2020
DOI: 10.1016/j.asoc.2019.105778
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Electrocardiogram soft computing using hybrid deep learning CNN-ELM

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Cited by 170 publications
(86 citation statements)
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“…Parallelization can be achieved by instantiating multiple identical computing modules; the structure between different layers of the network is highly similar, so each layer can be reused The computing resources of the network are used for the calculation of other layers. Different from the general CNN, due to the particularity of the cellular neural network, the output channel dimensions are small, so that parallelization in these two dimensions is difficult to exert the maximum efficiency [26][27]. This section mainly analyzes specific methods for implementing hardware acceleration of cellular neural network algorithms through fine-grained parallelism.…”
Section: B Feasibility Analysis Of Parallel Implementation Of Cellulmentioning
confidence: 99%
“…Parallelization can be achieved by instantiating multiple identical computing modules; the structure between different layers of the network is highly similar, so each layer can be reused The computing resources of the network are used for the calculation of other layers. Different from the general CNN, due to the particularity of the cellular neural network, the output channel dimensions are small, so that parallelization in these two dimensions is difficult to exert the maximum efficiency [26][27]. This section mainly analyzes specific methods for implementing hardware acceleration of cellular neural network algorithms through fine-grained parallelism.…”
Section: B Feasibility Analysis Of Parallel Implementation Of Cellulmentioning
confidence: 99%
“…In the experiment, the actual measurement of the rehabilitation robot collision radius is 156mm, and the human collision radius is 131mm. The first set of experiments shows that the target recognition algorithm based on deep learning has a recognition rate of 100% for target types, and for each type of target, the tracking error increases as the deflection angle between the target and the rehabilitation robot increases [51]. Because the deep learning algorithm has errors in the detection of the target image position, and the monocular range finding model also has a certain error in the positioning of the target, it causes errors in the tracking of the target by the rehabilitation robot.…”
Section: Figure 5 Comparison Of Target Recognition From Four Angles mentioning
confidence: 99%
“…The method of multi-LSTM model fusion was designed in literature [31], which only recognized a single P wave and achieved an average accuracy of 98.48. Literature [32], a method combining a CNN and ELM model, was used to recognize only a single QRS complex, with an average accuracy of 98.77. In literature [33], an support vector machine (SVM) model was used to recognize a single QRS complex, with an average accuracy of 95.26.…”
Section: Training Resultsmentioning
confidence: 99%
“…Faster R-CNN 98.94 O, P, QRS, T 4 LSTM [30] 92.00 P, QRS, T 3 4-LSTM [31] 98.48 P 1 CNN + ELM [32] 98.77 QRS 1 SVM [33] 95.26 QRS 1…”
Section: Recognition Methods Average Accuracy (%) Waveform Type Wavefomentioning
confidence: 99%