Automatic cycle identification in tidal breathing signals
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In this paper, we introduce a novel cycle identification algorithm using Matlab programming to automatically identify cycles in tidal breathing signals. The algorithm is designed in four steps using filtering, derivation, and other signal processing techniques. To verify the effectiveness of the proposed algorithm, its results are compared with those of cycles identified manually by a human coder. Simulations results show that despite the complexity of the respiratory signals, the proposed algorithm can identify cycles more correctly and more efficiently than cycles identified by hand-coding. This algorithm can serve as an important first step toward timely identification and coding of more complex respiratory signals, such as those underlying speech productions.
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science.