Name
Abstract | ||
|---|---|---|
An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. In this paper a deep belief network was constructed and the RBM based algorithm was used in the classification problem. Under the recommended twelve classes by the ANSI/AAMI EC57: 1998/(R)2008 standard as the waveform labels, the algorithm was evaluated on the two-lead ECG dataset of MIT-BIH and gets the performance with accuracy of 98.829%. The proposed algorithm performed well in the two-lead ECG classification problem, which could be generalized to multi-lead unsupervised ECG classification or detection problems. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BSN.2015.7299399 | 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) |
Keywords | Field | DocType |
big data,electrocardiography classification,restricted Boltzmann machine,deep belief network | Restricted Boltzmann machine,Data modeling,Pattern recognition,Computer science,Deep belief network,Waveform,Feature extraction,Probability distribution,Artificial intelligence,Artificial neural network,Electrocardiography,Machine learning | Conference |
ISSN | Citations | PageRank |
2376-8886 | 11 | 0.59 |
References | Authors | |
19 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Yan | 1 | 12 | 4.02 |
| Xinbing Qin | 2 | 11 | 0.59 |
| Yige Wu | 3 | 11 | 0.59 |
| Nannan Zhang | 4 | 11 | 0.59 |
| Jianping Fan | 5 | 2677 | 192.33 |
| Lei Wang | 6 | 30 | 9.89 |