Classification method for multivariable medical sensing data streams

A technology of sensing data and classification methods, applied in the computer field, can solve the problems of data noise, affecting the classification effect, large model scale, etc.

Active Publication Date: 2021-01-26
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

[0005] (1) The presence of noise in the data affects the classification effect
There is a lot of noise in the actual collected medical induction data stream, and the actual medical abnormality diagnosis problem is generally a multi-classification problem
[0007] (3) Existing models are large in scale

Method used

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  • Classification method for multivariable medical sensing data streams
  • Classification method for multivariable medical sensing data streams
  • Classification method for multivariable medical sensing data streams

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Embodiment Construction

[0071] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] The present invention focuses on the specific research on the classification of multi-variable medical sensor data streams, and uses deep learning technology to design a classification scheme suitable for multi-variable medical data streams collected by sensors, thereby further promoting the development of smart medical care in a modern society . The present invention proposes classification models that can be used in multivariate medical sensory data streams. First, a signature matrix is ​​constructed, which can capture the correlation between various time series and can represent these time series, and it is also robust to noise; then, for individual imbalanced categories, we use an auxiliary classifier to generate Adversarial Networks (Au...

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Abstract

The invention discloses a classification method for multivariable medical sensing data streams. The method comprises the following steps that firstly, a signature matrix which can capture the correlation among time sequences and represent the time sequences, and has robustness to noise is constructed; then, for individual unbalanced categories, an auxiliary classifier is adopted to generate an adversarial network ACGAN to generate enough signature matrixes corresponding to the categories; finally, a bidirectional convolution long-term and short-term memory (BPCLSTM) lightweight network classification model based on an attention Attention mechanism is constructed to realize accurate classification of multivariable medical sensing data streams, and the classification model not only can improve the classification accuracy, but also can reduce the scale of an original classification model.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a classification method applicable to multivariate medical sensory data streams. Background technique [0002] A medical sensor is a part of the sensor used in the biomedical field. It is a conversion device that converts the physiological information of the human body into electrical information that has a definite functional relationship with it. The information it picks up is the physiological information of the human body, and its output is often expressed in electrical signals. Medical sensors are often used to detect biological information, clinical monitoring, and control of human physiological processes. With the rapid development of the category and performance of medical sensors, they have become an indispensable part of medicine. By using medical sensors to collect various medical data, doctors can obtain the needed patient's body information at the first time, so ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/70G06N3/04G06N3/08
CPCG16H50/70G06N3/08G06N3/045
Inventor 孙乐仲昭奕瞿治国寇振媛路永平
Owner NANJING UNIV OF INFORMATION SCI & TECH
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