 Subjects -> ELECTRONICS (Total: 207 journals)
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- IEEE Engineering in Medicine and Biology Society Information
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Pages: C2 - C2
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- IEEE Transactions on Biomedical Engineering Information for Authors
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Pages: C3 - C3
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- IEEE Transactions on Biomedical Engineering Handling Editors Information
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Pages: C4 - C4
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Performance of a Convolutional Neural Network Derived From PPG Signal in
Classifying Sleep Stages-
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Authors:
Ahsan Habib;Mohammod Abdul Motin;Thomas Penzel;Marimuthu Palaniswami;John Yearwood;Chandan Karmakar;
Pages: 1717 - 1728
Abstract: Automatic sleep stage classification is vital for evaluating the quality of sleep. Conventionally, sleep is monitored using multiple physiological sensors that are uncomfortable for long-term monitoring and require expert intervention. In this study, we propose an automatic technique for multi-stage sleep classification using photoplethysmographic (PPG) signal. We have proposed a convolutional neural network (CNN) that learns directly from the PPG signal and classifies multiple sleep stages. We developed models for two- (Wake-Sleep), three- (Wake-NREM-REM) and four- (Wake-Light sleep-Deep sleep-REM) stages of sleep classification. Our proposed approach shows an average classification accuracy of 94.4%, 94.2%, and 92.9% for two, three, and four stages, respectively. Experimental results show that the proposed CNN model outperforms existing state-of-the-art models (classical and deep learning) in the literature.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Output Power Computation and Adaptation Strategy of an Electrosurgery
Inverter for Reduced Collateral Tissue Damage-
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Authors:
Congbo Bao;Sudip K. Mazumder;
Pages: 1729 - 1740
Abstract: Objective: This paper investigates two ways of output-power computation, namely, sparse- and multi-sampling-based methods, to overcome sampling speed limitation and arcing nonlinearity for electrosurgery. Moreover, an impedance-based power adaptation strategy is explored for reduced collateral tissue damage. Methods: The efficacy of the proposed power computation and adaptation strategy are experimentally investigated on a gallium-nitride (GaN)-based high-frequency inverter prototype that allows electrosurgery with a 390 kHz output frequency. Results: The sparse-sampling-based method samples output voltage once and current twice per cycle. The achieved power computing errors over 1000 cycles are 1.43 W, 2.54 W, 4.53 W, and 4.89 W when output power varies between 15 W and 45 W. The multi-sampling-based method requires 28 samples of both outputs, and the corresponding errors are 0.02 W, 0.86 W, 1.86 W, and 3.09 W. The collateral tissue damage gauged by average thermal spread is 0.86 mm, 0.43 mm, 1.11 mm, and 0.36 mm for the impedance-based power adaptation against 1.49 mm for conventional electrosurgery. Conclusion: Both power-computation approaches break sampling speed limitations and calculate output power with small errors. However, with arcing nonlinearity presence, the multi-sampling-based method yields better accuracy. The impedance-based power adaptation reduces thermal spreads and diminishes sensor count and cost. Significance: This paper exemplifies two novel power-computation ways using low-end industrial-scale processors for biomedical research involving high-frequency and nonlinearly distorted outputs. Additionally, this work is the first to present the original impedance-based power adaptation strategy for reduced collateral damage and it may motivate further interdisciplinary research towards collateral-damage-less electrosurgery.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- System Matrix Reconstruction Algorithm for Thermoacoustic Imaging With
Magnetic Nanoparticles Based on Acoustic Reciprocity Theorem-
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Authors:
Hongjia Liu;Yanhong Li;Guoqiang Liu;
Pages: 1741 - 1749
Abstract: Objective: According to the acoustic reciprocity theorem (ART), we propose a system matrix reconstruction algorithm of thermoacoustic imaging for magnetic nanoparticles (MNPs) by a single-pulse magnetic field. Methods: In both cases of inhomogeneous and homogeneous acoustic velocity, we respectively derive the linear equation between the sound pressure detection value and the distribution of MNPs. The image reconstruction problem is converted to an inverse matrix solution by using the truncated singular value decomposition (TSVD) method. Results: In forward problem, the calculated forward results are consistent with the simulated thermoacoustic signal signals. In inverse problem, we build the two-dimensional breast cancer model. The TSVD method based on the ART faithfully reflects the distribution of abnormal tissue labeled by the MNPs. In the experiment, the biological sample injected with the MNPs is used as the imaging target. The reconstructed image well reflects the cross-sectional images of the MNPs area. Conclusion: The TSVD method based on the ART takes into account energy attenuation and inhomogeneous acoustic velocity, and use a non-focused broadband ultrasonic transducer as the receiver to obtain a larger imaging field-of-view (FOV). By comparing the image metrics, we prove that the algorithm is superior to the traditional time reversal method. Significance: The TSVD method based on the ART can better suppress noise, which is expected to reduce the cost by reducing the number of detectors. It is of great significance for future clinical applications.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Approach to Quantify Eye Movements to Augment Stroke Diagnosis With a
