Arrhythmia detection based on the reduced features with K-SVD sparse coding algorithm

Shahsavani, Fatemeh; Nasiripour, Reza; Shakeri, Reza; Gholamrezaee, Alireza

doi:10.1007/s11042-022-13894-w

Cited by 3 publications

(6 citation statements)

References 35 publications

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“…The proposed approach has attained a remarkable accuracy rate of 95.24%, surpassing the performance of the existing model, exhibiting noticeable (i.e., 2.05%, 9.26%, 7.97%, 46.43%, 20.56%, 0.85%, 1.86%, and 3.44%) enhancement than the reference research work. Furthermore, the proposed approach has exhibited superior precision (100), sensitivity (89.47%), and specificity (100%) as compared to [8][9][10][11][12][13][14][15]. The comparison of the proposed approach with existing methods has been summarised in figure 8.…”

Section: Benchmarkingmentioning

confidence: 99%

“…Ayar et al (2023) [9] presented NSICA, a new multi-objective algorithm designed for optimal feature selection in arrhythmia classification, and demonstrated its use in both binary and multi-class tasks. Shahsavani et al (2023) [11] employed K-singular value decomposition (K-SVD), a sparse coding algorithm, for arrhythmia detection. To enhance classification performance, the extracted features were fed into a multi-layer perceptron (MLP) network.…”

Section: Literature Surveymentioning

confidence: 99%

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A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

Sharma,

Dhanka,

Kumar

et al. 2024

Eng. Res. Express

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Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within the heart, leading to irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is a significant cause of morbidity and mortality on a global scale. About 80% of deaths related to heart disease are caused by ventricular arrhythmias. This research investigated the application of an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated the model's performance on the arrhythmia dataset from the UCI ML repository with varying train-test splits (70:30, 80:20, and 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, and directory analysis were applied along with Pearson correlation for feature selection, all aimed at enhancing model performance. The proposed optimized RF model achieved impressive performance metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), and specificity (100%). Furthermore, the study compared the proposed approach to existing models, demonstrating significant improvements across various performance measures.

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Section: Benchmarkingmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

Sharma,

Dhanka,

Kumar

et al. 2024

Eng. Res. Express

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“…SVD helps to reduce the feature space's dimensionality, which can improve computational efficiency and reduce the risk of overfitting. The singular vectors obtained from SVD can provide insights into the principal components of the data, aiding in understanding the most influential features of gesture recognition [51][52][53]. For any m × n array A, there exist orthogonal arrays, as follows:…”

Section: Feature Extractionmentioning

confidence: 99%

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

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“…On the condition that 'm' is equal to 'n', the equation has one unique solution; and on the condition that 'm' is smaller than 'n', no unique solution is possible for the equation. Therefore, in order to achieve the specific solution, the following condition is considered for the equation [18]:…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Therefore, the closest solution to 𝑙 0 called 𝑙 1 -norm is used instead. Therefore, the problem becomes a convex optimization problem [18][19][20][21]:…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Algorithm of Predicting Heart Attack with using Sparse Coder

Mohamadzadeh,

Ghayedi,

Pasban

et al. 2023

IJE

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One of the most serious causes of disease in the world's population, which kills many people worldwide every year, is heart attack. Various factors are involved in this matter, such as high blood pressure, high cholesterol, abnormal pulse rate, diabetes, etc. Various methods have been proposed in this field, but in this article, by using sparse codes in the classification process, higher accuracy has been achieved in predicting heart attacks. The proposed method consists of two parts: preprocessing and sparse code processing. The proposed method is resistant to noise and data scattering because it uses a sparse representation for this purpose. The spars allow the signal to be displayed at its lowest value, which leads to improve computing speed and reduce storage requirements. To evaluate the proposed method, the Cleveland database has been used, which includes 303 samples and each sample has 76 features. Only 13 features are used in the proposed method. FISTA, AMP, DALM and PALM classifiers have been used for the classification process. The accuracy of the proposed method, especially with the PALM classifier, is the highest among other classifiers with 96.23%, and the other classifiers are 95.08%, 94.11% and 94.52% for DALM, AMP, FISTA, respectively.

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Arrhythmia detection based on the reduced features with K-SVD sparse coding algorithm

Cited by 3 publications

References 35 publications

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Algorithm of Predicting Heart Attack with using Sparse Coder

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