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Wave entropy of spontaneous dynamic activity based autism classification method

A classification method, a technique for autism, applied in the field of near-infrared spectroscopy, which can solve problems such as inability to describe phase fluctuations well

Pending Publication Date: 2020-05-05
SHANGHAI UNIV
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Problems solved by technology

However, the existing sample entropy theory cannot well describe the characteristics of stage fluctuations

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  • Wave entropy of spontaneous dynamic activity based autism classification method
  • Wave entropy of spontaneous dynamic activity based autism classification method
  • Wave entropy of spontaneous dynamic activity based autism classification method

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Embodiment Construction

[0044] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0045] Such as figure 1 As shown, a classification method for autism based on the fluctuation entropy of spontaneous kinetic activity includes the following steps:

[0046] Step 1. Cut the near-infrared spectrum fNIRS time series according to the sequence length, then set different sliding windows and step sizes, and perform spatial reconstruction on each subsequence according to the sliding window and step size to obtain the reconstructed components; the specific process is as follows:

[0047] (1-1) Pretreatment process: A continuous wave fNIRS system (FOIRE-3000, Shimadzu Corporations, Kyoto, Japan) was used in this example. The 16 light sources and 16 fiber optic detectors of FOIRE-3000 can form a maximum of 52 detec...

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Abstract

The invention discloses a wave entropy of spontaneous dynamic activity based autism classification method. The specific implementation steps of the method are as follows: (1) cutting near infrared spectroscopy fNIRS time sequences according to sequence lengths, setting different sliding windows and step lengths, performing spatial reconstruction on each subsequence according to the sliding windowsand the step lengths, and obtaining reconstruction components; (2) performing sample entropy calculating on the reconstruction components obtained by the step (1) so as to obtain a wave entropy sequence; (3) extracting four characteristic values (including the variance, mean, maximum and minimum of the wave entropy sequence) of the wave entropy sequence to generate a new sample as the input variable of a support vector machine (SVM); and (4) inputting the new sample obtained by the step (3) into the SVM to judge ASD. The method realizes high classification on ASD diagnosis; and the method indicates that the wave entropy is a very sensitive index to distinguish the ASD and TD, which might be an fNIRS based biomarker for predicting the ASD in the future.

Description

technical field [0001] The invention belongs to the field of near-infrared spectroscopy (fNIRS) processing, and in particular relates to an autism classification method based on fluctuation entropy of spontaneous dynamic activities. Background technique [0002] Autism Spectrum Disorder (ASD) is a system-wide disorder, which is mainly manifested in three aspects: speech function, impairment of social activities, narrow interest activities and stereotyped behavior activities. According to the survey, the prevalence of ASD in the United States increased from 0.67% in 2000 to 1.47% in 2010. According to the data of the Centers for Disease Control and Prevention (CDC), the current prevalence of ASD is about 1.69%. As a result, the prevalence of ASD among American children is steadily increasing. A current challenge in ASD research is early diagnosis, which may alter the developmental trajectory of children at risk of ASD and have important implications for long-term outcomes, a...

Claims

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Application Information

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IPC IPC(8): A61B5/00
CPCA61B5/0075A61B5/4088A61B5/7267A61B2503/06
Inventor 徐凌宇郭艳蓉金宝刚
Owner SHANGHAI UNIV
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