Cardiac arrhythmia classification by time–frequency features inputted to the designed convolutional neural networks

Zhang, Yi; Yi, Jizheng; Chen, Aibin; Cheng, Le

doi:10.1016/j.bspc.2022.104224

Cited by 14 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this work, an algorithm for morphological arrhythmia classification using ECG signal has been presented where ECG Beats utilized by the proposed algorithm. The performance of the proposed algorithm is also compared with [39], where the Convolutional Recurrence Neural Network (CRNN) is used for classification, after preprocessing the ECG signal. The overall accuracy of the present work is better than the accuracy reported in [39].…”

Section: Discussionmentioning

confidence: 99%

“…The performance of the proposed algorithm is also compared with [39], where the Convolutional Recurrence Neural Network (CRNN) is used for classification, after preprocessing the ECG signal. The overall accuracy of the present work is better than the accuracy reported in [39].…”

Section: Discussionmentioning

confidence: 99%

“…Several supervised learning algorithms such as Neural Networks [21][22][23][24][25][26], Support Vector Machines [17,27], Linear Discriminant [5,28,29], Neuro-Fuzzy Approach [30], Logistic Regression [31], Decision Trees [32,33], and K-Nearest Neighbor [34,38] have been utilized to classify ECG beats into different categories. Zhang et al [39] have employed the Convolutional Recurrence Neural Network (CRNN) to classify into eight classes after denoising and segmenting the ECG signal. The Thilagavathy and Venkataramani [40] proposed an approach to enhance the feature's potential using Discrete Anamorphic Stretch Transform (DAST) and classified the ECG beats into five classes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Electrocardiogram morphological arrhythmia classification using fuzzy entropy-based feature selection and optimal classifier

Chaubey,

Saha

2023

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Electrocardiogram (ECG) signal analysis has become significant in recent years as cardiac arrhythmia shares a major portion of all mortality world-wide. To detect these arrhythmias, computer-assisted algorithms play a pivotal role as beat-by-beat monitoring of holter ECG signals is required. In this paper, a morphological arrhythmia classification algorithm has been proposed to classify seven different ECG beats, namely Normal Beat (N), Left Bundle Branch Block Beat (L), Right Bundle Branch Block Beat (R), Atrial Premature Contraction Beat (A), Premature Ventricular Contraction Beat (V), Fusion of Normal and Ventricle Beat (F) and Pace Beat (P). A novel feature set of 25 attributes has been extracted from each ECG beat and ranked using the Fuzzy Entropy-based featureselection (FEBFS) technique. In addition, two distinct classifiers, support vector machine with radial basis function as the kernel (SVM-RBF) and weighted K-nearest neighbor (WKNN), are used to categorize ECG beats, and their performances are also evaluated after adjusting vital parameters. The performance of classifiers is compared for four different ECG beat segmentation approaches and further analyzed using three similarity measurement techniques and two fuzzy entropy methods while feature selection. The classifier results are also cross-validated using a 10-fold cross-validation scheme, and the MIT-BIH Arrhythmia Database has been used to validate the proposed work. After selecting 21 highly ranked features, WKNN achieves the best results with the nearest neighbor value K=3 and cityblock distance metrics, with Average Sensitivity (Sen) = 94.89%, Positive Predictivity (Ppre) = 97.13%, Specificity (Spe)= 99.72%, F1 Score = 95.95%, and Overall Accuracy (Acc) = 99.15%.The novelty of this work relies on formulating a unique feature set, including proposed symbolic features, followed by the FEBFS technique making this algorithm efficient and reliable for morphological arrhythmia classification. The above results demonstrate that the proposed algorithm performs better than many existing state-of-the-art works.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Electrocardiogram morphological arrhythmia classification using fuzzy entropy-based feature selection and optimal classifier

Chaubey,

Saha

2023

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

show abstract

“…[44][45][46] This operation uses a few parameters, which not only simplifies the training process but also speeds up the network. [47,48] But in reality, there are not a large number of parameters to design a more complex network. As mentioned in the above section, higher-order spectra are more suitable for non-Gaussian heart sound signals.…”

