IEEE Journal of Translational Engineering in Health and Medicine-Jtehm最新文献

{"title":"Breathing-Associated Facial Region Segmentation for Thermal Camera-Based Indirect Breathing Monitoring","authors":"Junhwan Kwon;Oyun Kwon;Kyeong Taek Oh;Jeongmin Kim;Sun K. Yoo","doi":"10.1109/JTEHM.2023.3295775","DOIUrl":"10.1109/JTEHM.2023.3295775","url":null,"abstract":"Breathing can be measured in a non-contact method using a thermal camera. The objective of this study investigates non-contact breathing measurements using thermal cameras, which have previously been limited to measuring the nostril only from the front where it is clearly visible. The previous method is challenging to use for other angles and frontal views, where the nostril is not well-represented. In this paper, we defined a new region called the breathing-associated-facial-region (BAFR) that reflects the physiological characteristics of breathing, and extract breathing signals from views of 45 and 90 degrees, including the frontal view where the nostril is not clearly visible. Experiments were conducted on fifteen healthy subjects in different views, including frontal with and without nostril, 45-degree, and 90-degree views. A thermal camera (A655sc model, FLIR systems) was used for non-contact measurement, and biopac (MP150, Biopac-systems-Inc) was used as a chest breathing reference. The results showed that the proposed algorithm could extract stable breathing signals at various angles and views, achieving an average breathing cycle accuracy of 90.9% when applied compared to 65.6% without proposed algorithm. The average correlation value increases from 0.587 to 0.885. The proposed algorithm can be monitored in a variety of environments and extract the BAFR at diverse angles and views.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"505-514"},"PeriodicalIF":3.4,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/fa/2d/jtehm-yoo-3295775.PMC10561734.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an Evaluation System for Transfer Care Skills Using Embroidered Body Pressure and Proximity Sensor","authors":"Hirofumi Kurosaki;Hiromu Shirahata;Junya Kawahara;Yasuhito Kondo;Ken Kondo;Bumsuk Lee;Masato Odagaki","doi":"10.1109/JTEHM.2023.3294062","DOIUrl":"10.1109/JTEHM.2023.3294062","url":null,"abstract":"Objective: It is important to improve caregiving skills to help reduce the strain on inexperienced caregivers. Previous studies on quantifying caregiving skills have predominantly relied on expensive equipment, such as motion-capture systems with multiple infrared cameras or acceleration sensors. To overcome the cost and space limitations of existing systems, we developed a simple evaluation system for transfer care skills that uses capacitive sensors composed of conductive embroidery fibers. The proposed system can be developed with a few thousand US dollars. Method: The developed evaluation system was used to compare the seating position and velocity of a care recipient during transfers from a nursing-care bed to a wheelchair between groups of inexperienced and expert caregivers. To validate the proposed system, we compare the motion data measured by our system and the data obtained from a conventional three-dimensional motion-capture system and force plate. Results: We analyze the relationship between changes in the center of pressure (CoP) recorded by the force plate and the center of gravity (CoG) obtained by the developed system. Evidently, the changes in CoP have a relation with the CoG. We show that the actual seating speed (\u0000<inline-formula> <tex-math>$v_{mathrm {z}}) $ </tex-math></inline-formula>\u0000 measured by the motion-capture system is related to the speed coefficient calculated from our sensor output. A significant difference exists in \u0000<inline-formula> <tex-math>$v_{mathrm {z}}$ </tex-math></inline-formula>\u0000 between the inexperienced group and the physical therapists/occupational therapists’ group. Conclusions: The proposed system can effectively estimate a caregiver’s skill level in transferring patients from a bed to a wheelchair in terms of the seating position and velocity.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"460-468"},"PeriodicalIF":3.4,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10177745","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commute Booster: A Mobile Application for First/Last Mile and Middle Mile Navigation Support for People With Blindness and Low Vision","authors":"Junchi Feng;Mahya Beheshti;Mira Philipson;Yuvraj Ramsaywack;Maurizio Porfiri;John-Ross Rizzo","doi":"10.1109/JTEHM.2023.3293450","DOIUrl":"10.1109/JTEHM.2023.3293450","url":null,"abstract":"Objective: People with blindness and low vision face substantial challenges when navigating both indoor and outdoor environments. While various solutions are available to facilitate travel to and from public transit hubs, there is a notable absence of solutions for navigating within transit hubs, often referred to as the “middle mile”. Although research pilots have explored the middle mile journey, no solutions exist at scale, leaving a critical gap for commuters with disabilities. In this paper, we proposed a novel mobile application, Commute Booster, that offers full trip planning and real-time guidance inside the station. Methods and procedures: Our system consists of two key components: the general transit feed specification (GTFS) and optical character recognition (OCR). The GTFS dataset generates a comprehensive list of wayfinding signage within subway stations that users will encounter during their intended journey. The OCR functionality enables users to identify relevant navigation signs in their immediate