Non-Calibrated Eye-Tracker-
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Authors:
Mohamed Abul Hassan;Chad M. Aldridge;Yan Zhuang;Xuwang Yin;Timothy McMurry;Gustavo K. Rohde;Andrew M. Southerland;
Pages: 1750 - 1757
Abstract: Automated eye-tracking technology could enhance diagnosis for many neurological diseases, including stroke. Current literature focuses on gaze estimation through a form of calibration. However, patients with neuro-ocular abnormalities may have difficulty completing a calibration procedure due to inattention or other neurological deficits. Objective: We investigated 1) the need for calibration to measure eye movement symmetry in healthy controls and 2) the potential of eye movement symmetry to distinguish between healthy controls and patients. Methods: We analyzed fixations, smooth pursuits, saccades, and conjugacy measured by a Spearman correlation coefficient and utilized a linear mixed-effects model to estimate the effect of calibration. Results: Healthy participants (n = 18) did not differ in correlations between calibrated and non-calibrated conditions for all tests. The calibration condition did not improve the linear mixed effects model (log-likelihood ratio test p = 0.426) in predicting correlation coefficients. Interestingly, the patient group (n = 17) differed in correlations for the DOT (0.844 [95% CI 0.602, 0.920] vs. 0.98 [95% CI 0.976, 0.985]), H (0.903 [95% CI 0.746, 0.958] vs. 0.979 [95% CI 0.971, 0.986]), and OKN (0.898 [95% CI 0.785, 0.958] vs. 0.993 [95% CI 0.987, 0.996]) tests compared to healthy controls along the x-axis. These differences were not observed along the y-axis. Significance: This study suggests that automated eye tracking can be deployed without calibration to measure eye movement symmetry. It may be a good discriminator between normal and abnormal eye movement symmetry. Validation of these findings in larger populations is required.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Magnetomotive Ultrasound Shear Wave Elastography (MMUS-SWE): A Validation
Study From Simulations to Experiments-
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Authors:
Haoming Lin;Tiemei Chen;Dingqian Deng;Zhaoke Pi;Siyuan Xie;Ge Ding;Tingting Qi;Kaixin Shu;Xiangwei Lin;Zhourui Xu;Zhiyong Wang;Siping Chen;Mian Chen;Xin Chen;
Pages: 1758 - 1767
Abstract: Ultrasound elastography is a functional imaging method that enables the measurement of soft tissue elasticity, which is associated with the pathological process of many diseases. However, the measurement area of the conventional elastography method is subjectively selected. Inspired by the targeted imaging technology, we propose a method of magnetomotive ultrasound shear wave elastography (MMUS-SWE). This method utilizes the magnetic force between the magnetic nanoparticles (MNPs) and the external magnetic field to generate shear waves. Then, it can detect the distribution of MNPs and the elasticity of the tissue around the MNPs. As MNPs have been widely used for targeted labeling, the strategy to induce local vibration by MNPs will be more specific than that of the conventional SWE. In this study, the theoretical feasibility was verified by the finite element simulation model. Then, an experimental system was built, and the experimental feasibility of the method was demonstrated through phantom experiments, in vitro tissue experiments, and in vivo experiments. The results show that the distribution of the MNPs and the elastic information of tissues surrounding the MNPs can be detected simultaneously. This technology is expected to realize targeted elasticity measurement based on the MNPs and has potential applications for disease diagnosis.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- A Novel Dual-Element Catheter for Improving Non-Uniform Rotational
Distortion in Intravascular Ultrasound-
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Authors:
Baoqiang Liu;Min Su;Zhiqiang Zhang;Rong Liu;Lei Sun;Hairong Zheng;Weibao Qiu;
Pages: 1768 - 1774
Abstract: For the early diagnosis of atherosclerosis and interventions, intravascular ultrasound (IVUS) is a valuable tool for intravascular luminal imaging. Compared with the array-based method, mechanically rotating IVUS catheters dominate the clinical applications because of their less complexity and better suitability for high-frequency ultrasound imaging. However, mechanically rotating catheters are suffering from non-uniform rotational distortion (NURD) which hinders accurate image acquisition. In this study, a dual-element imaging catheter is proposed, in which two elements with the same frequency and similar performance are assembled in a back-to-back arrangement. When the catheter encounters a NURD due to acute bending, the abnormal image of one element can be replaced by the normal image of the opposite element, thus eliminating the NURD in the reconstructed image. Moreover, two images can be obtained in one rotation and the imaging frame rate is doubled in the absence of NURD. The performance of the two elements was quantitatively assessed by a wire phantom. And the complementary imaging protocols were evaluated by a tissue phantom and ex vivo porcine vessel. The results show that the proposed strategy can be promising in clinical studies.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Data Augmentation of SSVEPs Using Source Aliasing Matrix Estimation for
Brain–Computer Interfaces-
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Authors:
Ruixin Luo;Minpeng Xu;Xiaoyu Zhou;Xiaolin Xiao;Tzyy-Ping Jung;Dong Ming;
Pages: 1775 - 1785
