ArticleScientific reports2020
Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- Machine learning-based models for intraoperative blood loss of retroperitoneal laparoscopic adrenalectomy.Surgical endoscopy · 2026Article
- Severe aortic stenosis detection using seismocardiography.Open heart · 2026Article
- A Machine Learning Approach to Microcalorimetric Pattern Classification of Pathogens in Synovial Fluid.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- Clinical Performance Evaluation of an Artificial Intelligence-Based Tool for Predicting the Presence of Obstructive Coronary Artery Disease: Protocol for a Cohort Observational Study.JMIR research protocols · 2025Article
- Automated valvular heart disease detection using heart sound with a deep learning algorithm.International journal of cardiology. Heart & vasculature · 2024Article
- Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level.IEEE open journal of engineering in medicine and biology · 2024Article
- Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis.Reviews in cardiovascular medicine · 2024Review
- Severe aortic stenosis detection by deep learning applied to echocardiography.European heart journal · 2023Article
- Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning.Proceedings of machine learning research · 2023Article
- Automated Detection of Aortic Stenosis Using Machine Learning.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2023Article
- Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases.Sensors (Basel, Switzerland) · 2023Article
- Clinical Applications of Artificial Intelligence-An Updated Overview.Journal of clinical medicine · 2022Review
- Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions.Bioengineering (Basel, Switzerland) · 2022Review
- Transfer learning for non-image data in clinical research: A scoping review.PLOS digital health · 2022Article
- Efficient detection of aortic stenosis using morphological characteristics of cardiomechanical signals and heart rate variability parameters.Scientific reports · 2021Article
- A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms.Proceedings of machine learning research · 2021Article
- An Open-Access Database for the Evaluation of Cardio-Mechanical Signals From Patients With Valvular Heart Diseases.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT). Performance comparisons were conducted among several machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neural network, and extreme gradient boosting. In addition, a two-dimensional convolutional neural network (2D-CNN) was developed using the CWT coefficients as images. The 2D-CNN was made with a custom-built architecture and a CNN based on Mobile Net via transfer learning. After the reduction of features by 95.47%, the results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 by XGBoost. Via the 2D-CNN framework, the transfer learning of Mobile Net shows an accuracy of 0.91, while the custom-constructed classifier reveals an accuracy of 0.89. Our results validate the effectiveness of the feature selection and classification framework. They also show a promising potential for the implementation of deep learning tools on the classification of AS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.