DeepPAD: Detection of Peripheral Arterial Disease Using Deep Learning-Reference-Cited by-同舟云学术

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DeepPAD: Detection of Peripheral Arterial Disease Using Deep Learning

Published:2022-08-15 Issue:16 Volume:22 Page:16254-16262
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Forghani Nosratallah¹^ORCID,Maghooli Keivan¹^ORCID,Dabanloo Nader Jafarnia¹,Farahani Ali Vasheghani²,Forouzanfar Mohamad³^ORCID

Affiliation:

1. Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2. Cardiac Primary Prevention Research Center, Cardiovascular Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran

3. Department of Systems Engineering, École de technologie supérieure (ÉTS), Université du Québec, Montreal, QC, Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/9857501/09827927.pdf?arnumber=9827927

Reference38 articles.

1. Deep Boltzmann Regression With Mimic Features for Oscillometric Blood Pressure Estimation

2. Automated, Portable, and Low-Cost System for Home Screening of Peripheral Arterial Disease

3. Photoplethysmography and its application in clinical physiological measurement

4. Photoplethysmography detection of lower limb peripheral arterial occlusive disease: a comparison of pulse timing, amplitude and shape characteristics

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1. Deep learning on medical image analysis;CAAI Transactions on Intelligence Technology;2024-06-24

2. Design of a Hybrid Bioinspired Deep Learning Model for Identification of Heart Diseases Using Clinical Parameters;SN Computer Science;2023-08-10

3. Diagnosis of Peripheral Artery Disease Using Backflow Abnormalities in Proximal Recordings of Accelerometer Contact Microphone (ACM);IEEE Journal of Biomedical and Health Informatics;2023-01

4. User-Tailored Hand Gesture Recognition System for Wearable Prosthesis and Armband Based on Surface Electromyogram;IEEE Transactions on Instrumentation and Measurement;2022

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