Journal: Medical engineering & physics
To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features.
Functional electrical stimulation (FES) has shown effectiveness in restoring upper-limb movement post-stroke when applied to assist participants' voluntary intention during repeated, motivating tasks. Recent clinical trials have used advanced controllers that precisely adjust FES to assist functional reach and grasp tasks with FES applied to three muscle groups, showing significant reduction in impairment. The system reported in this paper advances the state-of-the-art by: (1) integrating an FES electrode array on the forearm to assist complex hand and wrist gestures; (2) utilising non-contact depth cameras to accurately record the arm, hand and wrist position in 3D; and (3) employing an interactive touch table to present motivating virtual reality (VR) tasks. The system also uses iterative learning control (ILC), a model-based control strategy which adjusts the applied FES based on the tracking error recorded on previous task attempts. Feasibility of the system has been evaluated in experimental trials with 2 unimpaired participants and clinical trials with 4 hemiparetic, chronic stroke participants. The stroke participants attended 17, 1 hour training sessions in which they performed functional tasks, such as button pressing using the touch table and closing a drawer. Stroke participant results show that the joint angle error norm reduced by an average of 50.3% over 6 attempts at each task when assisted by FES.
In this study, we present a method for measuring functional magnetic resonance imaging (fMRI) signal complexity using fuzzy approximate entropy (fApEn) and compare it with the established sample entropy (SampEn). Here we use resting state fMRI dataset of 86 healthy adults (41 males) with age ranging from 19 to 85 years. We expect the complexity of the resting state fMRI signals measured to be consistent with the Goldberger/Lipsitz model for robustness where healthier (younger) and more robust systems exhibit more complexity in their physiological output and system complexity decrease with age. The mean whole brain fApEn demonstrated significant negative correlation (r = -0.472, p<0.001) with age. In comparison, SampEn produced a non-significant negative correlation (r = -0.099, p = 0.367). fApEn also demonstrated a significant (p < 0.05) negative correlation with age regionally (frontal, parietal, limbic, temporal and cerebellum parietal lobes). There was no significant correlation regionally between the SampEn maps and age. These results support the Goldberger/Lipsitz model for robustness and have shown that fApEn is potentially a sensitive new method for the complexity analysis of fMRI data.
Implant loosening - commonly linked with elevated initial micromotion - is the primary indication for total ankle replacement (TAR) revision. Finite element modelling has not been used to assess micromotion of TAR implants; additionally, the biomechanical consequences of TAR malpositioning - previously linked with higher failure rates - remain unexplored. The aim of this study was to estimate implant-bone micromotion and peri-implant bone strains for optimally positioned and malpositioned TAR prostheses, and thereby identify fixation features and malpositioning scenarios increasing the risk of loosening. Finite element models simulating three of the most commonly used TAR devices (BOX(®), Mobility(®) and Salto(®)) implanted into the tibia/talus and subjected to physiological loads were developed. Mobility and Salto demonstrated the largest micromotion of all tibial and talar components, respectively. Any malpositioning of the implant creating a gap between it and the bone resulted in a considerable increase in micromotion and bone strains. It was concluded that better primary stability can be achieved through fixation nearer to the joint line and/or while relying on more than a single peg. Incomplete seating on the bone may result in considerably elevated implant-bone micromotion and bone strains, thereby increasing the risk for TAR failure.
The plantar soft tissue is a highly functional viscoelastic structure involved in transferring load to the human body during walking. A Soft Tissue Response Imaging Device was developed to apply a vertical compression to the plantar soft tissue whilst measuring the mechanical response via a combined load cell and ultrasound imaging arrangement. Accuracy of motion compared to input profiles; validation of the response measured for standard materials in compression; variability of force and displacement measures for consecutive compressive cycles; and implementation in vivo with five healthy participants. Static displacement displayed average error of 0.04 mm (range of 15 mm), and static load displayed average error of 0.15 N (range of 250 N). Validation tests showed acceptable agreement compared to a Houndsfield tensometer for both displacement (CMC > 0.99 RMSE > 0.18 mm) and load (CMC > 0.95 RMSE < 4.86 N). Device motion was highly repeatable for bench-top tests (ICC = 0.99) and participant trials (CMC = 1.00). Soft tissue response was found repeatable for intra (CMC > 0.98) and inter trials (CMC > 0.70). The device has been shown to be capable of implementing complex loading patterns similar to gait, and of capturing the compressive response of the plantar soft tissue for a range of loading conditions in vivo.
Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to the laboratory until relatively recently. The application of an inexpensive body worn sensor with appropriate gait algorithms (BWM) is an attractive alternative and offers the potential to assess gait in any setting. In this study we investigated the use of a low-cost BWM, compared to laboratory reference using a robust testing protocol in both younger and older adults. We observed that the BWM is a valid tool for estimating total step count and mean spatio-temporal gait characteristics however agreement for variability and asymmetry results was poor. We conducted a detailed investigation to explain the poor agreement between systems and determined it was due to inherent differences between the systems rather than inability of the sensor to measure the gait characteristics. The results highlight caution in the choice of reference system for validation studies. The BWM used in this study has the potential to gather longitudinal (real-world) spatio-temporal gait data that could be readily used in large lifestyle-based intervention studies, but further refinement of the algorithm(s) is required.
