Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography
Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography
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应用新颖的时频矩奇异值分解方法和人工神经网络进行心冲击描记术
DOI：
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发表时间：
2007
影响因子：
1.9
通讯作者：
A. Värri
中科院分区：
文献类型：
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作者：
A. Akhbardeh;S. Junnila;M. Koivuluoma;T. Koivistoinen;A. Värri
奇异值分解（SVD）是一种计算矩阵奇异值的方法。然后，如果它被用来寻找一个由1或1由一个数组的SV与元素代表一个信号的样本，它将返回只有一个奇异值，这是不够的，以表达整个信号。为了克服这个问题，我们设计了一种新的特征提取方法，我们称之为“时频矩奇异值分解（TFM SVD）”。在这种新方法中，我们使用时间序列和频率序列（信号的傅立叶变换）的统计特征。然后将该信息提取到具有固定结构的某个矩阵中，并寻找该矩阵的SV。该变换可用作模式聚类方法中的预处理阶段。使用它的结果表明，包括这种变换和分类器的组合系统的性能是使用其他特征提取方法，如小波变换的性能相媲美。为了评估TFM-SVD，我们应用这种新方法和人工神经网络（ANN）的心冲击图（BCG）数据聚类寻找可能的心脏病的六个测试对象。来自测试对象的BCG是使用我们项目开发的椅状心冲击描记器记录的。这种结合自动记录和分析的设备将适合在许多地方使用，例如家庭、办公室等。实验结果表明，该方法具有较高的性能，对BCG波形延迟和非线性干扰几乎不敏感。
As we know, singular value decomposition (SVD) is designed for computing singular values (SVs) of a matrix. Then, if it is used for finding SVs of an -by-1 or 1-by- array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call 'time-frequency moments singular value decomposition (TFM-SVD).' In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal). This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs) for ballistocardiogram (BCG) data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.