An algorithm for automatic respiratory state classifications using tracheal sound analysis

Indu Priya Eedara; Moeness G. Amin; Jeffrey I. Joseph

doi:10.1117/12.2561489

23 April 2020 An algorithm for automatic respiratory state classifications using tracheal sound analysis

Indu Priya Eedara, Moeness G. Amin, Jeffrey I. Joseph

Proceedings Volume 11395, Big Data II: Learning, Analytics, and Applications; 113950F (2020) https://doi.org/10.1117/12.2561489
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Analyzing the breathing data to classify the respiratory states has various applications in the areas of drug overdose and medical diagnosis of several other respiratory medical conditions. Tracheal sounds have shown to provide accurate breathing data with high signal to noise ratio for measuring air flow. The heartbeat signal is a source of interference for the tracheal sound measurements and hence is often filtered out prior to the tracheal sound data analysis. Filtering the heartbeat signal, however, removes a part of the tracheal sound data along with its energy and statistical information. We propose an algorithm to classify the respiratory states from the tracheal sound data despite the presence of heartbeat signals. This algorithm uses the data histogram as well as the data autocorrelation function (ACF) for feature extractions, and shows that these features, when used by a Softmax classifier, can properly discriminate among breathing conditions with classification rates exceeding 97%.

Conference Presentation

Citation Download Citation

Indu Priya Eedara, Moeness G. Amin, and Jeffrey I. Joseph "An algorithm for automatic respiratory state classifications using tracheal sound analysis", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 113950F (23 April 2020); https://doi.org/10.1117/12.2561489

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