Abstract: Objective: Currently, ensemble task-related component analysis (eTRCA) and task discriminative component analysis (TDCA) are the state-of-the-art algorithms for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, training the BCIs requires multiple calibration trials. With insufficient calibration data, the accuracy of the BCI will degrade, or even become invalid with only one calibration trial. However, collecting a large amount of electroencephalography (EEG) data for calibration is a time-consuming and laborious process, which hinders the practical use of eTRCA and TDCA. Methods: This study proposed a novel method, namely Source Aliasing Matrix Estimation (SAME), to augment the calibration data for SSVEP-BCIs. SAME could generate artificial EEG trials with the featured SSVEPs. Its effectiveness was evaluated using two public datasets (i.e., Benchmark, BETA). Results: When combined with SAME, both eTRCA and TDCA had significantly improved performance with a limited number of calibration data. Specifically, SAME increased the average accuracy of eTRCA and TDCA by about 12% and 3%, respectively, with as few as two calibration trials. Notably, SAME enabled eTRCA and TDCA to work well with a single calibration trial, achieving an average accuracy>90% for the Benchmark dataset and>70% for the BETA dataset with 1-second EEG. Conclusion: SAME is an effective method for SSVEP-BCIs to augment the calibration data, thereby significantly enhancing the performance of eTRCA and TDCA. Significance: We propose a new data-augmentation method that is compatible with the state-of-the-art algorithms of SSVEP-based BCIs. It can significantly reduce the efforts required to calibrate SSVEP-BCIs, which is promising for the development of practical BCIs.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- In-Vivo Validation of a Novel Robotic Platform for Endovascular
Intervention-
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Authors:
Giulio Dagnino;Dennis Kundrat;Trevor M. Y. Kwok;Mohamed E. M. K. Abdelaziz;Wenqiang Chi;Anh Nguyen;Celia Riga;Guang-Zhong Yang;
Pages: 1786 - 1794
Abstract: Objective: In-vivo validation on animal setting of a pneumatically propelled robot for endovascular intervention, to determine safety and clinical advantage of robotic cannulations compared to manual operation. Methods: Robotic assistance and image-guided intervention are increasingly used for improving endovascular procedures with enhanced navigation dexterity and accuracy. However, most platforms developed in the past decade still present inherent limitations in terms of altered clinical workflow, counterintuitive human-robot interaction, and a lack of versatility. We have created a versatile, highly integrated platform for robot-assisted endovascular intervention aimed at addressing such limitations, and here we demonstrate its clinical usability through in-vivo animal trials. A detailed in-vivo study on four porcine models conducted with our robotic platform is reported, involving cannulation and balloon angioplasty of five target arteries. Results: The trials showed a 100% success rate, and post-mortem histopathological assessment demonstrated a reduction in the incidence and severity of vessel trauma with robotic navigation versus manual manipulation. Conclusion: In-vivo experiments demonstrated that the applicability of our robotic system within the context of this study was well tolerated, with good feasibility, and low risk profile. Comparable results were observed with robotics and manual cannulation, with clinical outcome potentially in favor of robotics. Significance: This study showed that the proposed robotic platform can potentially improve the execution of endovascular procedures, paving the way for clinical translation.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Voluntary Assist-as-Needed Controller for an Ankle Power-Assist
Rehabilitation Robot-
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Authors:
Renyu Yang;Zhihang Shen;Yueling Lyu;Yu Zhuang;Le Li;Rong Song;
Pages: 1795 - 1803
Abstract: Objective: Although existing assist-as-needed (AAN) controllers have been designed to adapt the robotic assistance to patients' movement performance, they ignore patient's active participation. This study proposed a voluntary AAN (VAAN) controller considering both movement performance and active participation for an ankle rehabilitation robot. Methods: According to the trajectory tracking error of the human-robot cooperation movement, the controller can switch among four working modes, including robot-resist, free, robot-assist, and robot-dominant mode. In order to reflect patients’ active participation, the voluntary torque of the ankle joint was estimated by an EMG-driven musculoskeletal model. The control torque in robot-resist, free, and robot-assist mode was determined by the voluntary torque of ankle joint multiplied by an assistance ratio to encourage subjects’ active participation, and a stiff torque was provided in robot-dominant mode. The controller was evaluated with 2 healthy subjects and 5 stroke patients on an ankle rehabilitation robot to investigate the clinical impact on the stroke patients. Results: The experiment results showed that as patients’ disability level increased, the trajectory tracking error increased and the proportion of human-dominant time and the voluntary torque of ankle joint decreased. Moreover, the results showed that the proposed VAAN controller achieved higher human contribution ratio than that of previous studies. Conclusion: The proposed VAAN controller can adapt the working mode to the movement performance and promote the subjects to participate actively. Significance: Based on its performance, the proposed VAAN controller has potential for use in robot-assisted rehabilitation.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Convex Combination of Images From Dual-Layer Detectors for High Detective
Quantum Efficiencies-
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Authors:
Dong Sik Kim;
Pages: 1804 - 1814
