Method for positioning scalp electroencephalogram epilepsy region based on artificial intelligence
The invention relates to the field of artificial intelligence, in particular to a scalp electroencephalogram epilepsy region positioning method based on artificial intelligence. Comprising the steps that historical electroencephalogram waveform images are collected and compressed, and a sparse codin...
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| creator | HAN XIONG HAN JIUYAN ZHENG MEIQIONG |
| description | The invention relates to the field of artificial intelligence, in particular to a scalp electroencephalogram epilepsy region positioning method based on artificial intelligence. Comprising the steps that historical electroencephalogram waveform images are collected and compressed, and a sparse coding image of each historical waveform image is obtained; a neural network is used for training, and the LOSS function historical value of each sparse coding image is obtained; calculating a mean value of historical values of the LOSS function of the sparse coding image, and obtaining an oscillation sparse coding image; calculating the similarity between the oscillation sparse coding image and each sparse coding image, and obtaining an associated sparse coding image; and reconstructing an LOSS function of the neural network, and outputting a labeled image. According to the technical means provided by the invention, the LOSS function of the neural network is re-constructed, so that the recognition accuracy of the neura |
| format | Patent |
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Comprising the steps that historical electroencephalogram waveform images are collected and compressed, and a sparse coding image of each historical waveform image is obtained; a neural network is used for training, and the LOSS function historical value of each sparse coding image is obtained; calculating a mean value of historical values of the LOSS function of the sparse coding image, and obtaining an oscillation sparse coding image; calculating the similarity between the oscillation sparse coding image and each sparse coding image, and obtaining an associated sparse coding image; and reconstructing an LOSS function of the neural network, and outputting a labeled image. 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Comprising the steps that historical electroencephalogram waveform images are collected and compressed, and a sparse coding image of each historical waveform image is obtained; a neural network is used for training, and the LOSS function historical value of each sparse coding image is obtained; calculating a mean value of historical values of the LOSS function of the sparse coding image, and obtaining an oscillation sparse coding image; calculating the similarity between the oscillation sparse coding image and each sparse coding image, and obtaining an associated sparse coding image; and reconstructing an LOSS function of the neural network, and outputting a labeled image. 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Comprising the steps that historical electroencephalogram waveform images are collected and compressed, and a sparse coding image of each historical waveform image is obtained; a neural network is used for training, and the LOSS function historical value of each sparse coding image is obtained; calculating a mean value of historical values of the LOSS function of the sparse coding image, and obtaining an oscillation sparse coding image; calculating the similarity between the oscillation sparse coding image and each sparse coding image, and obtaining an associated sparse coding image; and reconstructing an LOSS function of the neural network, and outputting a labeled image. According to the technical means provided by the invention, the LOSS function of the neural network is re-constructed, so that the recognition accuracy of the neura</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DIAGNOSIS HANDLING RECORD CARRIERS HUMAN NECESSITIES HYGIENE IDENTIFICATION IMAGE DATA PROCESSING OR GENERATION, IN GENERAL MEDICAL OR VETERINARY SCIENCE PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS SURGERY |
| title | Method for positioning scalp electroencephalogram epilepsy region based on artificial intelligence |
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