Humayun Ahmed Imtiaz, Ghaffarzadegan Shabnam, Feng Zhe, Hasan Taufiq
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1408-1411. doi: 10.1109/EMBC.2018.8512578.
Automatic heart sound abnormality detection can play a vital role in the early diagnosis of heart diseases, particularly in low-resource settings. The state-of-the-art algorithms for this task utilize a set of Finite Impulse Response (FIR) band-pass filters as a front-end followed by a Convolutional Neural Network (CNN) model. In this work, we propound a novel CNN architecture that integrates the front-end band-pass filters within the network using time-convolution (tConv) layers, which enables the FIR filter-bank parameters to become learnable. Different initialization strategies for the learnable filters, including random parameters and a set of predefined FIR filter-bank coefficients, are examined. Using the proposed tConv layers, we add constraints to the learnable FIR filters to ensure linear and zero phase responses. Experimental evaluations are performed on a balanced 4-fold cross-validation task prepared using the PhysioNet/CinC 2016 dataset. Results demonstrate that the proposed models yield superior performance compared to the state-of-the-art system, while the linear phase FIR filter-bank method provides an absolute improvement of 9.54% over the baseline in terms of an overall accuracy metric.
自动心音异常检测在心脏病的早期诊断中可以发挥至关重要的作用，尤其是在资源匮乏的环境中。用于此任务的最先进算法利用一组有限脉冲响应（FIR）带通滤波器作为前端，随后是卷积神经网络（CNN）模型。在这项工作中，我们提出了一种新颖的CNN架构，该架构使用时间卷积（tConv）层将前端带通滤波器集成到网络中，这使得FIR滤波器组参数能够被学习。我们研究了可学习滤波器的不同初始化策略，包括随机参数和一组预定义的FIR滤波器组系数。使用所提出的tConv层，我们对可学习的FIR滤波器添加约束，以确保线性和零相位响应。在使用PhysioNet/CinC 2016数据集准备的平衡4折交叉验证任务上进行了实验评估。结果表明，与最先进的系统相比，所提出的模型具有卓越的性能，而线性相位FIR滤波器组方法在整体准确率指标方面比基线绝对提高了9.54%。
