Abstract
Pneumoconiosis is one of the most serious occupational diseases in coal miners at present. If the disease can be detected in advance, we can prevent it from worsening and improve the survival probability. The frequently used method for clinical detection of pneumoconiosis is through X-ray or HRCT examination. This paper presents a method for detecting pneumoconiosis based on wrist pulse signal. Firstly, the pulse signals of non-pneumoconiosis people and pneumoconiosis patients were collected, and then a database was established. After pre-processing, periodic segmentation is performed and features are extracted from the time domain, frequency domain, and wavelet domain. Support vector machine (SVM) is used to classify. Finally, the detection decision was maken by voting. The accuracy of the proposed method is 88.31% on the testing set, which proves the effectiveness of this method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
