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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. 

This paper describes an open-access database for seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. The archive comprises SCG and GCG recordings sourced from and processed at multiple sites worldwide, including Columbia University Medical Center and Stevens Institute of Technology in the United States, as well as Southeast University, Nanjing Medical University, and the first affiliated hospital of Nanjing Medical University in China. It includes electrocardiogram (ECG), SCG, and GCG recordings collected from 100 patients with various conditions of valvular heart diseases such as aortic and mitral stenosis. The recordings were collected from clinical environments with the same types of wearable sensor patch. Besides the raw recordings of ECG, SCG, and GCG signals, a set of hand-corrected fiducial point annotations is provided by manually checking the results of the annotated algorithm. The database also includes relevant echocardiogram parameters associated with each subject such as ejection fraction, valve area, and mean gradient pressure. 

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. 

Objectives: 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. Firstly, a digital signal processing framework is proposed to extract time-frequency features. The features are then selected via the analysis of variance test. Different combinations of features are evaluated using the decision tree, random forest, and artificial neural network methods. Two classification tasks are conducted. The first task is a binary classification between normal subjects and AS patients. The second task is a multi-class classification of AS patients with co-existing valvular heart diseases. Results: In the binary classification task, the average accuracies achieved are 96.25% from decision tree, 97.43% from random forest, and 95.56% from neural network. The best performance is from combined SCG and GCG features with random forest classifier. In the multi-class classification, the best performance is 92.99% using the random forest classifier and SCG features. Conclusion: The results suggest that the solution could be a feasible method for classifying aortic stenosis, both in the binary and multi-class tasks. It also indicates that most of the important time-frequency features are below 11 Hz. Significance: The proposed method shows great potential to provide continuous monitoring of valvular heart diseases to prevent patients from sudden critical cardiac situations.