Multi-Class Cardiovascular Diseases Diagnosis from Electrocardiogram Signals using 1-D Convolution Neural Network

Fasihi, Mehdi; Nadimi-Shahraki, Mohammad H.; Jannesari, Ali

doi:10.1109/iri49571.2020.00060

Cited by 10 publications

(8 citation statements)

References 25 publications

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“…It occurs when the number of instances for different classes are significantly out of proportion. The minority classes with fewer instances usually contain the essential information, which has been observed in broad application areas, such as medical diagnosis [ 1 , 2 , 3 , 4 , 5 , 6 ], sentiment or image classification [ 7 , 8 ], fault identification [ 9 , 10 ], etc. Many typical classifiers may generate unsatisfactory results due to a concentration on global accuracy while ignoring the identification performance for minority samples.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments’ results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.

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Section: Introductionmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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“…Deep learning is a sub-branch of ANN, which includes various methods such as deep belief network (DBN) [16], deep neural network (DNN) [17], recurrent neural network (RNN) [18], deep autoencoder (DA) [19] and convolution neural network (CNN). The CNN can be divided into three different architectures, including 1-D CNN [9], 2-D CNN [20], and 3-D Fig. 1 Classification of clinical decision support system methods CNN [21] for CDD.…”

Section: Related Workmentioning

confidence: 99%

“…This research is an extension of our previous work [9] in which we have introduced a deep architecture of 1-D CNN based on four convolution layers, three pooling layers, and three fully connected layers to diagnose arrhythmia diseases automatically. However, in this research, we propose a new shallow architecture of 1-D CNN to improve fetal state assessment accuracy.…”

Section: Introductionmentioning

confidence: 95%

A Shallow 1-D Convolution Neural Network for Fetal State Assessment Based on Cardiotocogram

2021

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Cardiotocography consists of fetal heart rate and uterine contraction signals that have been utilized for fetal well-being assessment. Researchers have applied several machine learning methods to improve the classification accuracy of the fetal state assessment. However, the proposed methods do not fulfill the required accuracy, as they have to address signal challenges such as missing value and external noise. Recently, convolutional neural networks have been brought to researchers' attention to cope with the challenges above in other machine learning applications. In this article, a new shallow architecture of 1-D convolution neural network is proposed to enhance fetal state assessment accuracy. This architecture has performed based on one convolution layer, resulting in computational complexity reduction. Besides, pooling operation that is a standard part of traditional CNN is not applied in this architecture to have more features in the classification phase. The performance of the proposed architecture is evaluated using five different clinical data sets. The results show that the proposed architecture is more efficient than traditional 1-D CNN and five implemented classifiers. The proposed architecture also achieves very competitive accuracy in the fetal state assessment compared to previous researches.

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“…In this regard, various studies have been conducted based on the use of different deep learning models for text processing, such as Convolutional Neural Networks (CNN) [6], LSTM [7], and Recursive Neural Network (RNN) [8]. CNN uses convolution and pooling layers for classification and has a variety of applications [9,10]. The task of deep learning algorithms is to build a learning model that effectively predicts the possible set of labels of unknown objects [11].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

et al. 2022

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An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively.

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Multi-Class Cardiovascular Diseases Diagnosis from Electrocardiogram Signals using 1-D Convolution Neural Network

Cited by 10 publications

References 25 publications

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

A Shallow 1-D Convolution Neural Network for Fetal State Assessment Based on Cardiotocogram

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

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