Section: The Designed Mcnn For Heart Sounds Classificationmentioning

confidence: 99%

Heart Sounds Classification Based on High‐Order Spectrogram and Multi‐Convolutional Neural Network after a New Screening Strategy

Li,

Yi,

Zhong

et al. 2023

Advcd Theory and Sims

Self Cite

View full text Add to dashboard Cite

This paper proposes a pre‐processing method for heart sound screening and extracts the high‐order spectral feature of phonocardiogram. Moreover, a multi‐convolutional neural network (mCNN) is constructed to achieve the classification of normal, aortic stenosis, mitral regurgitation, mitral stenosis, and mitral valve prolapse. First, the heart sound recordings are down‐sampled, denoised by wavelet transform, and normalized. Second, a new heart sound screening algorithm is proposed. The waveform of the heart sound recording is segmented and saved as an image which is performed by the gray‐scale processing to calculate the amplitude of the heart sound. The extremely noisy heart sound segments are screened out based on the amplitude information, and the remaining heart sound segments are spliced as pure heart sound recordings. After 50% superposition segmentation of the heart sound recordings, high‐order spectral features are extracted and image data are stored. Finally, a 34‐layer mCNN is specifically designed to boost the performance of heart sound classification through multi‐layer dimensionality reduction. Experimental results show that the proposed method has superior performance compared with the existing one. For the two‐category dataset, the accuracy with and without PCG screening is 97.99% and 99.42%, respectively. For the five‐category dataset, the average accuracy is 99%.

show abstract

“…The study [21] trained deep convolutional neural networks to identify different numbers of heart diseases by splitting the signal into parts of 5 seconds and using dwt to remove noise, and the results achieved an improvement in the performance of the convolutional neural network model and an improvement in the performance of signal classification. The research [22] builds convolutional neural networks based on the features of time and frequency to classify the ECG into a normal state or 7 abnormal states, the first part of the research regulates frequencies, reduces noise, and cuts the signal, while the other part is designing a 1D-CNN model consisting of 12 layers, and the experimental results showed excellent performance and accuracy Rating.…”

Section: Related Workmentioning

confidence: 99%

Design an Intelligent Real Time ECG Monitoring System Using Convolution Neural Network

Falhi,

Khleaf

2023

RIA

View full text Add to dashboard Cite

Electrocardiogram (ECG) monitoring is now becoming part of everyday health life. Through ECG characteristics such as patient's heartbeats, heart conditions, and heart disease can be analyzed. This paper presents the design and implementation of a system for analyzing and filtering the ECG signal and allowing its remote monitoring based on the use of deep learning algorithms, this algorithm is Convolution Neural Network (CNN), where the network was built in MATLAB and training using the dataset (PhysioNet 2017). when, the ESP NODE MCU microcontroller was used with the AD8232 sensor in designing a system that records the ECG signal from the patient in real time and filtering it using FIR filter that will be designed in MATLAB, then transmits it to the network that has been trained to be classified as whether it is normal or abnormal. Then, this result is transmitted locally to be displayed in monitoring side, the results showed high accuracy in classifying the signal and in filtering different Noise, as well as its speed in responding to a change in the condition of the signal and giving a warning to the observer. This contributes to speeding up the detection of the deterioration of the patient's condition in a timely manner.

show abstract

Cardiac arrhythmia classification by time–frequency features inputted to the designed convolutional neural networks

Cited by 14 publications

References 29 publications

Electrocardiogram morphological arrhythmia classification using fuzzy entropy-based feature selection and optimal classifier

Electrocardiogram morphological arrhythmia classification using fuzzy entropy-based feature selection and optimal classifier

Heart Sounds Classification Based on High‐Order Spectrogram and Multi‐Convolutional Neural Network after a New Screening Strategy

Design an Intelligent Real Time ECG Monitoring System Using Convolution Neural Network

Contact Info

Product

Resources

About