Kaya Emine Merve, Elhilali Mounya
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:3949-52. doi: 10.1109/EMBC.2013.6610409.
Although great strides have been achieved in computer-aided diagnosis (CAD) research, a major remaining problem is the ability to perform well under the presence of significant noise. In this work, we propose a mechanism to find instances of potential interest in time series for further analysis. Adaptive Kalman filters are employed in parallel among different feature axes. Lung sounds recorded in noisy conditions are used as an example application, with spectro-temporal feature extraction to capture the complex variabilities in sound. We demonstrate that both disease indicators and distortion events can be detected, reducing long time series signals into a sparse set of relevant events.
尽管计算机辅助诊断（CAD）研究已经取得了长足的进步，但一个主要的遗留问题是在存在大量噪声的情况下仍能良好运行的能力。在这项工作中，我们提出了一种机制，用于在时间序列中找到潜在感兴趣的实例，以便进行进一步分析。自适应卡尔曼滤波器在不同特征轴之间并行使用。以在嘈杂条件下记录的肺音作为示例应用，通过频谱-时间特征提取来捕捉声音中的复杂变化。我们证明，疾病指标和失真事件都可以被检测到，将长时间序列信号简化为一组稀疏的相关事件。
