Acoustic analysis of the knee joint in the study of osteoarthritis detection during walking

This thesis investigates the potential of non-invasive detection of knee Osteoarthritis (OA) using the sounds emitted by the knee joint during walking and captured by a single microphone. This is a novel application since, until now, there are no other methods that considered this type of signals. Clinical detection of knee OA relies on imaging techniques such as X-radiology and Magnetic Resonance Imaging. Some of these methods are expensive and impractical while others pose health risks due to radiation. Knee sounds on the other hand may offer a quick, practical and cost-effective alternative for the detection of the disease. In this thesis, the knee sound signal structure is investigated using signal processing methods for information extraction from the time, frequency, cepstral and modulation domains. Feature representations are obtained and their discriminant properties are studied using statistical methods such as the Bhattacharyya distance and supervised learning techniques such as Support Vector Machine. From this work, a statistical feature parameterisation is proposed and its efficacy for the task of healthy vs OA knee condition classification is investigated using a comprehensive experimental framework proposed in this thesis. Feature-based representations that incorporate spatiotemporal information using gait pattern variables, were also investigated for classification. Using the waveform characteristics of the acoustic pulse events detected in the signal, such representations are proposed and evaluated. This approach utilised a novel stride detection and segmentation algorithm that is based on dynamic programming and is also proposed in the thesis. This algorithm opens up potential applications in other research fields such as gait analysis.Open Acces

Novel Multimodal Sensing Systems for Wearable Knee Health Assessment

Wearable technologies for healthcare represent a popular research area, as they can provide quantitative metrics during rehabilitation, enable long-term, at-home monitoring of chronic conditions, and facilitate preventative—versus reactive—medical interventions. Moreover, their low cost makes them accessible to broad subject populations and enables more frequent measures of biomarkers. Such technologies are particularly useful for areas of medicine where the diagnostic or evaluation tools are expensive, not readily available, or time consuming. Orthopedics, in particular joint health assessment, is an area where wearable devices may provide clinicians and patients with more readily available quantitative data. The objective of this research is to investigate wearable, multimodal sensing technologies to facilitate joint health and rehabilitation monitoring, ultimately providing a “joint health score” based on evaluation of joint acoustics, electrical bioimpedance, inertial measures, and temperature data. This joint health score may be employed in various applications—including during rehabilitation after an acute injury and management of joint diseases, such as arthritis—providing an actionable metric for physicians based on the underlying physiological changes of the joint itself. This work specifically investigates the hardware for such a system. First, we examined microphones suited for wearable applications (e.g., miniature, inexpensive) that still provide robust measurements in terms of signal quality and consistency for repeated measurements. Second, we implemented a microcontroller-based system to sample high-throughput audio data as well as lower-rate electrical bioimpedance, inertial, and temperature data, which was incorporated into a fully untethered “brace.” Importantly, this work provides the fundamental hardware system for wearable knee joint health assessment.Ph.D

Advances in Sensors and Sensing for Technical Condition Assessment and NDT

The adequate assessment of key apparatus conditions is a hot topic in all branches of industry. Various online and offline diagnostic methods are widely applied to provide early detections of any abnormality in exploitation. Furthermore, different sensors may also be applied to capture selected physical quantities that may be used to indicate the type of potential fault. The essential steps of the signal analysis regarding the technical condition assessment process may be listed as: signal measurement (using relevant sensors), processing, modelling, and classification. In the Special Issue entitled “Advances in Sensors and Sensing for Technical Condition Assessment and NDT”, we present the latest research in various areas of technology

