Near infrared spectroscopy for non-destructive evaluation of articular cartilage

There is a need for an accurate real-time quantitative system that would enhance decision-making in the treatment of osteoarthritis. To achieve this objective, significant research is required that will enable articular cartilage properties to be measured and categorized for health and functionality without the need for laboratory tests involving biopsies for pathological evaluation. Such a system would provide the capability of access to the internal condition of the cartilage matrix and thus extend the vision-based arthroscopy that is currently used beyond the subjective evaluation of surgeons. The system required must be able to non-destructively probe the entire thickness of the cartilage and its immediate subchondral bone layer. In this thesis, near infrared spectroscopy is investigated for the purpose mentioned above. The aim is to relate it to the structure and load bearing properties of the cartilage matrix to the near infrared absorption spectrum and establish functional relationships that will provide objective, quantitative and repeatable categorization of cartilage condition outside the area of visible degradation in a joint. Based on results from traditional mechanical testing, their innovative interpretation and relationship with spectroscopic data, new parameters were developed. These were then evaluated for their consistency in discriminating between healthy viable and degraded cartilage. The mechanical and physico-chemical properties were related to specific regions of the near infrared absorption spectrum that were identified as part of the research conducted for this thesis. The relationships between the tissue's near infrared spectral response and the new parameters were modeled using multivariate statistical techniques based on partial least squares regression (PLSR). With significantly high levels of statistical correlation, the modeled relationships were demonstrated to possess considerable potential in predicting the properties of unknown tissue samples in a quick and non-destructive manner. In order to adapt near infrared spectroscopy for clinical applications, a balance between probe diameter and the number of active transmit-receive optic fibres must be optimized. This was achieved in the course of this research, resulting in an optimal probe configuration that could be adapted for joint tissue evaluation. Furthermore, as a proof-of-concept, a protocol for obtaining the new parameters from the near infrared absorption spectra of cartilage was developed and implemented in a graphical user interface (GUI)-based software, and used to assess cartilage-on-bone samples in vitro. This conceptual implementation has been demonstrated, in part by the individual parametric relationship with the near infrared absorption spectrum, the capacity of the proposed system to facilitate real-time, non-destructive evaluation of cartilage matrix integrity. In summary, the potential of the optical near infrared spectroscopy for evaluating articular cartilage and bone laminate has been demonstrated in this thesis. The approach could have a spin-off for other soft tissues and organs of the body. It builds on the earlier work of the group at QUT, enhancing the near infrared component of the ongoing research on developing a tool for cartilage evaluation that goes beyond visual and subjective methods

Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea

The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overcome this shortcoming, we aimed to develop an accurate deep learning approach for automatic classification of sleep stages and to study the effect of OSA severity on the classification accuracy. Overnight polysomnographic recordings from a public dataset of healthy individuals (Sleep-EDF, n=153) and from a clinical dataset (n=891) of patients with suspected OSA were used to develop a combined convolutional and long short-term memory neural network. On the public dataset, the model achieved sleep staging accuracy of 83.7% (κ=0.77) with a single frontal EEG channel and 83.9% (κ=0.78) when supplemented with EOG. For the clinical dataset, the model achieved accuracies of 82.9% (κ=0.77) and 83.8% (κ=0.78) with a single EEG channel and two channels (EEG+EOG), respectively. The sleep staging accuracy decreased with increasing OSA severity. The single-channel accuracy ranged from 84.5% (κ=0.79) for individuals without OSA diagnosis to 76.5% (κ=0.68) for severe OSA patients. In conclusion, deep learning enables automatic sleep staging for suspected OSA patients with high accuracy and expectedly, the accuracy lowered with increasing OSA severity. Furthermore, the accuracies achieved in the public dataset were superior to previously published state-of-the-art methods. Adding an EOG channel did not significantly increase the accuracy. The automatic, single-channel-based sleep staging could enable easy, accurate, and cost-efficient integration of EEG recording into diagnostic ambulatory recordings