Abstract: Objective: In this paper, a novel dual-layer detector is considered to acquire X-ray images of high signal-to-noise ratio (SNR) as well as detective quantum efficiency (DQE) especially for high voltages of X-ray tubes. Methods: To achieve a uniform alignment property, the upper and lower detector layers are stacked with an aligning procedure while minimizing the layer distance. A convex combination of the images acquired from the layers is optimized with respect to the combination coefficient. For the optimization and an alignment analysis based on Monte Carlo simulations, parametric modeling for the detector is also conducted. Results: It is shown that for a given spatial frequency, the optimized DQE of the convex combination image (CCI) is the summation of the DQE values of the upper and lower layers. For extensive experiments, several types of the aligned dual-layer detector (ADD) are practically constructed to acquire CCI. The experimental results under a beam quality of RQA 9 showed that ADD could efficiently increase the DQE value from 50% to more than 75% at zero frequency. Conclusion: ADD can be used for increasing DQE as well as conventional spectral detector applications. Significance: CCI acquired from ADD can have significantly higher DQE values compared to the single-layer cases.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Design and Testing of a Dynamic Orthosis to Reduce Glenohumeral
Subluxation With Omnidirectional Shoulder Motion-
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Authors:
Shuangyuan Huang;Li Huang;Fawen Xie;Shiman He;Juncheng Li;Yan Chen;Haiqing Zheng;Longhan Xie;
Pages: 1815 - 1825
Abstract: Objective: This paper aimed to develop an orthosis to apply a compensating force to improve the stability of the glenohumeral joint without resisting arm movement. Methods: The proposed orthosis was based on a parallelogram structure to provide a pair of compensating forces to the glenohumeral joint center. Theoretical analysis was used to evaluate the additional moments caused by glenohumeral joint center shifting. Then, an experimental evaluation platform, composed of a torque sensor, a force sensor, and a 3D printed arm, was set up to assess the additional moments and compensating force. Finally, the proposed orthosis was compared with the traditional orthosis to compare the subluxation reduction and the movement restriction when worn by stroke patients. Results: There was only a maximum additional moment of 0.87 Nm for the glenohumeral center shifting. During 3D printed arm movement, the moment correlation coefficient between with and without the proposed orthosis was greater than 0.98, and the compensating force was larger than 90% of the arm weight. The proposed orthosis reduced subluxation by $12.5pm 3.5$ mm, and the traditional orthosis reduced subluxation by $7.7pm 2.2$ mm, indicating that the subluxation reduction of the proposed orthosis was more effective ($p< 0.001$). Meanwhile, the proposed orthosis's motion restriction joint was significantly smaller than traditional orthosis ($p< 0.001$). Conclusion: The proposed orthosis provided sufficient gravity compensation without resisting arm movement. Significance: The propose- orthosis can improve the shoulder's stability during shoulder movement, potentially improving the rehabilitation effect of patients with shoulder subluxation.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- A Multiscale Computational Model of Skeletal Muscle Electroporation
Validated Using In Situ Porcine Experiments-
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Authors:
Rok Šmerc;David A. Ramirez;Samo Mahnič-Kalamiza;Janja Dermol-Černe;Daniel C. Sigg;Lars M. Mattison;Paul A. Iaizzo;Damijan Miklavčič;
Pages: 1826 - 1837
Abstract: Objective: The goal of our study was to determine the importance of electric field orientation in an anisotropic muscle tissue for the extent of irreversible electroporation damage by means of an experimentally validated mathematical model. Methods: Electrical pulses were delivered to porcine skeletal muscle in vivo by inserting needle electrodes so that the electric field was applied in direction either parallel or perpendicular to the direction of the muscle fibres. Triphenyl tetrazolium chloride staining was used to determine the shape of the lesions. Next, we used a single cell model to determine the cell-level conductivity during electroporation, and then generalised the calculated conductivity changes to the bulk tissue. Finally, we compared the experimental lesions with the calculated field strength distributions using the Sørensen-Dice similarity coefficient to find the contours of the electric field strength threshold beyond which irreversible damage is thought to occur. Results: Lesions in the parallel group were consistently smaller and narrower than lesions in the perpendicular group. The determined irreversible threshold of electroporation for the selected pulse protocol was 193.4 V/cm with a standard deviation of 42.1 V/cm, and was not dependent on field orientation. Conclusion: Muscle anisotropy is of significant importance when considering electric field distribution in electroporation applications. Significance: The paper presents an important advancement in building up from the current understanding of single cell electroporation to an in silico multiscale model of bulk muscle tissue. The model accounts for anisotropic electrical conductivity and has been validated through experiments in vivo.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- A Wearable Fiber-Free Optical Sensor for Continuous Monitoring of Cerebral
Blood Flow in Freely Behaving Mice-
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Authors:
Xuhui Liu;Daniel A. Irwin;Chong Huang;Yutong Gu;Li Chen;Kevin D. Donohue;Lei Chen;Guoqiang Yu;
Pages: 1838 - 1848
