An algorithm for automatic respiratory state classifications using tracheal sound analysis

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%.