Biomedical Engineering Letters最新文献

Telemedicine data are measured directly by untrained patients, which may cause problems in data reliability. Many deep learning-based studies have been conducted to improve the quality of measurement data. However, they could not provide an accurate basis for judgment. Therefore, this study proposed a deep neural network filter-based reliability evaluation system that could present an accurate basis for judgment and verified its reliability by evaluating photoplethysmography signal and change in data quality according to judgment criteria through clinical trials. In the results, the deviation of 3% or more when the oxygen saturation was judged as normal according to each criterion was 0.3% and 0.82% for criteria 1 and 2, respectively, which was very low compared to the abnormal judgment (3.86%). The deviation of diastolic blood pressure (≥ 10 mmHg) according to criterion 3 was reduced by about 4% in the normal judgment compared to the abnormal. In addition, when multiple judgment conditions were satisfied, abnormal data were better discriminated than when only one criterion was satisfied. Therefore, the basis for judging abnormal data can be presented with the system proposed in this study, and the quality of telemedicine data can be improved according to the judgment result.

Chest X-Ray (CXR) images provide most anatomical details and the abnormalities on a 2D plane. Therefore, a 2D view of the 3D anatomy is sometimes sufficient for the initial diagnosis. However, close to fourteen commonly occurring diseases are sometimes difficult to identify by visually inspecting the images. Therefore, there is a drift toward developing computer-aided assistive systems to help radiologists. This paper proposes a deep learning model for the classification and localization of chest diseases by using image-level annotations. The model consists of a modified Resnet50 backbone for extracting feature corpus from the images, a classifier, and a pixel correlation module (PCM). During PCM training, the network is a weight-shared siamese architecture where the first branch applies the affine transform to the image before feeding to the network, while the second applies the same transform to the network output. The method was evaluated on CXR from the clinical center in the ratio of 70:20 for training and testing. The model was developed and tested using the cloud computing platform Google Colaboratory (NVidia Tesla P100 GPU, 16 GB of RAM). A radiologist subjectively validated the results. Our model trained with the configurations mentioned in this paper outperformed benchmark results.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-022-00249-5.

Brain-machine interface (BMI) provides an alternative route for controlling an external device with one's intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.

Retinal pigmentosa (RP) patients lose vision due to the loss of photoreceptors. Retinal prostheses bypass the dead photoreceptors by electrically stimulating surviving retinal neurons, such as bipolar cells or retinal ganglion cells (RGCs). In previous studies, stimulus charge has been mainly optimized to maximize the RGC response to electrical stimulation. This study aimed to investigate the effect of amplitude and duration even under the same charge condition on eliciting RGC spikes in the wild-type and degenerate retinas. Wild-type (WT) Sprague-Dawley rats were used as the normal retinal model, and Pde6b knockout rats were used as a retinal degeneration (RD) model. Electrically-evoked RGC spikes were recorded from isolated rat retinas using an 8 $\times$ 8 multielectrode array. The same charge was maintained (10 or 20 nC), and electrical stimulation was applied to WT and RD retinas, adjusting the amplitude and duration of the 1st phase of biphasic pulses. In the pulse modulation of the 1st phase, high amplitude (short duration) pulses induced more RGC spikes than low amplitude (long duration) pulses. Both WT and RD retinas showed a significant reduction in the number of RGC spikes upon stimulation with lower amplitude (longer duration) pulses. In clinical trials where stimulus charges are delivered to the degenerate retina of blind patients, high amplitude (short duration) pulses would help elicit more RGC spikes.

Cervical spinal cord injury (SCI) can significantly impair an individual's hand functionality due to the disruption of nerve signals from the brain to the upper extremity. Robotic assistive hand exoskeletons have been proposed as a potential technology to facilitate improved patient rehabilitation outcomes, but few exoskeleton studies utilize standardized hand function tests and questionnaires to produce quantitative data regarding exoskeleton performance. This work presents the human subject case study evaluation of the FLEXotendon Glove-III, a 5 degree-of-freedom voice-controlled, tendon-driven soft robotic assistive hand exoskeleton for individuals with SCI. The exoskeleton system was evaluated in a case study with two individuals with SCI through two standardized hand function tests namely, the Jebsen-Taylor Hand Function Test and the Toronto Rehabilitation Institute Hand Function Test and three questionnaires (Capabilities of Upper Extremities Questionnaire, Orthotics Prosthetics Users Survey, Quebec User Evaluation of Satisfaction with Assistive Technology). Minor design changes were made to the exoskeleton: integrated fingertip force sensors to sense excessive grasp force, a quick connect system to expedite the exoskeleton glove swapping process between users, compact tendon tension sensors to measure tendon force for admittance control, and a redesigned smartphone app to encompass all aspects of exoskeleton use.

