Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level

Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level

<italic>Goal:</italic> To evaluate the suitability of seismocardiogram (SCG) and gyrocardiogram (GCG) recorded at the skin level to classify aortic stenosis (AS) patients from healthy volunteers, and to determine the optimal sensor position for the classification. <italic>Methods:&...

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Bibliographic Details
Main Authors: Chiara Romano, Emanuele Maiorana, Annunziata Nusca, Simone Circhetta, Sergio Silvestri, Schena Emiliano, Gian Paolo Ussia, Carlo Massaroni
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Engineering in Medicine and Biology
Subjects:
Wearables
seismocardiogram
gyrocardiogram
machine learning
aortic stenosis (AS)
sensors
Online Access:https://ieeexplore.ieee.org/document/10534834/
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Summary:<italic>Goal:</italic> To evaluate the suitability of seismocardiogram (SCG) and gyrocardiogram (GCG) recorded at the skin level to classify aortic stenosis (AS) patients from healthy volunteers, and to determine the optimal sensor position for the classification. <italic>Methods:</italic> SCG and GCG were recorded along three axes at five chest locations of fifteen healthy subjects and AS patients. Signal frames underwent feature extraction in frequency and time-frequency domains. Then, binary classification was performed through three machine learning and three deep learning methods, considering SCG, GCG, and their combination. <italic>Results:</italic> The highest classification accuracies were achieved using Support Vector Machine (SVM) classifier, with the best sensor locations being at the mitral valve for SCG signals (92.3&#x0025; accuracy) and at the pulmonary valve for GCG (92.1&#x0025;). Combining SCG and GCG data allows for further improvement in the achievable accuracy (93.5&#x0025;). Jointly exploiting SCG and GCG signals and both SVM- and ResNet18-based classifiers, 40 s of monitoring allows for reaching 97.2&#x0025; accuracy with a single sensor on the pulmonary valve. <italic>Conclusions:</italic> Combining SCG and GCG with adequate machine learning and deep learning classifiers allows reliable classification of AS patients.
ISSN:2644-1276

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