ArticleScientific reports2021
Efficient detection of aortic stenosis using morphological characteristics of cardiomechanical signals and heart rate variability parameters.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Wearable SENsor to Diagnose and Assess SEverity of Aortic Stenosis (SENSE-AS): A Proof-of-Concept Study.JACC. Advances · 2026Article
- Severe aortic stenosis detection using seismocardiography.Open heart · 2026Article
- Advancing entropy analysis for heart rate variability: clinical insights for aging and diabetes.Frontiers in physiology · 2026Review
- Machine learning-based early detection of abnormal heart rate in critically ill patients: a real-world clinical dataset study with automated alert system integration.Frontiers in artificial intelligence · 2026Article
- Starfish-inspired wearable bioelectronic systems for physiological signal monitoring during motion and real-time heart disease diagnosis.Science advances · 2025Article
- Detection of severe aortic stenosis by clinicians versus artificial intelligence: A retrospective clinical cohort study.American heart journal plus : cardiology research and practice · 2024Article
- Detecting aortic valve stenosis based on the non-invasive blood pressure waveform-a proof of concept study.GeroScience · 2024Observational
- Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer.Journal for immunotherapy of cancer · 2024Article
- DEEP LEARNING FOR AUTOMATED DETECTION OF BREAST CANCER IN DEEP ULTRAVIOLET FLUORESCENCE IMAGES WITH DIFFUSION PROBABILISTIC MODEL.Proceedings. IEEE International Symposium on Biomedical Imaging · 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
- Enhanced detection of severe aortic stenosis via artificial intelligence: a clinical cohort study.Open heart · 2023Article
- Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases.Sensors (Basel, Switzerland) · 2023Article
- Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer.Frontiers in oncology · 2023Article
- Psychiatric symptoms and emotional impact of the COVID-19 pandemic on Italian adolescents during the third lockdown: a cross-sectional cohort study.Scientific reports · 2022Article
- Can Seismocardiogram Fiducial Points Be Used for the Routine Estimation of Cardiac Time Intervals in Cardiac Patients?Frontiers in physiology · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent research has shown promising results for the detection of aortic stenosis (AS) using cardio-mechanical signals. However, they are limited by two main factors: lacking physical explanations for decision-making on the existence of AS, and the need for auxiliary signals. The main goal of this paper is to address these shortcomings through a wearable inertial measurement unit (IMU), where the physical causes of AS are determined from IMU readings. To this end, we develop a framework based on seismo-cardiogram (SCG) and gyro-cardiogram (GCG) morphologies, where highly-optimized algorithms are designed to extract features deemed potentially relevant to AS. Extracted features are then analyzed through machine learning techniques for AS diagnosis. It is demonstrated that AS could be detected with 95.49-100.00% confidence. Based on the ablation study on the feature space, the GCG time-domain feature space holds higher consistency, i.e., 95.19-100.00%, with the presence of AS than HRV parameters with a low contribution of 66.00-80.00%. Furthermore, the robustness of the proposed method is evaluated by conducting analyses on the classification of the AS severity level. These analyses are resulted in a high confidence of 92.29%, demonstrating the reliability of the proposed framework. Additionally, game theory-based approaches are employed to rank the top features, among which GCG time-domain features are found to be highly consistent with both the occurrence and severity level of AS. The proposed framework contributes to reliable, low-cost wearable cardiac monitoring due to accurate performance and usage of solitary inertial sensors.
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