echemi logo
Product
  • Product
  • Supplier
  • Inquiry
    Home > Active Ingredient News > Study of Nervous System > IEEE trans: Using fNIRS to classify depression in sports rehabilitation

    IEEE trans: Using fNIRS to classify depression in sports rehabilitation

    • Last Update: 2021-12-03
    • Source: Internet
    • Author: User
    Search more information of high quality chemicals, good prices and reliable suppliers, visit www.echemi.com

    Depression is the most common depressive disorder, with significant and lasting depression as the main clinical feature, and it is the main type of mood disorder


    Depression is the most common depressive disorder, with significant and lasting depression as the main clinical feature, and it is the main type of mood disorder


    A clear link between the effect of depression on the rehabilitation process and the functional recovery of patients has not yet been established


    A clear link between the effect of depression on the rehabilitation process and the functional recovery of patients has not yet been established


    The participant puts on the instrument and sits upright

    The participant puts on the instrument and sits upright

    The study recruited 31 participants, including 14 adults (6 men and 8 women) with a clinical diagnosis of MDD and 17 healthy controls (6 men and 11 women)


    The study recruited 31 participants, including 14 adults (6 men and 8 women) with a clinical diagnosis of MDD and 17 healthy controls (6 men and 11 women)


    The continuous-wavelength fNIRS system (fNIRS equipment model 1000, USA) was used to record the light intensity emitted by 4 emitters and 10 detectors to obtain the hemodynamic response of 16 channels


    fNIRS detection PFC activation sensitivity map

    fNIRS detection PFC activation sensitivity map

    The study found that due to the number of features and the size of the data set used, the classification accuracy of the model is between 60% and 90%


    The study found that due to the number of features and the size of the data set used, the classification accuracy of the model is between 60% and 90%


    This study demonstrates the usefulness of predictive neurotechnology to classify depressive symptoms when performing motor tasks in adults


    Y.


     



    Leave a message here
    This article is an English version of an article which is originally in the Chinese language on echemi.com and is provided for information purposes only. This website makes no representation or warranty of any kind, either expressed or implied, as to the accuracy, completeness ownership or reliability of the article or any translations thereof. If you have any concerns or complaints relating to the article, please send an email, providing a detailed description of the concern or complaint, to service@echemi.com. A staff member will contact you within 5 working days. Once verified, infringing content will be removed immediately.

    Contact Us

    The source of this page with content of products and services is from Internet, which doesn't represent ECHEMI's opinion. If you have any queries, please write to service@echemi.com. It will be replied within 5 days.

    Moreover, if you find any instances of plagiarism from the page, please send email to service@echemi.com with relevant evidence.