QRS complex detection and arrhythmia classification using SVM

Barhatte, Alka; Ghongade, Rajesh; Thakare, Abhishek S.

doi:10.1109/ccintels.2015.7437915

Cited by 17 publications

(5 citation statements)

References 2 publications

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“…Next the start and end points of the QRS complex will be used to extract the PR interval, QRS interval, and QT interval. In recent years, there are many methods to identify PVC based on PVC disease characteristics, and we used 7 features (R amplitude [16], PR interval, QRS interval, QT interval, QRS area [17], pre_RR interval [18, 19], and post_RR interval [20]) for the study. In order to facilitate the subsequent calculation and understanding, the seven features are, respectively, recorded as R_amp, PR, QRS, QT, QRS_area, pre_RR, and post_RR.…”

Section: Pvc Identification Methods Based On Featuresmentioning

confidence: 99%

Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest

Xie

Shen

et al. 2019

Journal of Healthcare Engineering

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Premature ventricular contraction (PVC) is one of the most common arrhythmias in the clinic. Due to its variability and susceptibility, patients may be at risk at any time. The rapid and accurate classification of PVC is of great significance for the treatment of diseases. Aiming at this problem, this paper proposes a method based on the combination of features and random forest to identify PVC. The RR intervals (pre_RR and post_RR), R amplitude, and QRS area are chosen as the features because they are able to identify PVC better. The experiment was validated on the MIT-BIH arrhythmia database and achieved good results. Compared with other methods, the accuracy of this method has been significantly improved.

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Section: Pvc Identification Methods Based On Featuresmentioning

confidence: 99%

Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest

Xie

Shen

et al. 2019

Journal of Healthcare Engineering

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“…These samples are used as features of the final classification. According to the literature [ 14 , 15 , 20 , 22 – 24 , 29 ], the wavelength, interval, and morphology of ECG signals have important medical significance and can reveal the hidden information in the heartbeat. Hence, based on the detected fiducial points, the 10 feature parameters are extracted for classification in this paper.…”

Section: Methodsmentioning

confidence: 99%

“…Among them, the two steps of feature extraction and classification are the most critical in the classification process, which are deeply studied by researchers. Furthermore, researches used numerous features to describe the ECG heartbeats, Hermite functions [ 13 ], morphological features [ 14 , 15 ], wavelet features [ 16 , 17 ], high-order statistical features [ 18 , 19 ], QRS amplitude vector [ 20 ], QRS complex wave area [ 21 ], and heartbeat intervals [ 22 – 24 ]. Over the past few decades, numerous algorithms have been developed to distinguish different types of arrhythmias, including linear classifier [ 25 – 27 ], decision tree [ 28 , 29 ], k-nearest neighbor [ 30 – 32 ], support vector machine [ 33 , 34 ], random forest [ 35 , 36 ], and ensemble classifier [ 37 – 41 ], etc.…”

Section: Related Workmentioning

confidence: 99%

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm

Shen

et al. 2021

Journal of Healthcare Engineering

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Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules. In the preprocessing module, the signal is denoised and segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS length, T length, PR interval, ST segment, QT interval, RR interval, R amplitude, and T amplitude. Subsequently, the features are combined and normalized, and the effect of different feature combinations on heartbeat classification is analyzed to select the optimal feature combination. Finally, the four types of normal and abnormal heartbeats were identified using the Stacking-DWKNN algorithm. This method is performed on the MIT-BIH arrhythmia database. The result shows a sensitivity of 89.42% and a positive predictive value of 94.90% of S-type beats and a sensitivity of 97.21% and a positive predictive value of 97.07% of V-type beats. The obtained average accuracy is 99.01%. Compared to other models with the same features, this method can improve accuracy and has a higher positive predictive value and sensitivity, which is important for clinical decision-making.

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“…The workflow starts with signal filtering and QRS and R-peak detection algorithms methods, followed by feature extraction and simple classification with a classifier or classification fusion methods with multiple classifiers. A broad set of handcrafted features for ECG analysis, such as temporal relationships between waves, morphological descriptors, state-space features, linear transform, spectral representation, wavelet analysis, etc., have been described as well [11,12,[18][19][20][21][22][23][24]. Among the temporal features, a wide assortment of QRS morphological descriptors was mentioned, including QRS width, positive and negative peak amplitudes, QRS slopes, and cardiogram vector descriptors.…”

Section: Literature Reviewmentioning

confidence: 99%

Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning

et al. 2022

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The success of arrhythmia classification tasks with Machine Learning (ML) algorithms is based on the handcrafting extraction of features from Electrocardiography (ECG) signals. However, feature extraction is a time-consuming trial-and-error approach. Deep Neural Network (DNN) algorithms bypass the process of handcrafting feature extraction since the algorithm extracts the features automatically in their hidden layers. However, it is important to have access to a balanced dataset for algorithm training. In this exploratory research study, we will compare the evaluation metrics among Convolutional Neural Networks (1D-CNN) and Support Vector Machines (SVM) using a dataset based on the merged public ECG signals database TNMG and CINC17 databases. Results: Both algorithms showed good performance using the new, merged ECG database. For evaluation metrics, the 1D-CNN algorithm has a precision of 93.04%, an accuracy of 93.07%, a recall of 93.20%, and an F1-score of 93.05%. The SVM classifier (λ = 10, C = 10e9) achieved the best classification metrics with two combined, handcrafted feature extraction methods: Wavelet transforms and R-peak Interval features, which achieved an overall precision of 89.04%, accuracy of 92.00%, recall of 94.20%, and F1-score of 91.54%. As an unique input feature and SVM (λ=10,C=100), wavelet transforms achieved precision, accuracy, recall, and F1-score metrics of 86.15%, 85.33%, 81.16%, and 83.58%. Conclusion: Researchers face a challenge in finding a broad dataset to evaluate ML models. One way to solve this problem, especially for deep learning models, is to combine several public datasets to increase the amount of data. The SVM and 1D-CNN algorithms showed positive results with the merge of databases, showing similar F1-score, precision, and recall during arrhythmia classification. Despite the favorable results for both of them, it should be considered that in the SVM, feature selection is a time-consuming trial-and-error process; meanwhile, CNN algorithms can reduce the workload significantly. The disadvantage of CNN algorithms is that it has a higher computational processing cost; moreover, in the absence of access to powerful computational processing, the SVM can be a reliable solution.

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QRS complex detection and arrhythmia classification using SVM

Cited by 17 publications

References 2 publications

Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest

Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm

Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning

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