surroundings. By seamlessly integrating these two components, Commute Booster provides real-time feedback to users regarding the presence or absence of relevant navigation signs within the field of view of their phone camera during their journey. Results: As part of our technical validation process, we conducted tests at three subway stations in New York City. The sign detection achieved an impressive overall accuracy rate of 0.97. Additionally, the system exhibited a maximum detection range of 11 meters and supported an oblique angle of approximately 110 degrees for field of view detection. Conclusion: The Commute Booster mobile application relies on computer vision technology and does not require additional sensors or infrastructure. It holds tremendous promise in assisting individuals with blindness and low vision during their daily commutes. Clinical and Translational Impact Statement: Commute Booster translates the combination of OCR and GTFS into an assistive tool, which holds great promise for assisting people with blindness and low vision in their daily commute.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"523-535"},"PeriodicalIF":3.4,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10175612","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62231443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Evaluation of a Balanced Compliant Laparoscopic Grasper","authors":"Jan-Willem Klok;Roelf Postema;Asþor T. Steinþorsson;Jenny Dankelman;Tim Horeman","doi":"10.1109/JTEHM.2023.3291925","DOIUrl":"10.1109/JTEHM.2023.3291925","url":null,"abstract":"In laparoscopic surgery, quality of haptic feedback is reduced compared to conventional surgery, leading to unintentional tissue damage during grasping. From the perspective of haptics, poor mechanical design of laparoscopic instrument joints induces friction and a nonlinear actuation-tip force relation. In this study, a novel laparoscopic grasper using compliant joints and a magnetic balancer is presented, and the reduction in hysteresis and friction is evaluated. The hysteresis loop of the novel compliant grasper and two conventional laparoscopic graspers (high quality leading commercial brand and low quality unbranded grasper) were measured. In order to assess quality of haptic feedback, the lowest grasper tip load perceivable by instrument users was measured with the novel and the conventional laparoscopic graspers. The hysteresis loop measurement yielded a mechanical efficiency of 43% for the novel grasper, compared to- 25% and 23% for the Aesculap and the unbranded grasper, respectively. The forces perceivable by the user through the novel grasper were significantly lower (mean 1.37N, SD 0.44N) than those of conventional graspers (mean 2.15N, SD 0.71N and mean 2.65N, SD 1.20N, respectively). The balanced compliant grasper technology has the ability to improve the quality of haptic feedback compared to conventional laparoscopic graspers. Research is needed to relate these results to soft and delicate tissue grasping in a clinical setting, for which this instrument is intended.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"451-459"},"PeriodicalIF":3.4,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10172011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous Control of Venous Reservoir Level and Arterial Flow Rate in Cardiopulmonary Bypass With a Centrifugal Pump","authors":"Hidenobu Takahashi;Takuya Kinoshita;Zu Soh;Shigeyuki Okahara;Satoshi Miyamoto;Shinji Ninomiya;Toshio Tsuji","doi":"10.1109/JTEHM.2023.3290951","DOIUrl":"10.1109/JTEHM.2023.3290951","url":null,"abstract":"Cardiopulmonary bypass (CPB) is an indispensable technique in cardiac surgery, providing the ability to temporarily replace cardiopulmonary function and create a bloodless surgical field. Traditionally, the operation of CPB systems has depended on the expertise and experience of skilled perfusionists. In particular, simultaneously controlling the arterial and venous occluders is difficult because the blood flow rate and reservoir level both change, and failure may put the patient’s life at risk. This study proposes an automatic control system with a two-degree-of-freedom model matching controller nested in an I-PD feedback controller to simultaneously regulate the blood flow rate and reservoir level. CPB operations were performed using glycerin and bovine blood as perfusate to simulate flow-up and flow-down phases. The results confirmed that the arterial blood flow rate followed the manually adjusted target venous blood flow rate, with an error of less than 5.32%, and the reservoir level was maintained, with an error of less than 3.44% from the target reservoir level. Then, we assessed the robustness of the control system against disturbances caused by venting/suction of blood. The resulting flow rate error was 5.95%, and the reservoir level error 2.02%. The accuracy of the proposed system is clinically satisfactory and within the allowable error range of 10% or less, meeting the standards set for perfusionists. Moreover, because of the system’s simple configuration, consisting of a camera and notebook PC, the system can easily be integrated with general CPB equipment. This practical design enables seamless adoption in clinical settings. With these advancements, the proposed system represents a significant step towards the automation of CPB.