google. Findings Dr Edsinger received a BS degree in Computer Systems Engineering from Stanford, an MS in Computer Science from the Massachusetts Institute of Technology (MIT) and a PhD in Computer Science from MIT and did post-doctorate research in the Humanoid Robotics Group at the MIT Computer Science and Artificial Intelligence Lab. He co-founded his first company Meka Robotics in 2007 and that same year, he started his second company, HStar Technologies. In 2011, he cofounded Redwood Robotics, and in 2013, he sold Meka and Redwood to Google. From 2013 to 2017, he was a Robotics Director at Google. In August of 2017, he cofounded Hello Robot Inc. Originality/value Dr Edsinger’s work in robotics grew out of the San Francisco robotic art scene in the 1990s. Since then, he has collaborated and built over a dozen research and artistic robot platforms and has been granted 28 patents. His world-class robotic systems encompass Dr Edsinger’s innovative research in dexterous manipulation in unstructured environments, force controlled compliant actuation, human safe robotics, integrated mechatronic engineering and the design of humanoid robots. Domo, the humanoid robot he built, was named one of Time magazine’s Best Inventions of the Year for 2007. Out of the eight robot companies Google purchased in 2013, two were cofounded by Dr Edsinger. In 2017, Dr Edsinger left Google to cofound his new company, Hello Robot Inc, a stealth mode consumer robot company.

scholarly journals A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 951
Author(s):  
Amin Ullah ◽  
Sadaqat ur Rehman ◽  
Shanshan Tu ◽  
Raja Majid Mehmood ◽  
Fawad ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Vital Role ◽  
Massachusetts Institute Of Technology ◽  
One Dimensional ◽  
Ecg Signals ◽  
Electrocardiogram Signal ◽  
Deep Cnn ◽  
Institute Of Technology ◽  
Fully Connected ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.

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