Abstract: Objective: Wearable technologies for functional brain monitoring in freely behaving subjects can advance our understanding of cognitive processing and adaptive behavior. Existing technologies are lacking in this capability or need procedures that are invasive and/or otherwise impede brain assessments during social behavioral conditions, exercise, and sleep. Methods: In response a complete system was developed to combine relative cerebral blood flow (rCBF) measurement, O2 and CO2 supplies, and behavior recording for use on conscious, freely behaving mice. An innovative diffuse speckle contrast flowmetry (DSCF) device and associated hardware were miniaturized and optimized for rCBF measurements in small subject applications. The use of this wearable, fiber-free, near-infrared DSCF head-stage/probe allowed no craniotomy, minimally invasive probe implantation, and minimal restraint of the awake animal. Results and Conclusions: Significant correlations were found between measurements with the new DSCF design and an optical standard. The system successfully detected rCBF responses to CO2-induced hypercapnia in both anesthetized and freely behaving mice. Significance: Collecting rCBF and activity information together during natural behaviors provides realistic physiological results and opens the path to exploring their correlations with pathophysiological conditions.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Minimally Invasive Live Tissue High-Fidelity Thermophysical Modeling Using
Real-Time Thermography-
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Authors:
Hamza El-Kebir;Junren Ran;Yongseok Lee;Leonardo P. Chamorro;Martin Ostoja-Starzewski;Richard Berlin;Gabriela M. Aguiluz Cornejo;Enrico Benedetti;Pier C. Giulianotti;Joseph Bentsman;
Pages: 1849 - 1857
Abstract: We present a novel thermodynamic parameter estimation framework for energy-based surgery on live tissue, with direct applications to tissue characterization during electrosurgery. This framework addresses the problem of estimating tissue-specific thermodynamics in real-time, which would enable accurate prediction of thermal damage impact to the tissue and damage-conscious planning of electrosurgical procedures. Our approach provides basic thermodynamic information such as thermal diffusivity, and also allows for obtaining the thermal relaxation time and a model of the heat source, yielding in real-time a controlled hyperbolic thermodynamics model. The latter accounts for the finite thermal propagation time necessary for modeling of the electrosurgical action, in which the probe motion speed often surpasses the speed of thermal propagation in the tissue operated on. Our approach relies solely on thermographer feedback and a knowledge of the power level and position of the electrosurgical pencil, imposing only very minor adjustments to normal electrosurgery to obtain a high-fidelity model of the tissue-probe interaction. Our method is minimally invasive and can be performed in situ. We apply our method first to simulated data based on porcine muscle tissue to verify its accuracy and then to in vivo liver tissue, and compare the results with those from the literature. This comparison shows that parameterizing the Maxwell–Cattaneo model through the framework proposed yields a noticeably higher fidelity real-time adaptable representation of the thermodynamic tissue response to the electrosurgical impact than currently available. A discussion on the differences between the live and the dead tissue thermodynamics is also provided.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation
Robot-
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Authors:
Jie Zhou;Huanfeng Peng;Steven Su;Rong Song;
Pages: 1858 - 1868
Abstract: Compliance control is crucial for physical human–robot interaction, which can enhance the safety and comfort of robot-assisted rehabilitation. In this study, we designed a spatiotemporal compliance control strategy for a new self-designed wearable lower limb rehabilitation robot (WLLRR), allowing the users to regulate the spatiotemporal characteristics of their motion. The high-level trajectory planner consists of a trajectory generator, an interaction torque estimator, and a gait speed adaptive regulator, which can provide spatial and temporal compliance for the WLLRR. A radial basis function neural network adaptive controller is adopted as the low-level position controller. Over-ground walking experiments with passive control, spatial compliance control, and spatiotemporal compliance control strategies were conducted on five healthy participants, respectively. The results demonstrated that the spatiotemporal compliance control strategy allows participants to adjust reference trajectory through physical human-robot interaction, and can adaptively modify gait speed according to participants’ motor performance. It was found that the spatiotemporal compliance control strategy could provide greater enhancement of motor variability and reduction of interaction torque than other tested control strategies. Therefore, the spatiotemporal compliance control strategy has great potential in robot-assisted rehabilitation training and other fields involving physical human-robot interaction.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Non-Invasive Ultrasound Modulation of Solitary Tract Nucleus Exerts a
Sustainable Antihypertensive Effect in Spontaneously Hypertensive Rats-
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Authors:
Fangyuan Cao;Jingjing Zhang;Dapeng Li;Mengke Wang;Chunhao Lai;Tianqi Xu;Ayache Bouakaz;Pengyu Ren;Mingxi Wan;Jie Han;Siyuan Zhang;
Pages: 1869 - 1878