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"435-440"},"PeriodicalIF":3.4,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10168928","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9945552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Epileptic Seizure Prediction Method Based on CBAM-3D CNN-LSTM Model","authors":"Xiang Lu;Anhao Wen;Lei Sun;Hao Wang;Yinjing Guo;Yande Ren","doi":"10.1109/JTEHM.2023.3290036","DOIUrl":"10.1109/JTEHM.2023.3290036","url":null,"abstract":"Epilepsy as a common disease of the nervous system, with high incidence, sudden and recurrent characteristics. Therefore, timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. Epilepsy seizures is the result of temporal and spatial evolution, Existing deep learning methods often ignore its spatial features, in order to make better use of the temporal and spatial characteristics of epileptic EEG signals. We propose a CBAM-3D CNN-LSTM model to predict epilepsy seizures. First, we apply short-time Fourier transform(STFT) to preprocess EEG signals. Secondly, the 3D CNN model was used to extract the features of preictal stage and interictal stage from the preprocessed signals. Thirdly, Bi-LSTM is connected to 3D CNN for classification. Finally CBAM is introduced into the model. Different attention is given to the data channel and space to extract key information, so that the model can accurately extract interictal and pre-ictal features. Our proposed approach achieved an accuracy of 97.95%, a sensitivity of 98.40%, and a false alarm rate of 0.017 h\u0000⁻¹\u0000 on 11 patients from the public CHB-MIT scalp EEG dataset. \u0000<italic><b>Clinical and Translational Impact Statement</b></i>\u0000—Timely prediction of epileptic seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"417-423"},"PeriodicalIF":3.4,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10164022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9814105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TransU²-Net: An Effective Medical Image Segmentation Framework Based on Transformer and U²-Net","authors":"Xiang Li;Xianjin Fang;Gaoming Yang;Shuzhi Su;Li Zhu;Zekuan Yu","doi":"10.1109/JTEHM.2023.3289990","DOIUrl":"10.1109/JTEHM.2023.3289990","url":null,"abstract":"Background: In the past few years, U-Net based U-shaped architecture and skip-connections have made incredible progress in the field of medical image segmentation. U\u0000²\u0000-Net achieves good performance in computer vision. However, in the medical image segmentation task, U\u0000²\u0000-Net with over nesting is easy to overfit. Purpose: A 2D network structure TransU\u0000²\u0000-Net combining transformer and a lighter weight U\u0000²\u0000-Net is proposed for automatic segmentation of brain tumor magnetic resonance image (MRI). Methods: The light-weight U\u0000²\u0000-Net architecture not only obtains multi-scale information but also reduces redundant feature extraction. Meanwhile, the transformer block embedded in the stacked convolutional layer obtains more global information; the transformer with skip-connection enhances spatial domain information representation. A new multi-scale feature map fusion strategy as a postprocessing method was proposed for better fusing high and low-dimensional spatial information. Results: Our proposed model TransU\u0000²\u0000-Net achieves better segmentation results, on the BraTS2021 dataset, our method achieves an average dice coefficient of 88.17%; Evaluation on the publicly available MSD dataset, we perform tumor evaluation, we achieve a dice coefficient of 74.69%; in addition to comparing the TransU\u0000²\u0000-Net results are compared with previously proposed 2D segmentation methods. Conclusions: We propose an automatic medical image segmentation method combining transformers and U\u0000²\u0000-Net, which has good performance and is of clinical importance. The experimental results show that the proposed method outperforms other 2D medical image segmentation methods. \u0000<bold>Clinical Translation Statement</b>\u0000: We use the BarTS2021 dataset and the MSD dataset which are publicly available databases. All experiments in this paper are in accordance with medical ethics.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"441-450"},"PeriodicalIF":3.4,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/75/25/jtehm-fang-3289990.PMC10561737.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple Field-of-View Based Attention Driven Network for Weakly Supervised Common Bile Duct Stone Detection","authors":"Ya-Han Chang;Meng-Ying Lin;Ming-Tsung Hsieh;Ming-Ching Ou;Chun-Rong Huang;Bor-Shyang Sheu","doi":"10.1109/JTEHM.2023.3286423","DOIUrl":"10.1109/JTEHM.2023.3286423","url":null,"abstract":"Objective: Common bile duct (CBD) stones caused diseases are life-threatening. Because CBD stones locate in the distal part of the CBD and have relatively small sizes, detecting CBD stones from CT scans is a challenging issue in the medical domain. Methods and procedures: We propose a deep learning based weakly-supervised method called multiple field-of-view based attention driven network (MFADNet) to detect CBD stones from CT scans based on image-level labels. Three dominant modules including a multiple field-of-view encoder, an attention driven decoder and a classification network are collaborated in the network. The encoder learns the feature of multi-scale contextual information while the decoder with the classification network is applied to locate the CBD stones based on spatial-channel attentions. To drive the learning of the whole network in a weakly-supervised and end-to-end trainable manner, four losses including the foreground loss, background loss, consistency loss and classification loss are proposed. Results: Compared with state-of-the-art weakly-supervised methods in the experiments, the proposed method can accurately classify and locate CBD stones based on the quantitative and qualitative results. Conclusion: We propose a novel multiple field-of-view based attention driven network for a new medical application of CBD stone detection from CT scans while only image-levels are required to reduce the burdens of labeling and help physicians automatically diagnose CBD stones. The source code is available at \u0000<uri>https://github.com/nchucvml/MFADNet</uri>\u0000 after acceptance. Clinical impact: Our deep learning method can help physicians localize relatively small CBD stones for effectively diagnosing CBD stone caused diseases.