Directional-Guided Motion Sensitive Descriptor for Automated Detection of Hypertension Using Ultrasound Images

The current work proposes an efficient assessment of hypertension (HTN) using a Directional-Guided Motion Sensitive (DGMS) descriptor and Machine Learning (ML) algorithm. The main objective of the proposed work is to automate the detection of HTN using ultrasound (US) images. The four-chamber US ima...

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Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Gudigar, Anjan, Kadri, Nahrizul Adib, Raghavendra, U., Samanth, Jyothi, Inamdar, Mahesh Anil, Prabhu, Mukund A., Rajendra Acharya, U.
Format: Article
Language:English
Subjects:
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Artificial intelligence
Automation
Cardiovascular diseases
Computer aided diagnostic tool
directional-guided descriptor
Echocardiography
Electrocardiography
Feature extraction
Heart
Hypertension
K-nearest neighbors algorithm
Machine learning
Medical imaging
shallow classifier
Solid modeling
Ultrasonic imaging
ultrasound images
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Summary:The current work proposes an efficient assessment of hypertension (HTN) using a Directional-Guided Motion Sensitive (DGMS) descriptor and Machine Learning (ML) algorithm. The main objective of the proposed work is to automate the detection of HTN using ultrasound (US) images. The four-chamber US images from 70 healthy subjects and 70 HTN patients are collected. A novel pipelined architecture has been developed in two stages with four phases: preprocessing, feature extraction using DGMS descriptor, feature ranking and selection, and classification using shallow K-Nearest Neighbor classifier. The proposed model has achieved a classification accuracy of 98% using a set of prominent features, predominating the performance attained by other approaches. This study suggests US contains predictive signals even when standard measures are normal and lays the groundwork for artificial intelligence-assisted cardiac assessment to aid quicker, more objective diagnosis and earlier treatment. If further validated on additional diverse patient data, the technology could be integrated into clinics to enhance HTN detection through automated, early discernment of subtle manifestations missed by human eyes and traditional metrics.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3349090