Abstract: Objective: We applied the method of non-invasive ultrasound (US) neuromodulation to regulate blood pressure (BP) by stimulating the solitary tract nucleus (NTS) of spontaneously hypertensive rats (SHRs). Methods: The rats were exposed to US stimulation for 20 mins every day for two months. Morphology and function of the hypertensive target organs (heart and kidney) were then examined by echocardiography and immunohistochemical staining. C-fos immunofluorescence assays were used to evaluate neuronal activity in the US stimulated areas and to explore related neural pathways. Moreover, the effects of US stimulation on biochemical indicators angiotensinII (ANGII), aldosterone (Aldo), endothelin-1 (ET-1), atrial natriuretic factor (ANF), cortisol (Cor) in SHRs were detected. In addition, HE, TUNEL, and Nissl staining were performed to evaluate the safety of long-term transcranial US stimulation. Results: After two months of US stimulation, systolic blood pressure (SBP) decreased from 170 ± 1.1 mmHg to 158 ± 1.8 mmHg, p < 0.01. What's more, US stimulation effectively inhibited the pathological process of target organs from both morphological and functional levels. With US stimulation, neuronal activities were also significantly enhanced in the NTS, ventrolateral periaqueductal gray (vlPAG), and the caudal ventrolateral medulla (CVLM) region. And US stimulation did not cause brain tissue damage. Meanwhile, the plasma levels of ANF, ANGII, Aldo, and Cor content were inhibited. Conclusion: US stimulation of the NTS could significantly lower BP in SHRs. Significance: Non-invasive transcranial US stimulation acting on the NTS might be a potential therapeutic intervention due to its efficacy and safety.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Robust Bayesian Estimation of EEG-Based Brain Causality Networks
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Authors:
Ke Liu;Qin Lai;Peiyang Li;Zhuliang Yu;Bin Xiao;Cuntai Guan;Wei Wu;
Pages: 1879 - 1890
Abstract: Objective: The multivariate autoregression (MVAR) model is an effective model to construct brain causality networks. However, the accuracy of MVAR parameter estimation is considerably affected by outliers such as head movements and eye blinks contained in EEG signals, especially in short time windows. Methods: We proposed a robust MVAR parameter estimation method based on a Bayesian probabilistic framework and Laplace fitting error known as Lap-SBL. With the Bayesian inference framework, we can accurately estimate the MVAR parameters under short time windows. Additionally, to alleviate the influence of outliers, we model the fitting error using the Laplace distribution instead of the typical Gaussian distribution. We employ convex analysis to model the inference task by approximating the Laplace noise prior with a maximum over Gaussian functions with varying scales. The variational inference approach was used to efficiently estimate the MVAR parameters. Results: The numerical results suggest that the proposed method obtains less parameter estimation bias and more consistent linkages than existing benchmark methods, i.e., LS, LASSO, LAPPS and SBL. The motor imagery experimental data analysis shows that Lap-SBL can better describe the lateralization characteristics of brain network. This lateralization is less apparent in a subject with poor MI classification accuracy. Conclusion and significance: Lap-SBL effectively suppresses the influence of outliers and recovers reliable networks in the presence of outliers and short time windows.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Optical Coherence Tomography of Tumor Spheroids Identifies Candidates for
Drug Repurposing in Ovarian Cancer-
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Authors:
Feng Yan;Ji-Hee Ha;Yuyang Yan;Sam B. Ton;Chen Wang;Bornface Mutembei;Zaid A. Alhajeri;Aubrey F. McNiel;Andrew J. Keddissi;Qinghao Zhang;Muralidharan Jayaraman;Danny N. Dhanasekaran;Qinggong Tang;
Pages: 1891 - 1901
Abstract: Objective: Multicellular tumor spheroids (MCTs) are indispensable models for evaluating drug efficacy for precision cancer therapeutic strategies as well as for repurposing FDA-approved drugs for ovarian cancer. However, current imaging techniques cannot provide effective monitoring of pathological responses due to shallow penetration and experimentally operative destruction. We plan to utilize a noninvasive optical imaging tool to achieve in vivo longitudinal monitoring of the growth of MCTs and therapeutic responses to repurpose three FDA-approved drugs for ovarian cancer therapy. Methods: A swept-source optical coherence tomography (SS-OCT) system was used to monitor the volume growth of MCTs over 11 days. Three inhibitors of 2-Methoxyestradiol (2-ME), AZD1208, and R-Ketorolac (R-keto) with concentrations of 1, 10, and 25 µM were employed to treat ovarian MCTs on day 5. Three-dimensional (3D), intrinsic optical attenuation contrast, and degree of uniformity were applied to analyze the therapeutic effect of these inhibitors on ovarian MCTs. Results: We found that 2-ME, AZD1208, and R-keto with concentration of 10 and 25 µM significantly inhibited the volume growth of ovarian MCTs. There was no effect to necrotic tissues from all concentrations of 2-ME, AZD1208, and R-keto inhibitors from our OCT results. 2-ME and AZD1208 inhibited the growth of high uniformity tissues within MCTs and higher concentrations provided more significant inhibitory effects. Conclusion: Our results indicated that OCT was capable and reliable to monitor the therapeutic effect of inhibitors to ovarian MCTs and it can be used for the rapid characterization of novel therapeutics for ovarian cancers in the future.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- PIRET—A Platform for Treatment Planning in Electroporation-Based
Therapies-
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Authors:
Enric Perera-Bel;Kenneth N. Aycock;Zaid S. Salameh;Mario Gómez-Barea;Rafael V. Davalos;Antoni Ivorra;Miguel A. González Ballester;
Pages: 1902 - 1910
