A Linearly Adaptive Sine–Cosine Algorithm with Application in Deep Neural Network for Feature Optimization in Arrhythmia Classification using ECG Signals

Sharma, Pooja; Dinkar, Shail Kumar

doi:10.1016/j.knosys.2022.108411

Cited by 19 publications

(10 citation statements)

References 82 publications

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“…The classification dataset is the MIT-BIH arrhythmia database with an MLII data channel. The dataset was grouped into four classes: normal, ventricular, LBBB, and RBBB, having sample data of 3000, 740, 2111, and 3000, respectively The robustness of our classification method compared to other arrhythmia classifiers [17][18][19][20] is shown in Table 5. All methods, including our proposed MLP, were tested using the MIT-BIH arrhythmia database with different input lengths and numbers of classes.…”

Section: Arrhythmia Classificationmentioning

confidence: 99%

“…Cardiologists need to classify arrhythmias for diagnosis and plan for the patient's treatment. Previous studies [17][18][19][20] have succeeded in classifying arrhythmias into several classes. The research [17] classifies arrhythmias using the sinecosine algorithm (SCA) method.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies [17][18][19][20] have succeeded in classifying arrhythmias into several classes. The research [17] classifies arrhythmias using the sinecosine algorithm (SCA) method. The SCA method is a classification based on a heuristic approach.…”

Section: Introductionmentioning

confidence: 99%

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Arrhythmia Analysis in the Long-term Electrocardiogram Monitoring System

2023

IJIES

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Cardiovascular disease (CVD) is dangerous and has a high mortality rate. One of the signs of CVD is arrhythmia. The method to find out the symptoms of arrhythmia is an electrocardiogram (ECG) long-term monitoring. The duration of ECG analysis on long-term monitoring is 6 hours, 12 hours, or 24 hours. The device used for longterm ECG is a Holter device. Arrhythmia analysis has followed: detect QRS complex and continue arrhythmia classification. Holter data is stored in local memory or on the server. Storage on local storage makes it difficult for cardiologists to perform analysis. However, using server storage causes access times to slow if patient data increases. Because of this, the cardiologist needs a powerful processing unit to analyze arrhythmia. This study proposes Arrhythmia analysis in the long-term ECG monitoring system. ECG acquisition is sent and stored in the processing unit. We use a single-board computer (SBC) Raspberry Pi as a processing unit. Besides storing data, SBC is also analyzing arrhythmias. The analysis steps are detecting R-peak using the Pan-Tompkins (PTK) algorithm, removing P and T waves using the Gaussian filter, and arrhythmia classification using the multi-layer perceptron (MLP). MLP is a low computational deep learning, which is suitable for SBC. The total storage delay consists of sending and storing data in the database. In the experiment, the propagation data to the broker is 0.023 s., and the storage time to the SQLite database is 15.16 s. The limited recording time for acquisition data is 21 hours and 36 minutes. The success rate of the device in detecting the QRS complex has a precision (+P) of 98.61% and a sensitivity (Se) of 99.8%. Our classification has good in

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Section: Arrhythmia Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Arrhythmia Analysis in the Long-term Electrocardiogram Monitoring System

2023

IJIES

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“…Especially, many algorithms have been implemented for many practical engineering problems on account of their excellent performance, such as feature selection [ 23 , 24 , 25 ], image segmentation [ 26 , 27 ], signal processing [ 28 ], construction of water facilities [ 29 ], path planning for walking robots [ 30 , 31 ], job-shop scheduling problems [ 32 ], and piping and wiring problems in industrial and agricultural production [ 33 ]. Unlike gradient-based optimization algorithms, meta-heuristic algorithms rely on probabilistic search rather than gradient-based execution.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy

2023

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Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic algorithm derived from the distant sense of hearing of sand cats, which shows excellent performance in some large-scale optimization problems. However, the SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, and the tendency to be trapped in the topical optimum. To escape these demerits, an adaptive sand cat swarm optimization algorithm based on Cauchy mutation and optimal neighborhood disturbance strategy (COSCSO) are provided in this study. First and foremost, the introduction of a nonlinear adaptive parameter in favor of scaling up the global search helps to retrieve the global optimum from a colossal search space, preventing it from being caught in a topical optimum. Secondly, the Cauchy mutation operator perturbs the search step, accelerating the convergence speed and improving the search efficiency. Finally, the optimal neighborhood disturbance strategy diversifies the population, broadens the search space, and enhances exploitation. To reveal the performance of COSCSO, it was compared with alternative algorithms in the CEC2017 and CEC2020 competition suites. Furthermore, COSCSO is further deployed to solve six engineering optimization problems. The experimental results reveal that the COSCSO is strongly competitive and capable of being deployed to solve some practical problems.

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“…For a healthy heart, Arrhythmia may take place with minimum consequences based on respiratory behaviour. Thus, the automatic and accurate Arrhythmia classification technique is necessary for patient monitoring for the real‐time application scenario using the ECG signal of the patient and with minimal computational complexity through the efficient feature extraction technique (Azariadi et al, 2016;Sharma & Dinkar, 2022). Recently some meta‐heuristics algorithms, such as the monarch butterfly optimization (MBO), slime mould algorithm (SMA), moth search algorithm (MSA), hunger games search (HGS), Runge Kutta method (RUN), colony predation algorithm (CPA), and Harris hawks optimization (HHO) are established and these algorithms provide several advantages whilst tuning the classifier and provide better accuracy, reducing the risk of errors and ensures the faster convergence.…”

Section: Introductionmentioning

confidence: 99%

IoT based arrhythmia classification using the enhanced hunt optimization‐based deep learning

Kumar

Dutt

et al. 2023

Expert Systems

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The advancement of information technology, the Internet of Things (IoT), and several miniaturize equipment's enhances the healthcare field that provides real‐time patient monitoring, which helps to provide medication anywhere and anytime. However, accurate detection is still a challenging task for which an effective classification model is introduced in this research. The proposed method is the Enhanced Hunt optimization based Deep convolutional neural network (Enhanced Hunt based‐Deep CNN), in which the Enhanced Hunt optimization algorithm (EHOA) is developed by fusing the hunting habit of the predator and the herding characteristics of herding dog for enhancing the global optimal convergence. Here, the ECG signal from the individuals is collected using the IoT network and stored in the Hospital server, which is accessed by the doctor when requested, the classification is performed using the Enhanced Hunt based‐Deep CNN and the performance revealed the effectiveness with the accuracy, sensitivity, and specificity of 95.33%, 94.92%, and 97.57%.

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A Linearly Adaptive Sine–Cosine Algorithm with Application in Deep Neural Network for Feature Optimization in Arrhythmia Classification using ECG Signals

Cited by 19 publications

References 82 publications

Arrhythmia Analysis in the Long-term Electrocardiogram Monitoring System

Arrhythmia Analysis in the Long-term Electrocardiogram Monitoring System

An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy

IoT based arrhythmia classification using the enhanced hunt optimization‐based deep learning

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