An acoustical respiratory phase segmentation algorithm using genetic approach.
This paper proposes a robust and fully automated respiratory phase segmentation method using single channel tracheal breath sounds (TBS) recordings of different types. The estimated number of respiratory segments in a TBS signal is firstly obtained based on noise estimation and nonlinear mapping. Respiratory phase boundaries are then located through the generations of multi-population genetic algorithm by introducing a new evaluation function based on sample entropy (SampEn) and a heterogeneity measure. The performance of the proposed method is analyzed for single channel TBS recordings of various types. An overall respiratory phase segmentation accuracy is found to be 12 +/- 5 ms for normal TBS and 21 +/- 9 ms for adventitious sounds. The results show the robustness and effectiveness of the proposed segmentation method. The proposed method has been a successful attempt to solve the clinical application challenge faced by the existing phase segmentation methods in terms of respiratory dysfunctions.
Jin
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School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue 50, Singapore 639798, Singapore. jinf0001@ntu.edu.sg
Sattar
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Goh
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D000293:Adolescent; D000465:Algorithms; D002648:Child; D005260:Female; D006801:Humans; D008297:Male; D015656:Respiratory Mechanics / Q000502:physiology; D012135:Respiratory Sounds / Q000175:diagnosis* / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000379:methods; D014132:Trachea / Q000503:physiopathology; D055815:Young Adult