Abstract: Tissue electroporation is the basis of several therapies. Electroporation is performed by briefly exposing tissues to high electric fields. It is generally accepted that electroporation is effective where an electric field magnitude threshold is overreached. However, it is difficult to preoperatively estimate the field distribution because it is highly dependent on anatomy and treatment parameters. Objective: We developed PIRET, a platform to predict the treatment volume in electroporation-based therapies. Methods: The platform seamlessly integrates tools to build patient-specific models where the electric field is simulated to predict the treatment volume. Patient anatomy is segmented from medical images and 3D reconstruction aids in placing the electrodes and setting up treatment parameters. Results: Four canine patients that had been treated with high-frequency irreversible electroporation were retrospectively planned with PIRET and with a workflow commonly used in previous studies, which uses different general-purpose segmentation (3D Slicer) and modeling software (3Matic and COMSOL Multiphysics). PIRET outperformed the other workflow by 65 minutes (× 1.7 faster), thanks to the improved user experience during treatment setup and model building. Both approaches computed similarly accurate electric field distributions, with average Dice scores higher than 0.93. Conclusion: A platform which integrates all the required tools for electroporation treatment planning is presented. Treatment plan can be performed rapidly with minimal user interaction in a stand-alone platform. Significance: This platform is, to the best of our knowledge, the most complete software for treatment planning of irreversible electroporation. It can potentially be used for other electroporation applications.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Concurrent and Continuous Prediction of Finger Kinetics and Kinematics via
Motoneuron Activities-
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Authors:
Rinku Roy;Yang Zheng;Derek G. Kamper;Xiaogang Hu;
Pages: 1911 - 1920
Abstract: Objective: Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers. Methods: We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. Results: We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. Conclusion: The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. Significance: Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of de-terous hand skills involving both force control tasks and dynamic movement control tasks.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- 3D Morphology Measurement for Blastocyst Evaluation From “All
Angles”-
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Authors:
Guanqiao Shan;Changsheng Dai;Hang Liu;Xian Wang;Wenkun Dou;Zhuoran Zhang;Yu Sun;
Pages: 1921 - 1930
Abstract: Measuring the 3D morphology of spherical cell aggregates is required in both biology and medicine. Traditional methods either use fluorescent labeling, which cause cell toxicity and are unsuitable for clinical treatment, or use 2D images to roughly estimate 3D morphology. To overcome these limitations, this paper presents a quantitative label-free 3D morphology measurement technique using multi-view images. This technique, for the first time, enables the morphological evaluation of a blastocyst (Day 5 embryo) from “all angles” for IVF treatment. In this technique, a spherical rotation scale invariant feature transform (SR-SIFT) is proposed to address feature distortions for the rotation matrix calculation of the multi-view images. U-Net with generalized Dice loss is used to segment individual trophectoderm (TE) cells and the inner cell mass (ICM) of the blastocyst. Based on the rotation matrices and the segmentation results, the 3D morphological parameters of the entire blastocyst were quantified. Experimental results showed that the error of rotation angle was less than 1$^circ$, the Dice was 95.6% for TE segmentation and 92.3% for ICM segmentation, and the overall measurement error of clinically defined blastocyst parameters was less than 6.7%.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- OCTAve: 2D En Face Optical Coherence Tomography Angiography Vessel
Segmentation in Weakly-Supervised Learning With Locality Augmentation-
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Authors:
Amrest Chinkamol;Vetit Kanjaras;Phattarapong Sawangjai;Yitian Zhao;Thapanun Sudhawiyangkul;Chantana Chantrapornchai;Cuntai Guan;Theerawit Wilaiprasitporn;
Pages: 1931 - 1942
Abstract: Objective: While the microvasculature annotation within Optical Coherence Tomography Angiography (OCTA) can be leveraged using deep-learning techniques, expensive annotation processes are required to create sufficient training data. One way to avoid the expensive annotation is to use a type of weak annotation in which only the center of the vessel is annotated. However, retaining the final segmentation quality with roughly annotated data remains a challenge. Methods: Our proposed methods, called OCTAve, provide a new way of using weak-annotation for microvasculature segmentation. Since the centerline labels are similar to scribble annotations, we attempted to solve this problem by using the scribble-based weakly-supervised learning method. Even though the initial results look promising, we found that the method could be significantly improved by adding our novel self-supervised deep supervision method based on Kullback-Liebler divergence. Results: The study on large public datasets with different annotation styles (i.e., ROSE, OCTA-500) demonstrates that our proposed method gives better quantitative and qualitative results than the baseline methods and a naive approach, with a p-value less than 0.001 on dice's coefficient and a lot fewer artifacts. Conclusion: The segmentation results are both qualitatively and quantitatively superior to baseline weakly-supervised methods when using scribble-based weakly-supervised learning augmented with self-supervised deep supervision, with an average drop in segmentation performance of less than 10%. Significance: This work gives a new perspective on how weakly-supervised learning can be used to reduce the cost of annotating microvasculature, which can make the annotating process easier and reduce the amount of work for domain experts.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Contrastive Multi-View Composite Graph Convolutional Networks Based on
Contribution Learning for Autism Spectrum Disorder Classification-
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Authors:
Hao Zhu;Jun Wang;Yin-Ping Zhao;Minhua Lu;Jun Shi;
Pages: 1943 - 1954
