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

Xie, Tiantian; Li, Runchuan; Shen, Shengya; Zhang, Xingjin; Wang, Zongmin

doi:10.1155/2019/5787582

Cited by 16 publications

(19 citation statements)

References 27 publications

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“…Further, our method did not rely on complex preprocessing and was superior to reference [ 37 ] in all metrics. Finally, the proposed system’s sensitivity was similar to that of reference [ 30 , 32 , 33 ]. Our method was superior to the methods presented in these three literature pieces in terms of accuracy and specificity.…”

Section: Resultssupporting

confidence: 59%

“…The essence of the classifier is a hypothesis or discrete-valued function. There are some popular classifiers used to distinguish regular and PVC beats: Artificial neural networks (ANN) [ 20 , 21 , 22 ], learning vector quantization neural network (LVQNN) [ 23 ], k-nearest neighbours (k-NN) algorithm [ 24 , 25 ], discrete hidden Markov model (DHMM) [ 26 ], support vector machine (SVM) [ 27 , 28 ], Bayesian classification algorithms [ 29 ], and random forest (RF) [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Wang

Chen

et al. 2021

Biosensors

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Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing these long-term ECG is time-consuming and labor-intensive for cardiologists. Therefore, this paper proposed a simplistic but powerful approach to detect PVC from long-term ECG. The suggested method utilized deep metric learning to extract features, with compact intra-product variance and separated inter-product differences, from the heartbeat. Subsequently, the k-nearest neighbors (KNN) classifier calculated the distance between samples based on these features to detect PVC. Unlike previous systems used to detect PVC, the proposed process can intelligently and automatically extract features by supervised deep metric learning, which can avoid the bias caused by manual feature engineering. As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. The simulation events show that it is reliable to use deep metric learning and KNN for PVC recognition. More importantly, the overall way does not rely on complicated and cumbersome preprocessing.

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Section: Resultssupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Wang

Chen

et al. 2021

Biosensors

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“…Recently, low resolution Lorentz plot images were trained with a convolutional neural network to discriminate between AF and sinus rhythm [ 10 ]. In another recent study, RR intervals and QRS morphology-based features were combined with a random forest to detect PVC in [ 11 ]. However, there is scant literature on detecting PAC/PVC from PPG-based data.…”

Section: Introductionmentioning

confidence: 99%

Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch

Han

Bashar

Mohagheghian

et al. 2020

Sensors

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We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincaré plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.

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“…Based on the above four quantities, typical classification indicators [ 40 – 42 ] on each class including specificity (Spe), precision (Pre), recall (Rec), accuracy (Acc), and F 1 score ( F 1) are derived accordingly and defined as equations ( 11 )∼( 15 ).

…”

Section: Experiments and Resultsmentioning

confidence: 99%

Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification

Wang

et al. 2021

Journal of Healthcare Engineering

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Automatic classification of ECG is very important for early prevention and auxiliary diagnosis of cardiovascular disease patients. In recent years, many studies based on ECG have achieved good results, most of which are based on single-label problems; one record corresponds to one label. However, in actual clinical applications, an ECG record may contain multiple diseases at the same time. Therefore, it is very important to study the multilabel ECG classification. In this paper, a multiscale residual deep neural network CSA-MResNet model based on the channel spatial attention mechanism is proposed. Firstly, the residual network is integrated into a multiscale manner to obtain the characteristics of ECG data at different scales and then increase the channel spatial attention mechanism to better focus on more important channels and more important ECG data fragments. Finally, the model is used to classify multilabel in large databases. The experimental results on the multilabel CCDD show that the CSA-MResNet model has an average F1 score of 88.2% when the multilabel classification of 9 ECGs is performed. Compared with the benchmark model, the F1 score of CSA-MResNet in the multilabel ECG classification increased by up to 1.7%. And, in the model verification on another database HF-challenge, the final average F1 score is 85.8%. Compared with the state-of-the-art methods, CSA-MResNet can help cardiologists perform early-stage rapid screening of ECG and has a certain generalization performance, providing a feasible analysis method for multilabel ECG classification.

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

Cited by 16 publications

References 27 publications

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch

Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification

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