GB-SVNN: Genetic BAT assisted support vector neural network for arrhythmia classification using ECG signals

Vishwanath, Bhagyalakshmi; Pujeri, Ramachandra V.; Devanagavi, Geetha D.

doi:10.1016/j.jksuci.2018.02.005

Cited by 34 publications

(13 citation statements)

References 38 publications

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“…Bhagyalakshmi et al [8] proposed a supervised classification approach by using a support vector neural network (SVNN) to make the classification. In performing feature extraction, they used a wavelet and Gabor filter on the ECG signals.…”

Section: Parametric Feature Based Methodsmentioning

confidence: 99%

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Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals

Kumar¹,

Mallik²,

Kumar³

et al. 2023

Computers in Biology and Medicine

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Section: Parametric Feature Based Methodsmentioning

confidence: 99%

“…Most of the existing work done in the field of Arrhythmia detection from ECG signals relies on wavelet processing [8,17]. However, the recent popularity and utility of convolutional neural networks have proved their mantle in various fields including medical image diagnosis.…”

Section: Motivationmentioning

confidence: 99%

Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals

Kumar¹,

Mallik²,

Kumar³

et al. 2023

Computers in Biology and Medicine

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“…Instantaneous frequency is defined mainly the Hilbert Transformation (HT), and time-frequency techniques. The IMFs have a vertically symmetric and narrow band form, that allow the second step of the HHT to be applied the Hilbert transform of each IMF [17,18,19]. As explained below, the Hilbert Transform obtains the best fit of a sinusoid to each IMF at every point in time, identifying an instantaneous frequency (IF), along with its associated instantaneous amplitude (IA).…”

Section: Signal Filtering Methods -Hilbert Transformmentioning

confidence: 99%

ECG signal PQRS detection and comprehensive estimation of signal noise

Amhia¹,

Wadhwani²

2022

ijhs

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Automated bioelectric signal analysis has an important application in the wisdom medical care. In this work, we focus on ECG-signal and address a novel approach for cardiac arrhythmia diseases classification. We designed a novel analysis framework which extract different feature transformations from ECG signals. And we trained the ANN model for multi-feature to obtain the prediction. Finally, we tested our approach on the public database of MIT-BIH arrhythmia. And the results of experiments on the database demonstrate our model has better classification performance than other approaches.

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“…The genetic bat optimization technique was proposed for training the dataset based on the support vector neural network to classify the arrhythmia of ECG waveforms. For the feature extraction wavelet-based approach and the Gabor filters were used [8] to validate and classify the heart beat rate. The classification is based on the extraction of feature of heartbeat and classification techniques can be processed that features to diagnose the arrhythmia.…”

Section: Related Workmentioning

confidence: 99%

Analysis and classification of cardiac arrhythmia based on general sparsed neural network of ECG signals

2020

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In medical practices, the ECG plays an important role in diagnosing cardiac arrhythmia. In this paper efficient and most reliable technique is mentioned for the suitable classification of arrhythmia using a general sparsed neural network (GSNN). The sparsed neural network is used to extract the feature of ECG signals and then this feature is used in the neural network for processing to obtain the final classification result. The different class of ECG beat is chosen from MIT-BIH dataset. The signal to noise ratio has been calculated to filter the ECG signal. To evaluate the results, the MATLAB software is used. The main purpose of this paper is to design an efficient neural network and also to implement reliable techniques for the classification of various ECG arrhythmia conditions. The obtained accuracy level of arrhythmia detection is 98%, which is the highest rate of performance. The presented approach by GSNN to classify and predict arrhythmia will provide efficient arrhythmia detection as compared to other techniques. The suggested scheme will improve the prediction and classification efficiency.

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GB-SVNN: Genetic BAT assisted support vector neural network for arrhythmia classification using ECG signals

Cited by 34 publications

References 38 publications

Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals

Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals

ECG signal PQRS detection and comprehensive estimation of signal noise

Analysis and classification of cardiac arrhythmia based on general sparsed neural network of ECG signals

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