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.

中文翻译：

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使用来自胸部心脏机械信号的时频特征对主动脉瓣狭窄进行分类

目的：本文介绍了一种使用从可穿戴传感器收集的胸部心脏机械信号的时频特征检测和分类主动脉瓣狭窄 (AS) 的新方法，即地震心电图 (SCG) 和陀螺心电图 (GCG)信号。这种方法可以潜在地监测诊所外的高危患者。方法：从 20 名 AS 患者和 20 名健康受试者收集实验测量值。首先，提出了一种数字信号处理框架来提取时频特征。然后通过方差分析测试选择特征。使用决策树、随机森林和人工神经网络方法评估不同的特征组合。进行了两个分类任务。第一个任务是对正常受试者和 AS 患者进行二元分类。第二个任务是对同时存在心脏瓣膜病的 AS 患者进行多类分类。结果：在二元分类任务中，决策树的平均准确率为 96.25%，随机森林的平均准确率为 97.43%，神经网络的平均准确率为 95.56%。最好的性能来自将 SCG 和 GCG 特征与随机森林分类器相结合。在多类分类中，使用随机森林分类器和 SCG 特征的最佳性能为 92.99%。结论：结果表明，无论是在二分类任务还是多分类任务中，该解决方案都是一种可行的主动脉瓣狭窄分类方法。它还表明，大多数重要的时频特征都在 11 Hz 以下。 意义：