Analysis of spine sounds for spinal health assessment

This thesis proposes a spinal health assessment system based on acoustic biosignals. The aim of this study is to offer an alternative to the conventional spinal health assessment techniques such as MR, CT or x-ray scans. As conventional methods are time-consuming, expensive and harmful (radiation risk caused by medical scanning techniques), a cheap, fast and harmless method is proposed. It is observed that individuals with spinal health problems have unusual sounds. Using automatic speech recognition (ASR) algorithms, a diagnosis algorithm was developed for classifying joint sounds collected from the vertebrae of human subjects. First, feature parameters are extracted from spinal sounds. One of the most popular feature parameters used in speech recognition are Mel Frequency Cepstrum Coefficients (MFCC). MFCC parameters are classified using Artificial Neural Networks (ANN). In addition, the scattering transform cepstral coeffi- cients (STCC) algorithm is implemented as an alternative to the mel filterbank in MFCC. The correlation between the medical history of the subjects and the \click" sound in the collected sound data is the basis of the classification algorithm. In the light of collected data, it is observed that \click" sounds are detected in the individuals who have suffered low back pain (slipped disk) but not in healthy individuals. The identification of the \click" sound is carried out by using MFCC/STCC and ANN. The system has 92.2% success rate of detecting \click" sounds when MFCC based algorithm is used. The success rate is 83.5% when STCC feature extraction scheme is used.