The maximum diameter criterion is the most important factor in the clinical management of abdominal aortic aneurysms (AAA). Consequently, interventional repair is recommended when an aneurysm reaches a critical diameter, typically 5.0 cm in the United States. Nevertheless, biomechanical measures of the aneurysmal abdominal aorta have long been implicated in AAA risk of rupture. The purpose of this study is to assess whether other geometric characteristics, in addition to maximum diameter, may be highly correlated with the AAA peak wall stress (PWS). Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis using an isotropic constitutive material for the AAA wall. PWS, evaluated as the spatial maximum of the first principal stress, was calculated at a systolic pressure of 120 mmHg. The models were also used to calculate 47 geometric indices characteristic of the aneurysm geometry. Statistical analyses were conducted using a feature reduction algorithm in which the 47 indices were reduced to 11 based on their statistical significance in differentiating the models in the population (p < 0.05). A subsequent discriminant analysis was performed and 7 of these indices were identified as having no error in discriminating the AAA models with a significant nonlinear regression correlation with PWS. These indices were: Dmax (maximum diameter), T (tortuosity), DDr (maximum diameter to neck diameter ratio), S (wall surface area), Kmedian (median of the Gaussian surface curvature), Cmax (maximum lumen compactness), and Mmode (mode of the Mean surface curvature). Therefore, these characteristics of an individual AAA geometry are the highest correlated with the most clinically relevant biomechanical parameter for rupture risk assessment. We conclude that the indices can serve as surrogates of PWS in lieu of a finite element modeling approach for AAA biomechanical evaluation.
The aim of the study was to evaluate how bone porosity affects intraoperative volume measurement of the acetabulum in a plastic bone model study and to validate the measurement method on cadaveric acetabula. Point cloud collection was performed using a navigation system and compared to CT measurements as well as theoretical calculations on sawbones with different porosities and validated on cadaveric specimens. The grade of porosity had a significant influence on the volume measurement. In high porous materials volume calculation (61.5 cm²) was overestimated when using a digitalizer, while in materials with low porosity the volume was underestimated (57.0 cm²) in comparison to the known size of the defect (59.4 cm²). Digitalization time of the acetabulum was between 1 and 4 min. Validating the measurement on cadaveric bones no statistical significant difference could be found between digitalized volumes and theoretically calculated volumes. As digitalization of the acetabulum can be carried out in a reasonable time it could be used as a measurement tool to estimate the amount of allografts needed for filling bone defects.
Microsoft Kinect for Windows v2 is a motion analysis system that features a markerless human pose estimation algorithm. Given its affordability and portability, Kinect v2 has potential for use in biomechanical research and within clinical settings; however, recent studies suggest high inaccuracy of the markerless algorithm compared to marker-based motion capture systems. A novel tracking method was developed using Kinect v2, employing custom-made colored markers and computer vision techniques. The aim of this study was to test the accuracy of this approach relative to a conventional Vicon motion analysis system, performing a Bland-Altman analysis of agreement. Twenty participants were recruited, and markers placed on bony prominences near hip, knee and ankle. Three-dimensional coordinates of the markers were recorded during treadmill walking and running. The limits of agreement (LOA) of marker coordinates were narrower than - 10 and 10 mm in most conditions, however a negative relationship between accuracy and treadmill speed was observed along Kinect depth direction. LOA of the surrogate knee angles were within - 1.8°, 1.7° for flexion in all conditions and - 2.9°, 1.7° for adduction during fast walking. The proposed methodology exhibited good agreement with a marker-based system over a range of gait speeds and, for this reason, may be useful as low-cost motion analysis tool for selected biomechanical applications.
It is established that bone tissue adapts and responds to mechanical loading. Several studies have suggested an existence of positive influence of vibration on the bone mass maintenance. Thus, some bone regeneration therapies are based on vibration of bone tissue under circumstances of disease to stimulate its formation. Frequency of loading should be properly selected and therefore a correct characterization of the dynamic properties of this tissue may be critical for the success of such orthopedic techniques. On the other hand, many studies implement vibration techniques with in silico models. Numerical results are exclusively dependent on properties of bone tissue, i.e. geometry, density distribution and stiffness, as well as boundary conditions. In the present study, the influence of boundary conditions and material properties on the dynamic characteristics of bone tissue was explored in a human femur. Bone shape and density were directly reconstructed from computer tomographies, whereas natural frequencies and modes of vibration were obtained for different boundary conditions including physiological and mechanical ones. Results of this study show the moderate effect of material properties compared to the much substantial effect of boundary conditions. A factor of 2 in the natural frequency was obtained depending on imposed boundary conditions, highlighting the importance in the selection of appropriate conditions in the analysis of the bone organ.