Abstract: The resting-state functional magnetic resonance imaging (rs-fMRI) faithfully reflects the brain activities and thus provides a promising tool for autism spectrum disorder (ASD) classification. Up to now, graph convolutional networks (GCNs) have been successfully applied in rs-fMRI based ASD classification. However, most of these methods were developed based on functional connectivities (FCs) that only reflect low-level correlation between brain regions, without integrating both high-level discriminative knowledge and phenotypic information into classification. Besides, they suffered from the overfitting problem caused by insufficient training samples. To this end, we propose a novel contrastive multi-view composite GCN (CMV-CGCN) for ASD classification using both FCs and HOFCs. Specifically, a pair of graphs are constructed based on the FC and HOFC features of the subjects, respectively, and they share the phenotypic information in the graph edges. A novel contrastive multi-view learning method is proposed based on the consistent representation of both views. A contribution learning mechanism is further incorporated, encouraging the FC and HOFC features of different subjects to have various contribution in the contrastive multi-view learning. The proposed CMV-CGCN is evaluated on 613 subjects (including 286 ASD patients and 327 NCs) from the Autism Brain Imaging Data Exchange (ABIDE). We demonstrate the performance of the method for ASD classification, which yields an accuracy of 75.20% and an area under the curve (AUC) of 0.7338. Experimental results show that our proposed method outperforms state-of-the-art methods on the ABIDE database.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A
Benchmark Study-
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Authors:
Matthew Ng;Fumin Guo;Labonny Biswas;Steffen E. Petersen;Stefan K. Piechnik;Stefan Neubauer;Graham Wright;
Pages: 1955 - 1966
Abstract: Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. Methods: We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. Results: We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31–48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75–78% of all images). Conclusion: This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. Significance: Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- The Effect of Multiple Factors on Working Memory Capacities: Aging, Task
Difficulty, and Training-
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Authors:
Tao Xu;Jiajia Huang;Zian Pei;Jiaqing Chen;Junhua Li;Anastasios Bezerianos;Nitish Thakor;Hongtao Wang;
Pages: 1967 - 1978
Abstract: Goal: Working memory (WM) is a memory system with a limited capacity that can process and store information temporarily in the performing of cognitive tasks. Despite WM is known to be influenced by age, the difficulty of tasks and trained or not from behavior studies, little is known about their relationships from the aspect of the brain functional network. Our goal was to explore the factor of aging-related changes of WM with brain functional networks. Methods: In this study, 25 healthy elderly and 23 healthy young volunteers were recruited for electroencephalogram (EEG) recording during the visual WM task with four difficulty levels (1-4 backs). In each back, we repeat the experiment with four sessions, and we add training sections between session one and session two as well as between session two and session three. However, we remove any training section between session three and session four in order to evaluate the impact of forgetting on WM in different age groups. After the experiment, we utilized graph theoretical analysis to characterize the brain functional network in three frequency bands (alpha, beta, and theta). Results: From the well-designed experiment, we found that physiological aging influences brain network connectivity and makes the functional brain network less differentiated. Moreover, there is an inverse relationship between alpha activity and WM load for the elderly group, which is absent in the young group. At the same time, theta band activity will be correlated with behavioral performance for the elderly group with WM training between sessions, which is also absent in the young group. To further study the influence of difficulty of tasks and training on the WM, we distinguish the tasks with quantified topological characteristics, and the classification results manifest that the training is more effective for the young group. Finally, through the establishment of a brain map before and aft-r training, we find that the right parietal lobe plays an important role in the training of WM for the elderly group whereas the beta band plays an important role in WM for both the elderly group and the young group. Conclusion: Taken together, our findings clarify the underlying mechanism of WM under different frequency bands in terms of physiological aging, the influence of training, and task difficulty. Significance: the working memory capacities can be uncovered in terms of the combination of three-way ANOVA and EEG-based graph theoretical analysis.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
- Latent Similarity Identifies Important Functional Connections for
Phenotype Prediction-
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Authors:
Anton Orlichenko;Gang Qu;Gemeng Zhang;Binish Patel;Tony W. Wilson;Julia M. Stephen;Vince D. Calhoun;Yu-Ping Wang;
Pages: 1979 - 1989
Abstract: Objective: Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets. Methods: LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. Results: LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. Conclusion: We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. Significance: We propose a novel prediction algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work can lead to new insights in both algorithm design and neuroscience research.
PubDate:
June 2023
Issue No: Vol. 70, No. 6 (2023)
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