Multimodal Identification of Alzheimer's Disease: A Review

Alzheimer's disease is a progressive neurological disorder characterized by cognitive impairment and memory loss. With the increasing aging population, the incidence of AD is continuously rising, making early diagnosis and intervention an urgent need. In recent years, a considerable number of teams have applied computer-aided diagnostic techniques to early classification research of AD. Most studies have utilized imaging modalities such as magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalogram (EEG). However, there have also been studies that attempted to use other modalities as input features for the models, such as sound, posture, biomarkers, cognitive assessment scores, and their fusion. Experimental results have shown that the combination of multiple modalities often leads to better performance compared to a single modality. Therefore, this paper will focus on different modalities and their fusion, thoroughly elucidate the mechanisms of various modalities, explore which methods should be combined to better harness their utility, analyze and summarize the literature in the field of early classification of AD in recent years, in order to explore more possibilities of modality combinations

Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

Externally detected vibroarthrographic (VAG) signals bear diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the knee joint. Analysis of VAG signals could provide quantitative indices for noninvasive diagnosis of articular cartilage breakdown and staging of osteoarthritis. We propose the use of statistical parameters of VAG signals, including the form factor involving the variance of the signal and its derivatives, skewness, kurtosis, and entropy, to classify VAG signals as normal or abnormal. With a database of 89 VAG signals, screening efficiency of up to 0.82 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial basis functions

Vibration reponse analysis in orthopaedics and its application at the lumbar spine

Vibration response analysis has been carried out on human lumbar spines in-vitro and in-vivo. Random vibration in the frequency range between 20 Hz and 2 kHz was applied to the L5 spinous process in the antero-posterior direction while motion response was measured at the other spinous processes of the lumbar spine. Transfer mobility which defines the lumbar spine's motion response to vibratory force was evaluated by using the fast Fourier transform and spectral averaging technique. There was high damping during the in-vitro tests and the lumbar spine was found to behave as a segmented beam hinged at the thoracic and sacral ends. Fundamental mode shape was observed at frequencies lower than 150 Hz and this pattern was also observed with simulated fusion of the facet joints and interbody fusion. Mobility summated for the whole range of frequency could be modelled by an exponential expression. Useful parameters have been identified and they were found to relate to the lumbar spine's vibratory characteristics resulting from structural modifications. Vibration testing performed on normal subjects revealed that a relaxed lumbar spine was highly damped and non-resonant. First flexural vibration mode was observed only under the action of the back extensors. Averaged figures have been established for the coefficients of an exponential expression which fits closely to the summated mobility curve. The mobility and its attenuation coefficients in different frequency bands have been evaluated from twelve normal subjects. Localized attenuation of vibration response and the reduction in mobility were observed on a patient with osteoporotic lumbar spine. Mobility in the low frequencies was reduced while the medium and high band mobility were enhanced in patients with postero-lateral fusion and instrumentation for fixation of the lumbar spine. The attenuation pattern of these patients was consistent, and corresponded to the existence of structural enhancement.Vibration response analysis has been carried out on human lumbar spines in-vitro and in-vivo. Random vibration in the frequency range between 20 Hz and 2 kHz was applied to the L5 spinous process in the antero-posterior direction while motion response was measured at the other spinous processes of the lumbar spine. Transfer mobility which defines the lumbar spine's motion response to vibratory force was evaluated by using the fast Fourier transform and spectral averaging technique. There was high damping during the in-vitro tests and the lumbar spine was found to behave as a segmented beam hinged at the thoracic and sacral ends. Fundamental mode shape was observed at frequencies lower than 150 Hz and this pattern was also observed with simulated fusion of the facet joints and interbody fusion. Mobility summated for the whole range of frequency could be modelled by an exponential expression. Useful parameters have been identified and they were found to relate to the lumbar spine's vibratory characteristics resulting from structural modifications. Vibration testing performed on normal subjects revealed that a relaxed lumbar spine was highly damped and non-resonant. First flexural vibration mode was observed only under the action of the back extensors. Averaged figures have been established for the coefficients of an exponential expression which fits closely to the summated mobility curve. The mobility and its attenuation coefficients in different frequency bands have been evaluated from twelve normal subjects. Localized attenuation of vibration response and the reduction in mobility were observed on a patient with osteoporotic lumbar spine. Mobility in the low frequencies was reduced while the medium and high band mobility were enhanced in patients with postero-lateral fusion and instrumentation for fixation of the lumbar spine. The attenuation pattern of these patients was consistent, and corresponded to the existence of structural enhancement

