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• W2181310857 abstract "Normal and abnormal Cardiac function of the human heart can be evaluated through the application of EKG/ECG waveform processing and evaluation. Although traditionally the interpretation of these waveforms remains largely a manual effort, as computing power has increased, so has the application of computational methods to their evaluation and classification. One computational method previously applied was the Artificial Neural Network, but its application required the addition of signal pre-processing and feature extraction. Recently, a new computational model, Hierarchical Temporal Memory has become available for research. This model itself is an attempt to replicate the structural and algorithmic properties of the neocortex in the human mind. One key component of this model is the ability to present the waveform as an image absent of pre- processing and feature extraction. It is the application of this model to the evaluation of ECG waveforms that is the subject of this research. The results obtained correlate to the HTM model's ability to mimic human learning and provide similar classification ability." @default.
• W2181310857 created "2016-06-24" @default.
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• W2181310857 date "2011-01-01" @default.
• W2181310857 modified "2023-09-27" @default.
• W2181310857 title "The Application of Hierarchical Temporal Memory to the Evaluation of ECG Waveforms" @default.
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