Key result
A restricted Boltzmann machine-based deep belief network achieved an accuracy of 98.829% for two-lead ECG heartbeat classification on the MIT-BIH dataset.
A restricted Boltzmann machine-based algorithm demonstrated high accuracy (98.8%) for unsupervised classification of two-lead ECG heartbeats.
May aid automated ECG screening; leaves open prospective clinical validation before practice change.
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.
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Yan et al. (2015) studied ECG classification. Restricted Boltzmann machine (RBM) based algorithm was evaluated on Classification accuracy. A restricted Boltzmann machine-based deep belief network achieved an accuracy of 98.829% for two-lead ECG heartbeat classification on the MIT-BIH dataset.
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