Journal of Medical and Biological Engineering

Shen, Chien-Lung; Huang, Tzu-Hao; Hsu, Po-Chun; Ko, Ya-Chi; Chen, Fen-Ling; Wang, Wei-Chun; Kao, Tsair; Chan, Chia-Tai

2020 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-018-0440-8

The article “Respiratory Rate Estimation by Using ECG, Impedance, and Motion Sensing in Smart Clothing”, written by Chien-Lung Shen, Tzu-Hao Huang, Po-Chun Hsu, Ya-Chi Ko, Fen-Ling Chen, Wei-Chun Wang, Tsair Kao, Chia-Tai Chan was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 1 July 2017 without open access. After publication in volume [37], issue [6], page [826–842] the authors decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed to © The Author(s) 2018 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Tsai, Yuh-Feng; Hsu, Li-Ho; Wu, Chang-Chin; Cai, Wei-Hua; Yang, Kai-Chiang; Fan, Fang-Yu

2020 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-018-0427-5

The article “Long-Term Oral Toxicity and Anti-osteoporotic Effect of Sintered Dicalcium Pyrophosphate in Rat Model of Postmenopausal Osteoporosis”, written by Yuh-Feng Tsai, Li-Ho Hsu, Chang-Chin Wu, Wei-Hua Cai, Kai-Chiang Yang and Fang-Yu Fan was originally published Online First without open access. After publication in volume [37], issue [2], page [181–190] the author decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed to © The Author(s) [2018] and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Hedman, Thomas P.; Chen, Weng-Pin; Lin, Leou-Chyr; Lin, Hsiu-Jen; Chuang, Shih-Youeng

2020 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-018-0444-4

After publication in volume [37], issue [1], page [94–101] the authors decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed to © The Author(s) 2018 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Wu, Hui-Dong; Wong, Man-Sang

2020 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-020-00558-z

PurposeThe plane of maximum curvature (PMC), end-apical-end vertebrae plane (EAEP), and best-fit plane (BFP) have been used to describe the three-dimensional (3D) features of scoliosis but no thorough analyses were conducted. This study aimed to systematically review these descriptors about their potential differences, measurement techniques, and applications in scoliosis.MethodsArticles were retrieved from six databases and Google Scholar search engine using the keywords “maximum spinal deformity” and “scoliosis” combined with “And”.ResultsBFP was found superior to EAEP and PMC in describing the 3D features of scoliosis; however, whether this advantage changes when BFP or EAEP orientation is simplified remains unknown. With the development of 3D reconstruction technique, radiographs and ultrasound images can be used to estimate maximum spinal deformity. The three descriptors have been applied in 3D assessment, progression monitoring, and classification of scoliosis, as well as evaluation of orthotic and surgical treatments but are rarely considered in major clinical decision-making.ConclusionMore evidence is needed to support the superiority of PMC and simplified EAE and BFP, the accuracy of radiographic and ultrasound techniques, and the application of these descriptors to clinical decision-making. Further studies are deserved.

Baek, Dong-Hyun; Ahn, Seungjoon; Kim, Ho Seob; Kim, Dae Wook

2020 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-020-00540-9

PurposeA novel micro-electrocorticography (μ-ECoG) device was designed and its fabrication process was developed to demonstrate the possibility of electrical stimulation and observe visualized signals of activated neurons.MethodsWe designed a donut-type μ-ECoG electrode with a hole at the center of a micro-sized disk-type electrode. Further, we introduced a Pt electroplated nanostructure formation for 3 and 8 min on a gold electrode surface. We evaluated the electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) that depend on the dimensions and Pt electroplating time of the fabricated μ-ECoG devices.ResultsThe results confirmed that the electrochemical properties of the Pt electroplated donut-type electrode are similar to those of the conventionally used disk-type electrodes through the required electroplating time. Through the equivalent circuit model, EIS, and CV analysis, the donut-type μ-ECoG proved to have good electrochemical characteristics between the electrolyte and electrode surface.ConclusionThe proposed μ-ECoG device is suitable for stimulation electrodes without cytotoxic materials and it simultaneously acquires optical information through the window.

Kara, David Delil; Ring, Matthias; Hennig, Friedrich Frank; Michelson, Georg

2020 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-020-00542-7

PurposeThe study aimed to evaluate stereopsis as a surrogate marker for post-concussion oculomotor function to develop an objective test that can reliably and quickly detect mild traumatic brain injuries (TBI).MethodsThe cohort of this prospective clinical study included 30 healthy subjects (mean age 25 ± 2 years) and 30 TBI patients (43 ± 22 years) comprising 11 patients with moderate TBI and 19 patients with mild TBI. The healthy subjects were examined once, whereas the TBI patients were examined immediately after hospitalization, at 1 week, and at 2 months. A virtual reality (VR) program displayed three-dimensional rendering of four rotating soccer balls over VR glasses in different gaze directions. The subjects were instructed to select the ball that appeared to be raised from the screen as quickly as possible via remote control. The response times and fusion abilities in different gaze directions were recorded.ResultsThe correlation between stereopsis and TBI severity was significant. The response times of the moderate and mild TBI groups were significantly longer than those of the healthy reference group. The response times of the moderate TBI group were significantly longer than those of the mild TBI group. The response times at follow-up examinations were significantly shorter than those immediately after hospitalization. Fusion ability was primarily defective in the gaze direction to the right (90°) and left (270° and 315°).ConclusionsTBI patients showed impaired stereopsis. Measuring stereopsis in different positions of the visual field using VR can be effective for rapid concussion assessment.

