Neural network-based EKG pattern recognition

Foo, Simon Y.; Stuart, G. W.; Harvey, Bruce A.; Meyer‐Baese, Anke

doi:10.1016/s0952-1976(02)00041-6

Cited by 80 publications

(36 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the current studies ( [5] [7] [10] [11] [13]) have used a specific subset of data in the database. In this study, rather than using a specific subset, www.ijacsa.thesai.org almost all PVC beats existing in the database were used.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers attempting to classify PVC arrhythmias have mostly used time-frequency analysis techniques, statistical measurements, and hybrid methods. The most recently published works are those presented in [6][7][8][9][10][11][12][13][14][15]. In [6], the authors applied a dynamic Bayesian network for PVC classification.…”

Section: Introductionmentioning

confidence: 99%

“…In [12], the authors focused on manifold learning for PVC detection and proposed a method for PVC recognition using manifold learning and support vector machines (SVM). A neural network-based ECG pattern recognition method was presented in [13]. In that study, NN correctly distinguished normal heartbeats and PVCs in 92% of the proposed cases.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], Independent Component Analysis (ICA) was used for feature extraction and k-means and Fuzzy C-Means(FCM) classifiers were employed to recognize the PVC beat. All of these studies [6][7][8][9][10][11][12][13][14][15] used ECG records from the MIT-BIH Arrhythmia Database.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Classification of Premature Ventricular Contraction in ECG

Kaya¹,

Pehlivan²

2015

ijacsa

View full text Add to dashboard Cite

Abstract-Cardiac arrhythmia is one of the most important indicators of heart disease. Premature ventricular contractions (PVCs) are a common form of cardiac arrhythmia caused by ectopic heartbeats. The detection of PVCs by means of ECG (electrocardiogram) signals is important for the prediction of possible heart failure. This study focuses on the classification of PVC heartbeats from ECG signals and, in particular, on the performance evaluation of time series approaches to the classification of PVC abnormality. Moreover, the performance effects of several dimension reduction approaches were also tested. Experiments were carried out using well-known machine learning methods, including neural networks, k-nearest neighbour, decision trees, and support vector machines. Findings were expressed in terms of accuracy, sensitivity, specificity, and running time for the MIT-BIH Arrhythmia Database. Among the different classification algorithms, the k-NN algorithm achieved the best classification rate. The results demonstrated that the proposed model exhibited higher accuracy rates than those of other works on this topic. According to the experimental results, the proposed approach achieved classification accuracy, sensitivity, and specificity rates of 99.63%, 99.29% and 99.89%, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Premature Ventricular Contraction in ECG

Kaya¹,

Pehlivan²

2015

ijacsa

View full text Add to dashboard Cite

show abstract

“…Then, a multi-layer perceptron classifier was applied to detect 6 types of arrhythmia beats from a 4-dimensional input feature. Foo (Foo, Stuart et al 2002) compared and evaluated different types of multilayer neural network structures as the ECG pattern classifiers and finally settled on a two-layer feed-forward neural network. However, their work is limited to detecting only two types of patterns including normal beats and premature ventricular contractions (PVC).…”

Section: An Improved Procedures For Detection Of Heart Arrhythmiasmentioning

confidence: 99%