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"394-404"},"PeriodicalIF":3.4,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10153581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9854605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer’s Disease Diagnosis","authors":"Zongshuai Qu;Tao Yao;Xinghui Liu;Gang Wang","doi":"10.1109/JTEHM.2023.3285723","DOIUrl":"10.1109/JTEHM.2023.3285723","url":null,"abstract":"Objective: Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD. Methods: We proposed a univariate neurodegeneration biomarker (UNB) based GCN semi-supervised classification framework. We generated UNB by comparing the similarity of individual morphological atrophy pattern and the atrophy pattern of \u0000<inline-formula> <tex-math>$text{A}beta +$ </tex-math></inline-formula>\u0000 AD group according to the brain morphological abnormalities induced by AD. For the GCN semi-supervised classification model, we took the UNBs of individuals as the features of nodes and constructed the weight of edges according to the similarity of phenotypic information between individuals, which explored the essential features of individuals through spectral graph convolution. The attention module was constructed and embedded into the GCN framework, which may refine the input morphological features to highlight the main impact of AD on the cerebral cortex and weaken the instability caused by individual diversities, thereby identifying the significant ROIs affected by AD and improving the classification accuracy. Results: We tested the UNB-GCN framework on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The estimated minimum sample sizes were 156, 349 and 423 for the longitudinal \u0000<inline-formula> <tex-math>$text{A}beta +$ </tex-math></inline-formula>\u0000 AD, \u0000<inline-formula> <tex-math>$text{A}beta +$ </tex-math></inline-formula>\u0000 mild cognitive impairment (MCI) and \u0000<inline-formula> <tex-math>$text{A}beta +$ </tex-math></inline-formula>\u0000 cognitively unimpaired (CU) groups, respectively. And the proposed UNB-GCN framework combined with the attention module can effectively improve the classification performance with 93.90% classification accuracy for AD vs. CU and 82.05% for AD vs. MCI on the validation set. Conclusion: The proposed UNB measures were superior to the conventional volume measures in describing the AD-induced cerebral cortex morphological changes. And the UNB-GCN framework combined with attention module may effectively improve the classification performance between MCI subjects and AD patients. Clinical and Translational Impact Statement: This study aims to predict the early AD patients, so as to help clinicians develop effective interventions to delay the deterioration of AD symptoms.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"405-416"},"PeriodicalIF":3.4,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10149537","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9892878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sparse Independence Component Analysis for Competitive Endogenous RNA Co-Module Identification in Liver Hepatocellular Carcinoma","authors":"Yuhu Shi;Lili Zhou;Weiming Zeng;Boyang Wei;Jin Deng","doi":"10.1109/JTEHM.2023.3283519","DOIUrl":"10.1109/JTEHM.2023.3283519","url":null,"abstract":"Objective: Long non-coding RNAs (lncRNAs) have been shown to be associated with the pathogenesis of different kinds of diseases and play important roles in various biological processes. Although numerous lncRNAs have been found, the functions of most lncRNAs and physiological/pathological significance are still in its infancy. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood. Methods: In order to reveal functional lncRNAs and identify the key lncRNAs, we develop a new sparse independence component analysis (ICA) method to identify lncRNA-mRNA-miRNA expression co-modules based on the competitive endogenous RNA (ceRNA) theory using the sample-matched lncRNA, mRNA and miRNA expression profiles. The expression data of the three RNA combined together is approximated sparsely to obtain the corresponding sparsity coefficient, and then it is decomposed by using ICA constraint optimization to obtain the common basis and modules. Subsequently, affine propagation clustering is used to perform cluster analysis on the common basis under multiple running conditions to obtain the co-modules for the selection of different RNA elements. Results: We applied sparse ICA to Liver Hepatocellular Carcinoma (LIHC) dataset and the experiment results demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules. Conclusion: It may provide insights into the function of lncRNAs and molecular mechanism of LIHC. Clinical and Translational Impact Statement–The results on LIHC dataset demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules, which may provide insights into the function of IncRNAs and molecular mechanism of LIHC.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"384-393"},"PeriodicalIF":3.4,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10145103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9854603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}