Advanced analyses of physiological signals and their role in Neonatal Intensive Care

Preterm infants admitted to the neonatal intensive care unit (NICU) face an array of life-threatening diseases requiring procedures such as resuscitation and invasive monitoring, and other risks related to exposure to the hospital environment, all of which may have lifelong implications. This thesis examined a range of applications for advanced signal analyses in the NICU, from identifying of physiological patterns associated with neonatal outcomes, to evaluating the impact of certain treatments on physiological variability. Firstly, the thesis examined the potential to identify infants at risk of developing intraventricular haemorrhage, often interrelated with factors leading to preterm birth, mechanical ventilation, hypoxia and prolonged apnoeas. This thesis then characterised the cardiovascular impact of caffeine therapy which is often administered to prevent and treat apnoea of prematurity, finding greater pulse pressure variability and enhanced responsiveness of the autonomic nervous system. Cerebral autoregulation maintains cerebral blood flow despite fluctuations in arterial blood pressure and is an important consideration for preterm infants who are especially vulnerable to brain injury. Using various time and frequency domain correlation techniques, the thesis found acute changes in cerebral autoregulation of preterm infants following caffeine therapy. Nutrition in early life may also affect neurodevelopment and morbidity in later life. This thesis developed models for identifying malnutrition risk using anthropometry and near-infrared interactance features. This thesis has presented a range of ways in which advanced analyses including time series analysis, feature selection and model development can be applied to neonatal intensive care. There is a clear role for such analyses in early detection of clinical outcomes, characterising the effects of relevant treatments or pathologies and identifying infants at risk of later morbidity

Dynamics, Electromyography and Vibroarthrography as Non-Invasive Diagnostic Tools: Investigation of the Patellofemoral Joint

The knee joint plays an essential role in the human musculoskeletal system. It has evolved to withstand extreme loading conditions, while providing almost frictionless joint movement. However, its performance may be disrupted by disease, anatomical deformities, soft tissue imbalance or injury. Knee disorders are often puzzling, and accurate diagnosis may be challenging. Current evaluation approach is usually limited to a detailed interview with the patient, careful physical examination and radiographic imaging. The X-ray screening may reveal bone degeneration, but does not carry sufficient information of the soft tissue conditions. More advanced imaging tools such as MRI or CT are available, but expensive, time consuming and can be used only under static conditions. Moreover, due to limited resolution the radiographic techniques cannot reveal early stage arthritis. The arthroscopy is often the only reliable option, however due to its semi-invasive nature, it cannot be considered as a practical diagnostic tool. Therefore, the motivation for this work was to combine three scientific methods to provide a comprehensive, non-invasive evaluation tool bringing insight into the in vivo, dynamic conditions of the knee joint and articular cartilage degeneration. Electromyography and inverse dynamics were employed to independently determine the forces present in several muscles spanning the knee joint. Though both methods have certain limitations, the current work demonstrates how the use of these two methods concurrently enhances the biomechanical analysis of the knee joint conditions, especially the performance of the extensor mechanism. The kinetic analysis was performed for 12 TKA, 4 healthy individuals in advanced age and 4 young subjects. Several differences in the knee biomechanics were found between the three groups, identifying age-related and post-operative decrease in the extensor mechanism efficiency, explaining the increased effort of performing everyday activities experienced by the elderly and TKA subjects. The concept of using accelerometers to assess the cartilage degeneration has been proven based on a group of 23 subjects with non-symptomatic knees and 52 patients suffering from knee arthritis. Very high success (96.2%) of pattern classification obtained in this work clearly demonstrates that vibroarthrography is a promising, non-invasive and low-cost technique offering screening capabilities

Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions