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Abstract: Presents the front cover for this issue of the publication. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Arthur Chavignon;Baptiste Heiles;Vincent Hingot;Cyrille Orset;Denis Vivien;Olivier Couture;
Pages: 2132 - 2142 Abstract: Objective: Ultrasound Localization Microscopy (ULM) provides images of the microcirculation in-depth in living tissue. However, its implementation in two-dimension is limited by the elevation projection and tedious plane-by-plane acquisition. Volumetric ULM alleviates these issues and can map the vasculature of entire organs in one acquisition with isotropic resolution. However, its optimal implementation requires many independent acquisition channels, leading to complex custom hardware. Methods: In this article, we implemented volumetric ultrasound imaging with a multiplexed 32 × 32 probe driven by a single commercial ultrasound scanner. We propose and compare three different sub-aperture multiplexing combinations for localization microscopy in silico and in vitro with a flow of microbubbles in a canal. Finally, we evaluate the approach for micro-angiography of the rat brain. The “light” combination allows a higher maximal volume rate than the “full” combination while maintaining the field of view and resolution. Results: In the rat brain, 100,000 volumes were acquired within 7 min with a dedicated ultrasound sequence and revealed vessels down to 31 μm in diameter with flows from 4.3 mm/s to 28.4 mm/s. Conclusion: This work demonstrates the ability to perform a complete angiography with unprecedented resolution in the living rat's brain with a simple and light setup through the intact skull. Significance: We foresee that it might contribute to democratize 3D ULM for both preclinical and clinical studies. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Ying Fang;Greg Orekhov;Zachary F. Lerner;
Pages: 2143 - 2152 Abstract: Objective: Many individuals with cerebral palsy (CP) experience gait deficits resulting in metabolically-inefficient ambulation that is exacerbated by graded walking terrains. The primary goal of this study was to clinically-validate the accuracy and efficacy of adaptive ankle exoskeleton assistance during steady-state incline walking and stair ascent in individuals with CP. Exploratory goals were to assess safety and feasibility of using adaptive ankle exoskeleton assistance in real-world mixed-terrain settings. Methods: We used a novel battery-powered ankle exoskeleton to provide adaptive ankle plantar-flexor assistance during stance phase. Seven ambulatory individuals with CP completed the study. Results: Adaptive controller accuracy was 85% for incline walking and 81% for stair-stepping relative to the biological ankle moment. Assistance improved energy cost of steady-state incline walking by 14% (p = 0.004) and stair ascent by 21% (p = 0.001) compared to walking without the device. Assistance reduced the muscular demand for the soleus and vastus lateralis during both activities. All participants were able to safely complete the real-world mixed-terrain route, with adaptive ankle assistance resulting in improved outcomes compared to walking with the device providing zero-torque; no group-level differences were found compared to walking without the device, yet individuals with more impairment exhibited a marked improvement. Conclusion: Adaptive ankle exoskeleton assistance can improve the energy cost of steady-state incline walking and stair ascent in individuals with CP. Significance: As the first study to demonstrate safety and performance benefits of ankle assistance on graded terrains in CP, these findings encourage further investigation in free-livin- settings. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Masoomeh Rahimpour;Jeroen Bertels;Ahmed Radwan;Henri Vandermeulen;Stefan Sunaert;Dirk Vandermeulen;Frederik Maes;Karolien Goffin;Michel Koole;
Pages: 2153 - 2164 Abstract: Convolutional neural networks (CNNs) for brain tumor segmentation are generally developed using complete sets of magnetic resonance imaging (MRI) sequences for both training and inference. As such, these algorithms are not trained for realistic, clinical scenarios where parts of the MRI sequences which were used for training, are missing during inference. To increase clinical applicability, we proposed a cross-modal distillation approach to leverage the availability of multi-sequence MRI data for training and generate an enriched CNN model which uses only single-sequence MRI data for inference but outperforms a single-sequence CNN model. We assessed the performance of the proposed method for whole tumor and tumor core segmentation with multi-sequence MRI data available for training but only $T_{1}$-weighted ($T_{text{1}w}$) sequence data available for inference, using BraTS 2018, and in-house datasets. Results showed that cross-modal distillation significantly improved the Dice score for both whole tumor and tumor core segmentation when only $T_{text{1}w}$ sequence data were available for inference. For the evaluation using the in-house dataset, cross-modal distillation achieved an average Dice score of 79.04% and 69.39% for whole tumor and tumor core segmentation, respectively, while a single-sequence U-Net model using $T_{text{1}w}$ sequence data for both training and inference achieved an average Dice score of 73.60% and 62.62%, respectively. These findings confirmed cross-modal distillation as an effective method to increase the potential of single-sequence CNN models such that segmentation performance is-less compromised by missing MRI sequences or having only one MRI sequence available for segmentation. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Jin Chen;Xiwei Huang;Xuefeng Xu;Renjie Wang;Maoyu Wei;Wentao Han;Jiafei Cao;Weipeng Xuan;Yakun Ge;Junchao Wang;Lingling Sun;Ji-Kui Luo;
Pages: 2165 - 2175 Abstract: Objective: Separation and detection of micro-particles or cells from bio-samples by point-of-care (POC) systems are critical for biomedical and healthcare diagnostic applications. Among various microfluidic separation techniques, acoustophoresis-based technique has the advantages of label-free and good biocompatibility. However, most of the existing separation techniques are bulky and require additional equipment for analysis. Methods: We proposed a platform, which integrates an acoustophoresis-based separation device and a lensless imaging sensor into a compact standalone system to tackle this challenge. Standing Surface Acoustic Wave (SSAW) is utilized for label-free particle separation, while lensless imaging is employed for seamless particle detection and counting using self-developed dual-threshold motion detection algorithms. In particular, we specially optimized the design of microfluidic channel and interdigital transducers (IDTs) for higher performance bioparticle separation, designed a heat dissipation system for the suppression of fluid temperature, and proposed a novel frequency-temperature-curve based method to determine the appropriate signal driving frequency for IDTs. Results: At 2 μL/min flow rate, separation efficiency of 93.52% and purity of 94.29% for 15 μm microbead were achieved in mixed 5μm and 15μm microbead solution at a 25 dBm RF driving power, and similar results for mixed 10 μm and 15 μm microbead solution. Conclusions: The results showed that the integrated platform has an excellent capability to seamlessly separate, distinguish, and count microbeads of different sizes. Significance: Such a platform and the design methodologies offer a promising POC solution for label-free cell separat-on and detection in biomedical diagnostics. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Mojtaba Yavandhasani;Foad Ghaderi;
Pages: 2176 - 2183 Abstract: Responses of the human brain to different visual stimuli elicit specific patterns in electroencephalography (EEG) signals. It is confirmed that by analyzing these patterns, we can recognize the category of the visited objects. However, high levels of noise and artifacts in EEG signals and the discrepancies between the recorded data from different subjects in visual object recognition task make classification of cognitive states of subjects a serious challenge. In this research, we present a framework for evaluating machine learning and wrapper channel selection algorithms used for classifying single-trial EEG signals recorded in response to photographic stimuli. It is shown that by correctly mapping the entire EEG data space to informative feature spaces (IFS), the performance of the classification methods can improve significantly. Results outperform the state-of-the-art results and confirm efficiency of the proposed feature selection methods in capturing the most informative EEG channels. This can help to achieve high separability of object categories in single-trial visual object recognition task. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Saba Rahimi;Ryan M. Jones;Kullervo Hynynen;
Pages: 2184 - 2191 Abstract: Objective: To investigate the feasibility of developing an acoustic measurement library for non-invasive trans-rodent skull ultrasonic focusing at high frequency. Methods: A fiber-optic hydrophone (FOH) was positioned at the geometric focus of a spherically-curved phased array (64 elements, 25 mm diameter, 20 mm radius of curvature). Elements were driven sequentially (3.3 MHz driving frequency) and FOH waveforms were recorded with and without intervening ex-vivo rodent skullcaps. Measurements were carried out on 15 skullcaps (Sprague-Dawley rats, 182-209 g) across 3 fixed transmission regions per specimen. An element-wise measurement library of skull-induced phase differences was constructed using mean values across all specimens for each transmission region. Library-based transcranial phase differences were compared with direct FOH-based measurements across 5 additional skullcaps not included in the library. Results: Library-based phase corrections deviated less from FOH-based trans-skull phase difference values than those calculated for the water-path case, and restored partial transcranial focal quality relative to that recovered using invasive hydrophone-based corrections. Retrospective analysis suggests comparable performance can be obtained using smaller library sizes. Conclusion: An acoustic measurement library can facilitate non-invasive transcranial aberration correction in rodents at high frequency. Significance: Library-based focusing represents a practical approach for delivering high-frequency ultrasound brain treatments in small animals. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Yahya Z. Yahya;Ian W. Hunter;Thor F. Besier;Andrew J. Taberner;Bryan P. Ruddy;
