Publications by authors named "Nicole D Aranoff"
Article Synopsis
- * This paper introduces a wearable inertial measurement unit (IMU) that captures physical causes of AS through seismo-cardiogram (SCG) and gyro-cardiogram (GCG) data, utilizing optimized algorithms and machine learning for diagnosis.
- * The proposed framework shows a detection accuracy of 95.49-100.00% for AS and 92.29% for determining AS severity, making it a reliable and cost-effective solution for cardiac monitoring using only inertial sensors.
Mean Pressure Gradient Prediction Based on Chest Angular Movements and Heart Rate Variability Parameters.
Annu Int Conf IEEE Eng Med Biol Soc
November 2021
Article Synopsis
- The study explores classifying the severity of aortic stenosis (AS) using low-cost wearable sensors that analyze angular chest movements instead of expensive ultrasound echocardiography.
- It utilizes machine learning techniques, particularly the Light Gradient-Boosted Machine, to achieve high accuracy (94.44%) in classifying AS severity into mild, moderate, and severe cases.
- Key findings indicate that isovolumetric contraction time and isovolumetric relaxation time are crucial features for determining AS severity, suggesting this method could serve as a viable, affordable alternative for clinical assessments.
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).
View Article and Find Full Text PDFClassification of Aortic Stenosis Before and After Transcatheter Aortic Valve Replacement Using Cardio-mechanical Modalities.
Annu Int Conf IEEE Eng Med Biol Soc
July 2020
This paper reports our study on the impact of transcatheter aortic valve replacement (TAVR) on the classification of aortic stenosis (AS) patients using cardio-mechanical modalities. Machine learning algorithms such as decision tree, random forest, and neural network were applied to conduct two tasks. Firstly, the pre- and post-TAVR data are evaluated with the classifiers trained in the literature.
View Article and Find Full Text PDFObjectives: This paper introduces a novel method for the detection and classification of aortic stenosis (AS) using the time-frequency features of chest cardio-mechanical signals collected from wearable sensors, namely seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. Such a method could potentially monitor high-risk patients out of the clinic.
Methods: Experimental measurements were collected from twenty patients with AS and twenty healthy subjects.
A Binary Classification of Cardiovascular Abnormality Using Time-Frequency Features of Cardio-mechanical Signals.
Annu Int Conf IEEE Eng Med Biol Soc
July 2018
This paper introduces a novel method of binary classification of cardiovascular abnormality using the time-frequency features of cardio-mechanical signals, namely seismocardiography (SCG) and gyrocardiography (GCG) signals. A digital signal processing framework is proposed which utilizes decision tree and support vector machine methods with features generated by continuous wavelet transform. Experimental measurements were collected from twelve patients with cardiovascular diseases as well as twelve healthy subjects to evaluate the proposed method.
View Article and Find Full Text PDF