Very deep feature extraction and fusion for arrhythmias detection

Amrani, Moussa; Hammad, Mohamed; Jiang, Feng; Wang, Kuanquan; Amrani, Amel

doi:10.1007/s00521-018-3616-9

Cited by 58 publications

(23 citation statements)

References 28 publications

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“…From the results in Table VI, it can be concluded that the proposed method matches or betters the other methods in terms of robustness for the dataset reported. As noted in our motivation for embarking on this study, while there are a good number of the machine or deep learning approaches for detecting heart disorders (such as arrhythmia) most of them are encumbered by the computational overhead associated with the complex frameworks utilized in their detection models [15,16,18,19,20,21,22,23]. Many of these frameworks are slow [20,21,22,23] while others are overburdened by memory-related constraints [15,16,17,18].…”

Section: E Comparison With the State-of-the-artsmentioning

confidence: 99%

“…Recently, focus on ECG rhythm (ECGr) classification has similarly been on the increase. ECGr classification can be grouped into areas that focus on finding effective extraction methods, [13,14] improving classification outcomes, [15][16][17][18][19] and utilization of deep learning methods to enhance the performance of classification [20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, deep learning is widely used in many fields [30][31][32], especially in medical fields [33][34][35]. For arrhythmia detection, several methods are presented [20][21][22][23][24][25][26][27][28][29]. However, these methods have many limitations that have overcome by using this paper.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

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165

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Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECGbased CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework.

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Section: E Comparison With the State-of-the-artsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

Self Cite

165

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“…In [42], the researcher proposed an approach for feature selection which was constructed on the arrangement of differential evolution and Support Vector Machine. In [43][44][45][46][47] researchers extracted features using machine learning techniques for skin cancer detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection

et al. 2020

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Melanoma is considered the most serious type of skin cancer. All over the world, the mortality rate is much high for melanoma in contrast with other cancer. There are various computer-aided solutions proposed to correctly identify melanoma cancer. However, the difficult visual appearance of the nevus makes it very difficult to design a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection. Existing systems either uses traditional machine learning models and focus on handpicked suitable features or uses deep learning-based methods that use complete images for feature learning. The automatic and most discriminative feature extraction for skin cancer remains an important research problem that can further be used to better deep learning training. Furthermore, the availability of the limited available images also creates a problem for deep learning models. From this line of research, we propose an intelligent Region of Interest (ROI) based system to identify and discriminate melanoma with nevus cancer by using the transfer learning approach. An improved k-mean algorithm is used to extract ROIs from the images. These ROI based approach helps to identify discriminative features as the images containing only melanoma cells are used to train system. We further use a Convolutional Neural Network (CNN) based transfer learning model with data augmentation for ROI images of DermIS and DermQuest datasets. The proposed system gives 97.9% and 97.4% accuracy for DermIS and DermQuest respectively. The proposed ROI based transfer learning approach outperforms existing methods that use complete images for classification.

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“…For their part, DLMs, such as CNNs [ 17 , 18 , 19 ] and ConvLSTM [ 20 , 21 , 22 ], have been widely applied in several medical fields as improvements to ML techniques. While ML techniques and DLMs may seem instinctive candidates in our present crusade to annihilate COVID-19, the absence of reliable data to exploit the “learnability” inherent to DLMs makes them palpable choices.…”

Section: Introductionmentioning

confidence: 99%

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Sedik

Iliyasu

El-Rahiem

et al. 2020

Viruses

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170

125

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This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

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Very deep feature extraction and fusion for arrhythmias detection

Cited by 58 publications

References 28 publications

A Multitier Deep Learning Model for Arrhythmia Detection

A Multitier Deep Learning Model for Arrhythmia Detection

Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

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