对比不同窗函数和长度对心音信号分类的影响，发现高斯窗75毫秒效果最佳。
Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals
- 采用三种窗形、每种三长度，滑动分段提取心音特征
- 75毫秒高斯窗分类准确率最高，三角窗次之
- 矩形窗表现最差，优于基线方法
心音信号（即心电图信号，PCG）可用于自动诊断心血管疾病。此类分类任务可借助双向长短期记忆网络（biLSTM）实现，该网络基于标注的PCG信号提取的特征进行训练。由于PCG信号具有非平稳性，建议使用特定形状和长度的滑动窗从多个短时段信号中提取特征。然而，某些窗函数会产生不利的频谱旁瓣，导致特征失真。因此，应根据分类性能调整窗函数形状与长度。本文对三种窗形（矩形、三角、高斯）各选取三个长度进行了实验评估。在统计特征提取后，使用biLSTM网络进行训练与测试，结果表明：当采用高斯窗时性能最佳，三角窗在75毫秒长度下表现接近高斯窗；而矩形窗虽常见，却是最差选择。此外，75毫秒高斯窗的分类性能优于基线方法。
原文摘要 · Abstract (English)
Heart sound signals, phonocardiography (PCG) signals, allow for the automatic diagnosis of potential cardiovascular pathology. Such classification task can be tackled using the bidirectional long short-term memory (biLSTM) network, trained on features extracted from labeled PCG signals. Regarding the non-stationarity of PCG signals, it is recommended to extract the features from multiple short-length segments of the signals using a sliding window of certain shape and length. However, some window contains unfavorable spectral side lobes, which distort the features. Accordingly, it is preferable to adapt the window shape and length in terms of classification performance. We propose an experimental evaluation for three window shapes, each with three window lengths. The biLSTM network is trained and tested on statistical features extracted, and the performance is reported in terms of the window shapes and lengths. Results show that the best performance is obtained when the Gaussian window is used for splitting the signals, and the triangular window competes with the Gaussian window for a length of 75 ms. Although the rectangular window is a commonly offered option, it is the worst choice for splitting the signals. Moreover, the classification performance obtained with a 75 ms Gaussian window outperforms that of a baseline method.
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