摘要
AIM:To explore the more accurate lung sounds auscultation technology in high battlefield noise environment.METHODS: In this study, we restrain high background noise using a new method-adaptive noise canceling based on independent component analysis (ANC-ICA), the method, by incorporating both second-order and higher-order statistics can remove noise components of the primary input signal based on statistical independence.RESULTS:The algorithm retained the local feature of lung sounds while eliminating high background noise, and performed more effectively than the conventional LMS algorithm.CONCLUSION:This method can cancel high battlefield noise of lung sounds effectively thus can help diagnose lung disease more accurately.
AIM:To explore the more accurate lung sounds auscultation technology in high battlefield noise environment. METHODS: In this study, we restrain high background noise using a new method-adaptive noise canceling based on independent component analysis ( ANC-ICA) , the method, by incorporating both second-order and higher-order statistics can remove noise components of the primary input signal based on statistical independence. RESULTS: The algorithm retained the local feature of lung sounds while eliminating high background noise, and performed more effectively than the conventional LMS algorithm. CONCLUSION: This method can cancel high battlefield noise of lung sounds effectively thus can help diagnose lung disease more accurately.
基金
Supported by Obligatory Budget of Chine PLA in the "tenth-five years"(OIL077)
