A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals

Sharma, Pooja; Dinkar, Shail Kumar; Gupta, Deep

doi:10.1007/s00521-021-06005-7

Cited by 43 publications

(25 citation statements)

References 41 publications

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“…The performance analysis of the proposed model is carried and the outcomes attained are compared with existing methods ( Latif et al., 2022 ), ( Khan et al., 2021 ), ( Yang et al., 2017 ) and ( Sharma et al., 2021b ) to validate the efficiency of proposed methodology. The outcomes attained from the analysis is projected below in the table and graphical representation.…”

Section: Performance Analysismentioning

confidence: 99%

A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms

Dahan

Alroobaea

et al. 2023

Front. Physiol.

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Generally, cloud computing is integrated with wireless sensor network to enable the monitoring systems and it improves the quality of service. The sensed patient data are monitored with biosensors without considering the patient datatype and this minimizes the work of hospitals and physicians. Wearable sensor devices and the Internet of Medical Things (IoMT) have changed the health service, resulting in faster monitoring, prediction, diagnosis, and treatment. Nevertheless, there have been difficulties that need to be resolved by the use of AI methods. The primary goal of this study is to introduce an AI-powered, IoMT telemedicine infrastructure for E-healthcare. In this paper, initially the data collection from the patient body is made using the sensed devices and the information are transmitted through the gateway/Wi-Fi and is stored in IoMT cloud repository. The stored information is then acquired, preprocessed to refine the collected data. The features from preprocessed data are extracted by means of high dimensional Linear Discriminant analysis (LDA) and the best optimal features are selected using reconfigured multi-objective cuckoo search algorithm (CSA). The prediction of abnormal/normal data is made by using Hybrid ResNet 18 and GoogleNet classifier (HRGC). The decision is then made whether to send alert to hospitals/healthcare personnel or not. If the expected results are satisfactory, the participant information is saved in the internet for later use. At last, the performance analysis is carried so as to validate the efficiency of proposed mechanism.

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Section: Performance Analysismentioning

confidence: 99%

A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms

Dahan

Alroobaea

et al. 2023

Front. Physiol.

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“…where function S(v) is a sigmoid limiting transformation and value of rand() is derived from U(0,1). We use a novel formula that can be denoted as follows to determine the binary bit pxij by eqn (20):…”

Section: Algorithm For Svm_bementioning

confidence: 99%

“…It is important to note that the tests were conducted using a variety of ECG signals, and the given model correctly identified atrial fibrillation. NCBAM in a novel CNN [20] with an emphasis on representative characteristics along space, time, and channels was utilised to study cardiac arrhythmias and cardiovascular illnesses.…”

Section: Background Studymentioning

confidence: 99%

ECG signal processing by feature selection with classification based on machine learning architectures in cardiovascular disease detection

Qamar

2022

Preprint

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A typical method for capturing electrical activity of human heart is electrocardiography (ECG). Because of its low amplitude and extreme susceptibility to high frequency noise and other distortions, accurate computer analysis of ECG signals is difficult. This paper's main goal is to provide machine learning methods for diagnosing myocardial infarction and differentiating between arrhythmias, hypertrophy, and heart enlargement. This research propose novel technique in ECG signal processing based on machine learning architectures by feature extraction with classification. Here the input ECG signal is processed for noise removal, smoothening and normalization; signals has been obtained as frames. Then this signal feature has been selected using support vector machine integrated with backward elimination architecture. the selected features of ECG signal is classified using probabilistic particle swarm optimization. From the classified ECG signal the type of cardio vascular disease detected. The experimental analysis is carried out for different dataset in which the proposed technique is analysed in terms of accuracy, precision, recall, F-measure, RMSE.

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“…Recently, BPN still remains one of the popular machine learning methods or an important base for different AI applications, including predicting or classifying many health and medical events (Ref. [50][51][52][53][54][55]), such as BPN associated models for predicting medical expenses and all-cause risk of 30-day readmission, detecting of COVID-19 disease and ischemic stroke, diagnosing hypertrophic cardiomyopathy and hypertensive heart disease, classifying arrythmia disease (with ECG signals), etc.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Wu²,

et al. 2022

Preprint

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Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

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A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals

Cited by 43 publications

References 41 publications

A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms

A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms

ECG signal processing by feature selection with classification based on machine learning architectures in cardiovascular disease detection

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

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