Pages: 2192 - 2201 Abstract: Objective: In this study, we present a method to quantify the mechanics of the shoulder joint in a functional posture, in two degrees of freedom: internal/external rotation and horizontal abduction/adduction. Methods: We performed experiments on 15 healthy participants using a custom perturbation robot. Perturbations were applied in internal/external rotation and horizontal abduction/adduction, whilst participants applied varying levels of joint torque. System identification techniques were used to quantify the mechanical properties of the shoulder joint at various levels of muscle contraction, including; stiffness, viscous damping, and inertia parameters, natural frequency, and damping parameter. We compared the shoulder mechanical properties between dominant and non-dominant limbs. Results: The mean stiffness increased 4.8 times in external rotation, and 6.25 times in internal rotation as a result of contraction to 8 Nm. It increased 2.8 times in adduction and 4.6 times in abduction as a result of contraction to 16 Nm. The mean viscous damping increased 3 times in external rotation, 2.8 times in internal rotation as a result of contraction to 8 Nm. It increased 1.6 times in adduction and 2.25 times in abduction as a result of contraction to 16 Nm. Conclusion: Joint stiffness, viscous damping and natural frequency all increased with the level of shoulder contraction torque, whereas the damping parameter remained unchanged. No differences were observed between dominant and non-dominant limbs. Significance: We have presented a method to characterize the mechanical properties of the shoulder complex during various activation states, which has application as a diagnostic and assessment tool for shoulder pathology. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Hamed Hanafi Alamdari;Luke Hacquebard;Stephen Driscoll;Kamal El-Sankary;David C Roach;Robin LeBlanc;Scott Lowe;Sageev Oore;Thomas Penzel;Ingo Fietze;Michael Schmidt;Debra Morrison;
Pages: 2202 - 2211 Abstract: Oscillometry or Forced Oscillation Technique, traditionally used in intermittent clinical measurements, has recently gained substantial attention from its application as a continuous monitoring tool for large and small airways. However, low frequency ( PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Max Berniker;Kiran D. Bhattacharyya;Kristen C. Brown;Anthony Jarc;
Pages: 2212 - 2219 Abstract: Identifying and quantifying the activities that compose surgery is essential for effective interventions, computer-aided analyses and the advancement of surgical data science. For example, recent studies have shown that objective metrics (referred to as objective performance indicators, OPIs) computed during key surgical tasks correlate with surgeon skill and clinical outcomes. Unambiguous identification of these surgical tasks can be particularly challenging for both human annotators and algorithms. Each surgical procedure has multiple approaches, each surgeon has their own level of skill, and the initiation and termination of surgical tasks can be subject to interpretation. As such, human annotators and machine learning models face the same basic problem, accurately identifying the boundaries of surgical tasks despite variable and unstructured information. For use in surgeon feedback, OPIs should also be robust to the variability and diversity in this data. To mitigate this difficulty, we propose a probabilistic approach to surgical task identification and calculation of OPIs. Rather than relying on tasks that are identified by hard temporal boundaries, we demonstrate an approach that relies on distributions of start and stop times, for a probabilistic interpretation of when the task was performed. We first use hypothetical data to outline how this approach is superior to other conventional approaches. Then we present similar analyses on surgical data. We find that when surgical tasks are identified by their individual probabilities, the resulting OPIs are less sensitive to noise in the identification of the start and stop times. These results suggest that this probabilistic approach holds promise for the future of surgical data science. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Alice Maria Leondina Santilli;Kevin Ren;Richard Oleschuk;Martin Kaufmann;John Rudan;Gabor Fichtinger;Parvin Mousavi;
Pages: 2220 - 2232 Abstract: Objective: A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeon’s ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines.Methods: We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies. Results: Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively. Conclusion: Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible. Significance: By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Enze Su;Siqi Cai;Longhan Xie;Haizhou Li;Tanja Schultz;
Pages: 2233 - 2242 Abstract: Objective: Humans are able to localize the source of a sound. This enables them to direct attention to a particular speaker in a cocktail party. Psycho-acoustic studies show that the sensory cortices of the human brain respond to the location of sound sources differently, and the auditory attention itself is a dynamic and temporally based brain activity. In this work, we seek to build a computational model which uses both spatial and temporal information manifested in EEG signals for auditory spatial attention detection (ASAD). Methods: We propose an end-to-end spatiotemporal attention network, denoted as STAnet, to detect auditory spatial attention from EEG. The STAnet is designed to assign differentiated weights dynamically to EEG channels through a spatial attention mechanism, and to temporal patterns in EEG signals through a temporal attention mechanism. Results: We report the ASAD experiments on two publicly available datasets. The STAnet outperforms other competitive models by a large margin under various experimental conditions. Its attention decision for 1-second decision window outperforms that of the state-of-the-art techniques for 10-second decision window. Experimental results also demonstrate that the STAnet achieves competitive performance on EEG signals ranging from 64 to as few as 16 channels. Conclusion: This study provides evidence suggesting that efficient low-density EEG online decoding is within reach. Significance: This study also marks an important step towards the practical implementation of ASAD in real life applications. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Heather E. Williams;Ahmed W. Shehata;Michael R. Dawson;Erik Scheme;Jacqueline S. Hebert;Patrick M. Pilarski;
Pages: 2243 - 2255 Abstract: Objective: Persons with normal arm function can perform complex wrist and hand movements over a wide range of limb positions. However, for those with transradial amputation who use myoelectric prostheses, control across multiple limb positions can be challenging, frustrating, and can increase the likelihood of device abandonment. In response, the goal of this research was to investigate convolutional neural network (RCNN)-based position-aware myoelectric prosthesis control strategies. Methods: Surface electromyographic (EMG) and inertial measurement unit (IMU) signals, obtained from 16 non-disabled participants wearing two Myo armbands, served as inputs to RCNN classification and regression models. Such models predicted movements (wrist flexion/extension and forearm pronation/supination), based on a multi-limb-position training routine. RCNN classifiers and RCNN regressors were compared to linear discriminant analysis (LDA) classifiers and support vector regression (SVR) regressors, respectively. Outcomes were examined to determine whether RCNN-based control strategies could yield accurate movement predictions, while using the fewest number of available Myo armband data streams. Results: An RCNN classifier (trained with forearm EMG data, and forearm and upper arm IMU data) predicted movements with 99.00% accuracy (versus the LDA's 97.67%). An RCNN regressor (trained with forearm EMG and IMU data) predicted movements with R2 values of 84.93% for wrist flexion/extension and 84.97% for forearm pronation/supination (versus the SVR's 77.26% and 60.73%, respectively). The control strategies that employed these models required fewer than all available data streams. Conclusion: RCNN-based control strategies offer novel means of mitigating limb position challenges. Significance: This research furthers the development of improved position-aware myoelectric prosthesis control. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Bohan Shi;Arthur Tay;W. L. Au;Dawn M. L. Tan;Nicole S. Y. Chia;Shih-Cheng Yen;
Pages: 2256 - 2267 Abstract: Parkinson’s disease (PD) is a chronic, non-reversible neurodegenerative disorder, and freezing of gait (FOG) is one of the most disabling symptoms in PD as it is often the leading cause of falls and injuries that drastically reduces patients’ quality of life. In order to monitor continuously and objectively PD patients who suffer from FOG and enable the possibility of on-demand cueing assistance, a sensor-based FOG detection solution can help clinicians manage the disease and help patients overcome freezing episodes. Many recent studies have leveraged deep learning models to detect FOG using signals extracted from inertial measurement unit (IMU) devices. Usually, the latent features and patterns of FOG are discovered from either the time or frequency domain. In this study, we investigated the use of the time-frequency domain by applying the Continuous Wavelet Transform to signals from IMUs placed on the lower limbs of 63 PD patients who suffered from FOG. We built convolutional neural networks to detect the FOG occurrences, and employed the Bayesian Optimisation approach to obtain the hyper-parameters. The results showed that the proposed subject-independent model was able to achieve a geometric mean of 90.7% and a F1 score of 91.5%. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Kieran J. Bennett;Claudio Pizzolato;Saulo Martelli;Jasvir S. Bahl;Arjun Sivakumar;Gerald J. Atkins;Lucian Bogdan Solomon;Dominic Thewlis;
Pages: 2268 - 2275 Abstract: Objective: Using a musculoskeletal modelling framework, we aimed to (1) estimate knee joint loading using static optimization (SO); (2) explore different calibration functions in electromyogram (EMG)-informed models used in estimating knee load; and (3) determine, when using an EMG-informed stochastic method, if the measured joint loadings are solutions to the muscle redundancy problem when investigating only the uncertainty in muscle forces. Methods: Musculoskeletal models for three individuals with instrumented knee replacements were generated. Muscle forces were calculated using SO, EMG-informed, and EMG-informed stochastic methods. Measured knee joint loads from the prostheses were compared to the SO and EMG-informed solutions. Root mean square error (RMSE) in joint load estimation was calculated, and the muscle force ranges were compared. Results: The RMSE ranged between 192-674 N, 152-487 N, and 7-108 N for the SO, the calibrated EMG-informed solution, and the best fit stochastic result, respectively. The stochastic method produced solution spaces encompassing the measured joint loading up to 98% of stance. Conclusion: Uncertainty in muscle forces can account for total knee loading and it is recommended that, where possible, EMG measurements should be included to estimate knee joint loading. Significance: This work shows that the inclusion of EMG-informed modelling allows for better estimation of knee joint loading when compared to SO. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Julia Walther;Jonas Golde;Marius Albrecht;Bryden C. Quirk;Loretta Scolaro;Rodney W. Kirk;Yuliia Gruda;Christian Schnabel;Florian Tetschke;Korinna Joehrens;Dominik Haim;Michaela Buckova;Jiawen Li;Robert A. McLaughlin;
Pages: 2276 - 2282 Abstract: This study presents a highly miniaturized, handheld probe developed for rapid assessment of soft tissue using optical coherencetomography (OCT). OCT is a non-invasive optical technology capable of visualizing the sub-surface structural changes that occur in soft tissue disease such as oral lichen planus. However, usage of OCT in the oral cavity has been limited, as the requirements for high-quality optical scanning have often resulted in probes that are heavy, unwieldy and clinically impractical. In this paper, we present a novel probe that combines an all-fiber optical design with a light-weight magnetic scanning mechanism to provide easy access to the oral cavity. The resulting probe is approximately the size of a pen (10 mm × 140 mm) and weighs only 10 grams. To demonstrate the feasibility and high image quality achieved with the probe, imaging was performed on the buccal mucosa and alveolar mucosa during routine clinical assessment of six patients diagnosed with oral lichen planus. Results show the loss of normal tissue structure within the lesion, and contrast this with the clear delineation of tissue layers in adjacent inconspicuous regions. The results also demonstrate the ability of the probe to acquire a three-dimensional data volume by manually sweeping across the surface of the mucosa. The findings of this study show the feasibility of using a small, lightweight probe to identify pathological features in oral soft tissue. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Michael Wand;Morten B. Kristoffersen;Andreas W. Franzke;Jürgen Schmidhuber;
Pages: 2283 - 2293 Abstract: Objective: We show that state-of-the-art deep neural networks achieve superior results in regression-based multi-class proportional myoelectric hand prosthesis control than two common baseline approaches, and we analyze the neural network mapping to explain why this is the case. Methods: Feedforward neural networks and baseline systems are trained on an offline corpus of 11 able-bodied subjects and 4 prosthesis wearers, using the $R^2$ score as metric. Analysis is performed using diverse qualitative and quantitative approaches, followed by a rigorous evaluation. Results: Our best neural networks have at least three hidden layers with at least 128 neurons per layer; smaller architectures, as used by many prior studies, perform substantially worse. The key to good performance is to both optimally regress the target movement, and to suppress spurious movements. Due to the properties of the underlying data, this is impossible to achieve with linear methods, but can be attained with high exactness using sufficiently large neural networks. Conclusion: Neural networks perform significantly better than common linear approaches in the given task, in particular when sufficiently large architectures are used. This can be explained by salient properties of the underlying data, and by theoretical and experimental analysis of the neural network mapping. Significance: To the best of our knowledge, this work is the first one in the field which not only reports that large and deep neural networks are superior to existing architectures, but also explains this result. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Zhao He;Ya-Nan Zhu;Suhao Qiu;Tao Wang;Chencheng Zhang;Bomin Sun;Xiaoqun Zhang;Yuan Feng;
Pages: 2294 - 2304 Abstract: Objective: Interventional MRI (i-MRI) is crucial for MR image-guided therapy. Current image reconstruction methods for dynamic MR imaging are mostly retrospective that may not be suitable for real-time i-MRI. Therefore, an algorithm to reconstruct images without a temporal pattern as in dynamic imaging is needed for i-MRI. Methods: We proposed a low-rank and sparsity (LS) decomposition algorithm with framelet transform to reconstruct the interventional feature with a high temporal resolution. Different from the existing LS-based algorithms, the spatial sparsity of both the low-rank and sparsity components was used. We also used a primal dual fixed point (PDFP) method for optimization of the objective function to avoid solving sub-problems. Intervention experiments with gelatin and brain phantoms were carried out for validation. Results: The LS decomposition with framelet transform and PDFP could provide the best reconstruction performance compared with those without. Satisfying reconstruction results were obtained with only 10 radial spokes for a temporal resolution of 60 ms. Conclusion and Significance: The proposed method has the potential for i-MRI in many different application scenarios. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Paul Ritter;Aijia Cai;Barbara Reischl;Maren Fiedler;Gerhard Prölß;Benjamin Frieß;Elke Kretzschmar;Mena Michael;Kristin Hartmann;Christian Lesko;Haitham Salti;Andreas Arkudas;Raymund Horch;Friedrich Paulsen;Oliver Friedrich;Michael Haug;
Pages: 2305 - 2313 Abstract: Objective: Decellularizing solid organs is a promising top-down process to produce acellular bio-scaffolds for ‘de novo’ regrowth or application as tissue ‘patches’ that compensate, e.g., large volumetric muscle loss in reconstructive surgery. Therefore, generating standardized acellular muscle scaffolds marks a pressing area of need. Although animal muscle decellularization protocols were established, those are mostly manually performed and lack defined bioreactor environments and metrologies to assess decellularization quality in real-time. To close this gap, we engineered an automated bioreactor system to provide chemical decellularization solutions to immersed whole rat gastrocnemius medialis muscle through perfusion of the main feeding arteries. Results: Perfusion control is adjustable according to decellularization quality feedback. This was assessed both from (i) ex situ assessment of sarcomeres/nuclei through multiphoton fluorescence and label-free Second Harmonic Generation microscopy and DNA quantification, along with (ii) in situ within the bioreactor environment assessment of the sample’s passive mechanical elasticity. Conclusion: We find DNA and sarcomere-free constructs after 72 h of 0.1% SDS perfusion-decellularization. Furthermore, passive elasticity can be implemented as additional online decellularization quality measure, noting a threefold elasticity decrease in acellular constructs. Significance: Our MyoBio represents a novel and useful automated bioreactor environment for standardized and controlled generation of acellular whole muscle scaffolds as a valuable source for regenerative medicine. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Juhyeon Lee;Brandon Oubre;Jean-Francois Daneault;Christopher D. Stephen;Jeremy D. Schmahmann;Anoopum S. Gupta;Sunghoon Ivan Lee;
Pages: 2314 - 2323 Abstract: Objective: Assessment of motor severity in cerebellar ataxia is critical for monitoring disease progression and evaluating the effectiveness of therapeutic interventions. Though wearable sensors have been used to monitor gait tasks in order to enable frequent assessment, existing solutions only estimate gait performance severity rather than comprehensive motor severity. In this study, we propose a new approach that analyzes sub-second movement profiles of the lower-limbs during gait to estimate overall motor severity in cerebellar ataxia. Methods: A total of 37 ataxia subjects and 12 healthy subjects performed a 5 m walk-and-turn task with two ankle-worn inertial sensors. Lower-limb movements were decomposed into one-dimensional sub-movements, namely movement elements. Supervised regression models trained on data features of movement elements estimated the Brief Ataxia Rating Scale (BARS) and its sub-scores evaluated by clinicians. The proposed models were also compared to models trained on widely-accepted spatiotemporal gait features. Results: Estimated total BARS showed strong agreement with clinician-evaluated scores with $r^2$ = 0.72 and a root mean square error of 2.6 BARS points. Movement element-based models significantly outperformed conventional, spatiotemporal gait feature-based models. Conclusion: The proposed algorithm accurately assessed overall motor severity in cerebellar ataxia using inertial data collected from bilaterally-placed ankle sensors during a simple walk-and-turn task. Significance: Our work could support fine-grained monitoring of disease progression and patients’ responses to medical/clinical interventions. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Killian McManus;Barry R. Greene;Lilian Genaro Motti Ader;Brian Caulfield;
Pages: 2324 - 2332 Abstract: Ageing incurs a natural decline of postural control which has been linked to an increased risk of falling. Accurate balance assessment is important in identifying postural instability and informing targeted interventions to prevent falls in older adults. Inertial sensor (IMU) technology offers a low-cost means for objective quantification of human movement. This paper describes two studies carried out to advance the use of IMU-based balance assessments in older adults. Study 1 (N = 39) presents the development of two new IMU-derived balance measures. Study 2 (N = 248) reports a reliability analysis of IMU postural stability measures and validates the novel balance measures through comparison with clinical scales. We also report a statistical fall risk estimation algorithm based on IMU data captured during static balance assessments alongside a method of improving this fall risk estimate by incorporating standard clinical fall risk factor data. Results suggest that both new balance measures are sensitive to balance deficits captured by the Berg Balance Scale (BBS) and Timed Up and Go test. Results obtained from the fall risk classifier models suggest they are more accurate (67.9%) at estimating fall risk status than a model based on BBS (59.2%). While the accuracies of the reported models are lower than others reported in the literature, the simplicity of the assessment makes it a potentially useful screening tool for balance impairments and falls risk. The algorithms presented in this paper may be suitable for implementation on a smartphone and could facilitate unsupervised assessment in the home. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Stephanie Velasco;Luciano Branco;Aviva Abosch;Nuri F. Ince;
Pages: 2333 - 2341 Abstract: Objective: Beta bursts of local fields potentials (LFPs) recorded from subthalamic nucleus (STN) have been recently proposed as a new temporal feature for patients with Parkinson's disease (PD). We introduce a new technique for the adaptive time-domain segmentation of STN-LFP recordings such that the constructed time segments are proportional to the duration of stationary beta activity. We investigated whether the spectral entropy of the adaptively captured beta oscillations can describe the improvement in motor signs following dopaminergic medication. Methods: STN-LFP recordings from externalized chronic deep brain stimulation (DBS) leads were obtained in 9 PD patients. During this monitoring, each patient underwent 3 medication intake cycles where short acting agents (L-DOPA equivalent dose) were administered. We analyzed 2-minute resting state LFP data in each OFF and L-DOPA-induced ON medication states and constructed time domain segmentation of LFP signal in which the length segmentations are adapted to time-varying nature of the oscillatory activity. Results: Adaptively constructed segments were noted to be significantly longer in OFF- and shorter in ON-state (p375 ms) was in the low range (12-23 Hz) of the beta spectrum, whereas shorter beta bursts ( PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Ye Yang;Teng Ma;Qi Zhang;Jiqing Huang;Qi Hu;Yongchuan Li;Congzhi Wang;Hairong Zheng;
Pages: 2342 - 2352 Abstract: Flexible manipulation techniques for living cells and organisms are extremely useful tools for fundamental biomedical and life science research. Acoustic tweezers, which permit non-contact, label-free manipulation, are particularly suited to micromanipulation tasks as they provide a large acoustic radiation force and can be applied in various media. Here, we describe the design and fabrication of a 3 MHz, 64-element (8 × 8), 2D planar ultrasound array that realizes the multidimensional translation, rotation, orientation, and levitation of living cells and organisms. The focusing vortex and twin fields are generated using the holographic acoustic elements framework method. We demonstrate that the eggs and larvae of brine shrimp can be translated along a preset trajectory by controlling the central position of the vortex. By multiplexing counterclockwise vortices, clockwise vortices, and twin trap fields in a time sequence, the rotation direction of the shrimp eggs can be switched in real time, while non-spherical larvae can be reoriented. Moreover, the reflection of the acoustic beam can lift eggs and larvae from the bottom of the culture dish and further manipulate them in the vertical and horizontal directions. Additionally, we present quantitative analyses of the shrimp-egg rotation frequency with respect to the focal depths, topological charges of the vortex, and excitation voltages. These results indicate that acoustic tweezers based on 2D matrix arrays can realize complex and selective manipulation of living cells and organisms, thereby demonstrating their value for advancing research in the fields of cell assembly, tissue engineering, and micro-robot driving. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Ross A. Petrella;Shani L. Levit;Christopher C. Fesmire;Christina Tang;Michael B. Sano;
Pages: 2353 - 2362 Abstract: Expanding the volume of an irreversible electroporation treatment typically necessitates an increase in pulse voltage, number, duration, or repetition. This study investigates the addition of polyethylenimine nanoparticles (PEI-NP) to pulsed electric field treatments, determining their combined effect on ablation size and voltages. U118 cells in an in vitro 3D cell culture model were treated with one of three pulse parameters (with and without PEI-NPs) which are representative of irreversible electroporation (IRE), high frequency irreversible electroporation (H-FIRE), or nanosecond pulsed electric fields (nsPEF). The size of the ablations were compared and mapped onto an electric field model to describe the electric field required to induce cell death. Analysis was conducted to determine the role of PEI-NPs in altering media conductivity, the potential for PEI-NP degradation following pulsed electric field treatment, and PEI-NP uptake. Results show there was a statistically significant increase in ablation diameter for IRE and H-FIRE pulses with PEI-NPs. There was no increase in ablation size for nsPEF with PEI-NPs. This all occurs with no change in cell media conductivity, no observable degradation of PEI-NPs, and moderate particle uptake. These results demonstrate the synergy of a combined cationic polymer nanoparticle and pulsed electric field treatment for the ablation of cancer cells. These results set the foundation for polymer nanoparticles engineered specifically for irreversible electroporation. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Gerwin Dijk;Romanos Poulkouras;Rodney P. O'Connor;
