IEEE J Biomed Health Inform. 2020 Aug;24(8):2189-2198. doi: 10.1109/JBHI.2020.2970252. Epub 2020 Jan 31.
Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases. While machine learning based systems can aid in automatically screening patients, the robustness of these systems is affected by numerous factors including the stethoscope/sensor, environment, and data collection protocol. This article studies the adverse effect of domain variability on heart sound abnormality detection and develops strategies to address this problem.
We propose a novel Convolutional Neural Network (CNN) layer, consisting of time-convolutional (tConv) units, that emulate Finite Impulse Response (FIR) filters. The filter coefficients can be updated via backpropagation and be stacked in the front-end of the network as a learnable filterbank.
On publicly available multi-domain datasets, the proposed method surpasses the top-scoring systems found in the literature for heart sound abnormality detection (a binary classification task). We utilized sensitivity, specificity, F-1 score and Macc (average of sensitivity and specificity) as performance metrics. Our systems achieved relative improvements of up to 11.84% in terms of MAcc, compared to state-of-the-art methods.
The results demonstrate the effectiveness of the proposed learnable filterbank CNN architecture in achieving robustness towards sensor/domain variability in PCG signals.
The proposed methods pave the way for deploying automated cardiac screening systems in diversified and underserved communities.
心脏听诊是早期诊断心脏病最常用的非侵入性和具有成本效益的方法。虽然基于机器学习的系统可以帮助自动筛选患者，但这些系统的稳健性受到许多因素的影响，包括听诊器/传感器、环境和数据采集协议。本文研究了领域变异性对心音异常检测的不利影响，并提出了解决该问题的策略。
我们提出了一种新的卷积神经网络 (CNN) 层，由时间卷积 (tConv) 单元组成，该单元模拟有限脉冲响应 (FIR) 滤波器。滤波器系数可以通过反向传播进行更新，并作为可学习滤波器组堆叠在网络的前端。
在公开的多领域数据集上，我们提出的方法在心音异常检测（二分类任务）方面超过了文献中得分最高的系统。我们使用灵敏度、特异性、F1 分数和 Macc（灵敏度和特异性的平均值）作为性能指标。与最先进的方法相比，我们的系统在 Macc 方面的相对提高了高达 11.84%。
结果表明，所提出的可学习滤波器组 CNN 架构在实现对 PCG 信号中传感器/领域变异性的稳健性方面是有效的。
所提出的方法为在多样化和服务不足的社区中部署自动化心脏筛查系统铺平了道路。
