Güler Emin Cagatay, Sankur Bülent, Kahya Yasemin P, Raudys Sarunas
Biomedical Engineering Institute, Bogaziçi University, Bebek, 34342 Istanbul, Turkey.
Comput Biol Med. 2005 Jan;35(1):67-83. doi: 10.1016/j.compbiomed.2003.11.001.
The classification problem of respiratory sound signals has been addressed by taking into account their cyclic nature, and a novel hierarchical decision fusion scheme based on the cooperation of classifiers has been developed. Respiratory signals from three different classes are partitioned into segments, which are later joined to form six different phases of the respiration cycle. Multilayer perceptron classifiers classify the parameterized segments from each phase and decision vectors obtained from different phases are combined using a nonlinear decision combination function to form a final decision on each subject. Furthermore a new regularization scheme is applied to the data to stabilize training and consultation.
通过考虑呼吸音信号的周期性来解决其分类问题，并开发了一种基于分类器协作的新型分层决策融合方案。来自三个不同类别的呼吸信号被分割成段，随后这些段被连接起来形成呼吸周期的六个不同阶段。多层感知器分类器对每个阶段的参数化段进行分类，并使用非线性决策组合函数将从不同阶段获得的决策向量组合起来，以对每个受试者形成最终决策。此外，一种新的正则化方案被应用于数据，以稳定训练和咨询。