Flexible and stretchable neural electrodes are promising tools for high-fidelity interfacing with soft and curvilinear brain surface. Here, we describe a flexible and stretchable neural electrode array that consists of polyacrylonitrile (PAN) nanofiber network reinforced gold (Au) film electrodes. Under stretching, the interweaving PAN nanofibers effectively terminate the formation of propagating cracks in the Au films and thus enable the formation of a dynamically stable electrode-tissue interface. Moreover, the PAN nanofibers increase the surface roughness and active surface areas of the Au electrodes, leading to reduced electrochemical impedance and improved signal-to-noise ratio. As a result, PAN nanofiber network reinforced Au electrode arrays can allow for reliable in vivo multichannel recording of epileptiform activities in rats.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-022-00257-5.

This paper proposes an efficient algorithm for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the neural membrane time constant and input-output (IO) curve parameters in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). The proposed SPE is performed by administering a train of optimally tuned TMS pulses and updating the estimations until a stopping rule is satisfied or the maximum number of pulses is reached. The pulse amplitude is computed by the Fisher information maximization. The pulse width is chosen by maximizing a normalized depolarization factor, which is defined to separate the optimization and tuning of the pulse amplitude and width. The normalized depolarization factor maximization identifies the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane. The effectiveness of the proposed algorithm is evaluated through simulation. The results confirm satisfactory estimation of the membrane time constant and IO curve parameters for the simulation case. By defining the stopping rule based on the satisfaction of the convergence criterion with tolerance of 0.01 for 5 consecutive times for all parameters, the IO curve parameters are estimated with 52 TMS pulses, with absolute relative estimation errors (AREs) of less than 7%. The membrane time constant is estimated with 0.67% ARE, and the pulse width value tends to the critical pulse width with 0.16% ARE with 52 TMS pulses. The results confirm that the pulse width and amplitude can be tuned optimally and automatically to estimate the membrane time constant and IO curve parameters in real-time with closed-loop EMG-guided cTMS.

Plasma energy has been used to provide minimally invasive interventional treatment for spinal problems. However, this procedure has been used for limited indications mainly because of its small resection range. To overcome this problem, we designed the enhanced power plasma device. This device seeks to maximize the resection area by modifying the electrode arrangement and enhancing the maximum electric power. The purpose of this study is to assess the efficiency and safety of this newly designed plasma generator, a device for percutaneous disc decompression. We performed an intradiscal procedure on 7 fresh human cadaver lumbar spine specimens using the enhanced power plasma under C-arm fluoroscopic guidance at various voltages. As a result, the volume of the removed area was proportional to the applied magnitude of the electric power level. In particular, under the high-power level condition after 500 s treatment, nearly the entire nucleus pulposus was eliminated. The generated plasma density also tends to grow along with the given electric power. The highest level of temperature rise did not exceed the level that would lead to degeneration in the collagen tissue of the intervertebral disc. Histopathologic examination also demonstrated that there was no thermal damage to the surrounding neural tissues. In conclusion, we speculate that the concepts of this newly designed enhanced plasma generator could be applied to remove huge disc materials without thermal or structural damage to the adjacent target tissues in future spine clinics.

In this paper, we propose an accurate and rapid non-rigid registration method between blood vessels in temporal 3D cardiac computed tomography angiography images of the same patient. This method provides auxiliary information that can be utilized in the diagnosis and treatment of coronary artery diseases. The proposed method consists of the following four steps. First, global registration is conducted through rigid registration between the 3D vessel centerlines obtained from temporal 3D cardiac CT angiography images. Second, point matching between the 3D vessel centerlines in the rigid registration results is performed, and the corresponding points are defined. Third, the outliers in the matched corresponding points are removed by using various information such as thickness and gradient of the vessels. Finally, non-rigid registration is conducted for hierarchical local transformation using an energy function. The experiment results show that the average registration error of the proposed method is 0.987 mm, and the average execution time is 2.137 s, indicating that the registration is accurate and rapid. The proposed method that enables rapid and accurate registration by using the information on blood vessel characteristics in temporal CTA images of the same patient.

While brain-implantable neural spike sorting can be realized using efficient algorithms, the presence of noise may make it difficult to maintain high-peformance sorting using conventional techniques. In this article, we explore the use of partially binarized neural networks (PBNNs), to the best of our knowledge for the first time, for sorting of neural spike feature vectors. It is shown that compared to the waveform template-based methods, PBNNs offer robust spike sorting over various datasets and noise levels. The ASIC implementation of the PBNN-based spike sorting system in a standard 180-nm CMOS process is presented. The post place and route simulations results show that the synthesized PBNN consumes only 0.59 $\mu$ W of power from a 1.8 V supply while operating at 24 kHz and occupies 0.15 mm ${}^2$ of silicon area. It is shown that the designed PBNN-based spike sorting system not only offers comparable accuracy to the state-of-the-art spike sorting systems over various noise levels and datasets, it also occupies a smaller silicon area and consumes less power and energy. This makes PBNNs a viable alternative towards the implementation of brain-implantable spike sorting systems.