scholarly journals Myocardial infarction detection using artificial intelligence and nonlinear features based on synthesis of the standard 12-lead and Frank XYZ leads

2020 ◽  
Author(s):  
Wei Zeng ◽  
Zixiang Lin ◽  
Chengzhi Yuan
Keyword(s):  
Artificial Intelligence ◽  
Myocardial Infarction ◽  
Phase Space ◽  
System Dynamics ◽  
Automatic Detection ◽  
Discrete Wavelet ◽  
Potential Candidate ◽  
Detection Techniques ◽  
Cardiac System ◽  
Cardiac Vector

Abstract Nowadays cardiovascular diseases ( CVD ) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention of CVD . Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgment and several electrocardiographic (ECG) or vectorcardiographic ( VCG ) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. This vector is decomposed into a series of proper rotation components ( PRCs ) by using the intrinsic time-scale decomposition ( ITD ) method. Second, four levels discrete wavelet transform ( DWT ) is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies ( db3 ) wavelet function is selected as reference variable for analysis. Third, phase space of the reference variable is reconstructed based on db3 , in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional ( 3D ) phase space reconstruction ( PSR ) together with Euclidean distance (ED) has been utilized to derive features. Fourth, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 98.20 % . The result verifies the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

Deep Learning Based Adaptive Recurrent Neural Network for Detection of Myocardial Infarction

2021 ◽  
Vol 11 (12) ◽  
pp. 3044-3053
Author(s):  
Rakesh Kumar Mahendran ◽  
V. Prabhu ◽  
V. Parthasarathy ◽  
A. Mary Judith
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Training Data ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Detection Techniques

Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.

ID: 3525204 ARTIFICIAL INTELLIGENCE AND VIDEO CAPSULE COLONOSCOPY: AUTOMATIC DETECTION OF BLOOD IN COLONIC LUMEN USING A CONVOLUCIONAL NEURAL NETWORK

2021 ◽  
Vol 93 (6) ◽  
pp. AB190-AB191
Author(s):  
João Afonso ◽  
Miguel M. Saraiva ◽  
Helder Cardoso ◽  
João Ferreira ◽  
Patrícia Andrade ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Automatic Detection ◽  
Colonic Lumen

scholarly journals Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wenzhi Zhang ◽  
Runchuan Li ◽  
Shengya Shen ◽  
Jinliang Yao ◽  
Yan Peng ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Clinical Decision Making ◽  
Human Life ◽  
Principal Component ◽  
Fusion Rule ◽  
Clinical Decision ◽  
Gradient Boosting ◽  
Discrete Wavelet ◽  
Extreme Gradient Boosting ◽  
Ventricular Activity

Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.

The association between reconstructed phase space and Artificial Neural Networks for vectorcardiographic recognition of myocardial infarction

2018 ◽  
Vol 51 (3) ◽  
pp. 443-449 ◽  
Author(s):  
Cecília M. Costa ◽  
Ittalo S. Silva ◽  
Rafael D. de Sousa ◽  
Renato A. Hortegal ◽  
Carlos Danilo M. Regis
Keyword(s):  
Myocardial Infarction ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Phase Space ◽  
Reconstructed Phase Space ◽  
Artificial Neural

Real-time Mental Stress Detection Technique Using Neural Networks towards Wearable Health Monitor

2021 ◽  
Author(s):  
Nilava Mukherjee ◽  
Sumitra Mukhopadhyay ◽  
Rajarshi Gupta
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Mental Stress ◽  
Detection Technique ◽  
Physiological Signals ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Optimum Number ◽  
Stress Detection ◽  
Detection Techniques

Abstract Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

Artificial Intelligence in Kidney Transplantation: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Mowafa Househ ◽  
Asma Alamgir ◽  
Yasmin Abdelaal ◽  
Hagar Hussein
Keyword(s):  
Artificial Intelligence ◽  
Kidney Transplantation ◽  
Kidney Transplant ◽  
Data Extraction ◽  
Care Plan ◽  
Reference List ◽  
Potential Candidate ◽  
Transplant Patients ◽  
Study Selection ◽  
Standardized Practice

BACKGROUND Artificial Intelligence technologies and big data have been increasingly used to enhance kidney transplant experts’ ability to make critical decisions and manage the care plan for their patients. OBJECTIVE To explore the use of AI technologies in the field of kidney transplantation as reported in the literature. METHODS Embase, CINAHL, PubMed and Google Scholar were used in the search. Backward reference list checking of included studies was also conducted. Study selection and data extraction was done independently by three reviewers. Data extracted was synthesized in a narrative approach. RESULTS Of 505 citations retrieved from the databases, 33 unique studies are included in this review. Artificial intelligence (AI) technologies in the included studies were used to help with diagnosis (n= 16), used as a prediction tool (n=15) and, also for supporting appropriate prescription for kidney transplant patients (n = 2). The population who benefited from the technique included patients who underwent kidney transplantation procedure (n = 24) and those who are potential candidate (n=6). The most prominent AI branch used in kidney transplantation care was machine learning with Random Forest (n=11) being the most used AI model, followed by Linear Regression (n=6). CONCLUSIONS Conclusion: AI is extensively being used in the field of kidney transplant. However, there is a gap in research on the limitation and obstacles associated with implementing AI technologies in kidney transplant. There is a need for more research to identify educational needs and standardized practice for clinicians who wish to apply AI technologies in critical transplantation-related decisions.

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