Lim, Chiho; Ko, Hoo Sang; Cho, Sohyung; Ohu, Ikechukwu; Wang, Henry E.; Griffin, Russell; Kerrey, Benjamin; Carlson, Jestin N.

2020 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-020-00541-8

PurposeTraditionally, proficiency in endotracheal intubation (ETI) has been assessed by human supervisors in a subjective manner during training sessions; however, recent advances in sensor and computing technology have made it possible to obtain objective measures to evaluate the practitioner's performance. This study presents an automated and objective ETI assessment system based on multi-sensor integration which aims at discriminating experienced from novice providers accurately.MethodsTo this end, four different types of sensors were used to collect data, including hand motion of the provider, and tongue force, incisor force and head angle of the training mannequin. Features were extracted from the datasets, and relevant ones were identified by applying feature selection algorithms to create individual and integrated feature sets. An artificial neural network-based classification model was developed for each feature set.ResultsThe results show that a classifier based on a small number of integrated features achieves the best accuracy (96.4%), significantly higher than the best obtained by any individual feature sets (91.17% by hand motion).ConclusionThis study demonstrated the feasibility of a multi-sensor based ETI assessment system that can provide practitioners with objective and timely feedback about their performance.

Fu, Xue; Chen, Chunxiao; Li, Dongsheng

2020 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-020-00559-y

PurposeGlioblastoma is the most common primary brain tumor worldwide. Computer-aided survival prediction can provide a scientific basis for doctors to develop treatment plans to effectively avoid excessive treatment and waste of medical resources. Therefore, we used an end-to-end deep network based on radiomics to make survival predictions of patients with glioblastoma.Methods360 magnetic resonance imaging images of glioblastoma patients were randomly selected from the TCIA database including T1 weighted and T2 weighted images. Based on the traditional residual network (ResNet), a two-branch residual network survival prediction (BSP) model was proposed to extract and learn the features from T1 and T2 images separately. Furthermore, considering the association of tumor area and whole brain tissues, a multi-branch residual network survival prediction (M-BSP) model was proposed, which can make full use of the features of the tumor image and the whole brain image.ResultsThe classification accuracy of M-BSP model using different amounts of residual blocks on test sequence was 89%, 83% and 83%, respectively, demonstrating that the M-BSP model with two residual blocks performed better in prediction. Further, the survival analysis of the prediction results indicated that the M-BSP model can effectively classify patients into a high-risk group and a low-risk group.ConclusionThe classification and prediction results demonstrated that the M-BSP model can attain superior classification results, which can assist doctors in making diagnostic decisions and developing treatment plans.

Shajil, Nijisha; Mohan, Sasikala; Srinivasan, Poonguzhali; Arivudaiyanambi, Janani; Arasappan Murrugesan, Arunnagiri

2020 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-020-00538-3

PurposeBrain–Computer Interface (BCI) system offers a new means of communication for those with paralysis or severe neuromuscular disorders. BCI systems based on Motor Imagery (MI) Electroencephalography (EEG) signals enable the user to convert their thoughts into actions without any voluntary muscle movement. Recently, Convolutional neural network (CNN) is used for the classification of MI signals. However, to produce good MI classification, it is necessary to effectively represent the signal as an input image to the CNN and train the deep learning classifier using large training data.MethodsIn this work, EEG signals are acquired over 16 channels and are filtered using a bandpass filter with the frequency range of 1 to 100 Hz. The processed signal is spatially filtered using Common Spatial Pattern (CSP) filter. The spectrograms of the spatially filtered signals are given as input to CNN. A single convolutional layer CNN is designed to classify left hand, right hand, both hands, and feet MI EEG signals. The size of the training data is increased by augmenting the spectrograms of the EEG signals.ResultsThe CNN classifier was evaluated using MI signals acquired from twelve healthy subjects. Results show that the proposed method achieved an average classification accuracy of 95.18 ± 2.51% for two-class (left hand and right hand) and 87.37 ± 1.68% for four-class (Left hand, Right hand, Both hands, and Feet) MI.ConclusionThus, the method manifests that this 2D representation of 1D EEG signal along with image augmentation shows a high potential for classification of MI EEG signals using the designed CNN model.

Özkal, Fatih Mehmet; Cakir, Ferit; Sensoz, Ersin

2020 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-020-00528-5

PurposeFemoral neck fracture (FNF) is one of the most observed orthopedic injuries in elderly patients with accompanying osteoporosis, while treatment process could be highly troublesome in young patients. Therefore, it is necessary to apply a strong fixation to the FNFs. This study aims to suggest an approach for the optimum screw design for FNFs using genetic algorithm (GA) and finite element method (FEM) in a seriously shorter time, considering that a very large number for the design of the implants comes forward that would take a lifetime to solve individually.MethodsIn biomechanical studies conducted under laboratory conditions and focusing on stabilization, limited number of combinations have been tested with limited materials by now. However, ideal position, size and number of the screws are still subject of discussion. Unlike previous biomechanical studies; the present study addresses three types of CSFs (binary screw, triple screw and quadruple screw), while aiming to determine the optimum position, size and number of the screws using a design approach based on GA and FEM.ResultsThis study emphasizes that screw configuration plays an important role on the treatment process of the femur. As a result of all evaluations and analyses, the most effective designs have been achieved for binary, triple and quadruple screw patterns.ConclusionIn this study, all of the possible combinations and screw sizes have been evaluated to determine the optimum conditions for fracture stability. Suggested design approach could be used more effectively by healthcare disciplines such as orthopedics, in which biomechanical principles are significant. Moreover, cooperation between structural and biomechanical engineering is another remarkable eligibility of this research.