Online classification of lung sounds using DSP

In this paper a real-time diagnosis system, based on Motorola's 56311 Digital Signal Processor (DSP), is used for the classification of lung sounds into two classes: healthy and pathological. The instrument has two inputs the first of which is from a microphone placed on the chest of the patient while the other is from a flowmeter that is used to label the lung sounds as belonging to the inspiration or expiration cycle. The sampled lung sound of a full respiration cycle is divided into 6 phases with the help of the flowmeter signal, and each phase is divided further into 10 overlapping segments. Each segment is modeled by an Auto Regressive (AR) model of order 6 by means of the Levinson-Durbin algorithm. The classification process is done using two classifiers: k-Nearest Neighbor (k-NN) classifier with Itakura and Euclidean distance measures, and Minimum distance classifier with the Mahalanobis distance measure. The software was written entirely in assembly language and the result of the classification process is displayed on a character display (LCD).