Pages: 2363 - 2369 Abstract: Objective: Monitoring of impedance changes during electroporation-based treatments can be used to study the biological response and provide feedback regarding treatment progression. However, seamless integration of the sensing electrodes with the setup can be challenging and high impedance sensing electrodes limit the recording sensitivity as well as the spatial resolution. Here, we present an all-in-one microchip containing stimulation electrodes, as well as an array of low impedance, micro-scale sensing electrodes for highly sensitive impedance monitoring. Methods: An in vitro platform is fabricated with integrated stimulation and sensing electrodes. To reduce the impedance, the sensing electrodes are coated with the conducting polymer PEDOT:PSS. The performance is studied during the growth of a confluent cell layer and treatment with electrical pulses. Results: Coated electrodes, compared to uncoated electrodes, show more pronounced impedance changes in a broader frequency range throughout the formation of a confluent cell layer and after electrical treatment. Conclusion: PEDOT:PSS coatings enhance the monitoring of impedance changes with micro-scale electrodes, enabling high spatial resolution and increased sensitivity. Significance: Such monitoring systems can be used to study electroporation dynamics and monitor treatment progression for better understanding of underlying mechanisms and improved outcomes. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Saeed Akbarzadeh;Tianchan Lyu;Roozbeh Farhoodi;Muhammad Awais;Saadullah Farooq Abbasi;Xian Zhao;Chen Chen;Amara Amara;Yasemin Akay;Metin Akay;Wei Chen;
Pages: 2370 - 2378 Abstract: Due to the lack of enough physical or suck central pattern generator (SCPG) development, premature infants require assistance in improving their sucking skills as one of the first coordinated muscular activities in infants. Hence, we need to quantitatively measure their sucking abilities for future studies on their sucking interventions. Here, we present a new device that can measure both intraoral pressure (IP) and expression pressure (EP) as ororhithmic behavior parameters of non-nutritive sucking skills in infants. Our device is low-cost, easy-to-use, and accurate, which makes it appropriate for extensive studies. To showcase one of the applications of our device, we collected weekly data from 137 premature infants from 29 week-old to 36 week-old. Around half of the infants in our study needed intensive care even after they were 36 week-old. We call them full attainment of oral feeding (FAOF) infants. We then used the Non-nutritive sucking (NNS) features of EP and IP signals of infants recorded by our device to predict FAOF infants’ sucking conditions. We found that our pipeline can predict FAOF infants several weeks before discharge from the hospital. Thus, this application of our device presents a robust and inexpensive alternative to monitor oral feeding ability in premature infants. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Xuen Hoong Kok;Syed Anas Imtiaz;Esther Rodriguez-Villegas;
Pages: 2379 - 2389 Abstract: Objective: Long-term monitoring of epilepsy patients outside of hospital settings is impractical due to the complexity and costs associated with electroencephalogram (EEG) systems. Alternative sensing modalities that can acquire, and automatically interpret signals through easy-to-use wearable devices, are needed to help with at-home management of the disease. In this paper, a novel machine learning algorithm is presented for detecting epileptic seizures using acoustic physiological signals acquired from the neck using a wearable device. Methods: Acoustic signals from an existing database, were processed, to extract their Mel-frequency Cepstral Coefficients (MFCCs) which were used to train RUSBoost classifiers to identify ictal and non-ictal acoustic segments. A postprocessing stage was then applied to the segment classification results to identify seizures episodes. Results: Tested on 667 hours of acoustic data acquired from 15 patients with at least one seizure, the algorithm achieved a detection sensitivity of 88.1% (95% CI: 79%–97%) from a total of 36 seizures, out of which 24 had no motor manifestations, with a FPR of 0.83/h, and a median detection latency of −42 s. Conclusion: The results demonstrated for the first time the ability to identify seizures using acoustic internal body signals acquired on the neck. Significance: The results of this paper validate the feasibility of using internal physiological sounds for seizure detection, which could potentially be of use for the development of novel, wearable, very simple to use, long term monitoring, or seizure detection systems; circumventing the practical limitations of EEG monitoring outside hospital settings, or systems based on sensing modalities that work on convulsive seizures only. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Harry J. Davies;Patrik Bachtiger;Ian Williams;Philip L. Molyneaux;Nicholas S. Peters;Danilo P. Mandic;
Pages: 2390 - 2400 Abstract: An ability to extract detailed spirometry-like breathing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
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Authors:
Antti Paldanius;Bachir Dekdouk;Jussi Toivanen;Ville Kolehmainen;Jari Hyttinen;
Pages: 2401 - 2401 Abstract: In the above paper [1] there are two errors which we correct here. An important reference was omitted. PubDate:
July 2022
Issue No:Vol. 69, No. 7 (2022)
Hybrid journal (It can contain Open Access articles)
