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Robust adaptive observer-based control of blood glucose level for type 1 diabetic patient Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-03-15 Masoud Seyedabadi, Ali Akbarzadeh Kalat
In this paper, an adaptive controller is designed to regulate the blood glucose level of type 1 diabetes mellitus while not all states of the system are measurable and also its parameters are unknown. The main goal in the control of diabetes is to preserve blood glucose level within a safe rang by a suitable injecting insulin rate to the patient. Herein, it is achieved by measuring the blood glucose
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Evaluation on safety and efficacy of ultrasound assisted thrombolysis in a sheep artificial heart pump Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-03-13 Yongchun Cui, Xiaobing Zheng, Shuo Wang, Jianye Zhou, Guangxin Yue, Peng Peng, Qiuju Li, Jubo Li, Yue Li, Jiafei Luo, Qi Zhang, Xue Zhang, Yongjian Li, Xin Wang
Thrombosis is a major and serious complication in patients with artificial heart pump assist device (HPAD). There is an urgent need for an efficient and safe method to solve the clinical challenge. We have developed a new type of ultrasound integrated heart pump assist device (uHPAD) with a pair of ultrasonic transducer rings installed around the pump. Based on the experiments, the sonothrombolysis
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Multiwavelength laser doppler holography (MLDH) in spatiotemporal optical coherence tomography (STOC-T) Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-03-12 Dawid Borycki, Egidijus Auksorius, Piotr Węgrzyn, Kamil Liżewski, Sławomir Tomczewski, Ieva Žičkienė, Karolis Adomavičius, Karol Karnowski, Maciej Wojtkowski
Spatiotemporal optical coherence tomography (STOC-T) is the novel modality for high-speed, crosstalk- and aberration-free volumetric imaging of biological tissue . STOC-T extends the Fourier-Domain holographic Optical Coherence Tomography by the spatial phase modulation that enables the reduction of spatial coherence of the tunable laser. By reducing the spatial coherence of the laser, we suppress
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Spatio-spectral independent component analysis for fetal ECG extraction from two-channel maternal abdominal signals Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-02-24 Marian P. Kotas, Anwar M. AlShrouf
Independent component analysis (ICA) is widely used to separate maternal and fetal electrocardiograms. However, it has become less effective due to the efforts to reduce the number of recording electrodes. To address this issue, we propose an extension of ICA that can extract the fetal electrocardiogram from only two maternal abdominal electric signals. We solve this problem by increasing the dimension
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Multi-scale local-global transformer with contrastive learning for biomarkers segmentation in retinal OCT images Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-02-16 Xiaoming Liu, Yuanzhe Ding, Ying Zhang, Jinshan Tang
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A comprehensive analysis of task-specific hand kinematic, muscle and force synergies Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-02-15 Martina Lapresa, Virginia Corradini, Antonio Iacca, Francesco Scotto di Luzio, Loredana Zollo, Francesca Cordella
Synergies were demonstrated to exist in the kinematic, force and muscular domains, and their task-specificity and subject-specificity was also highlighted in literature. Despite that, no works have extracted synergies on specific grasp classes to analyze task-specific synergistic patterns. Moreover, only few studies focused on the combined analysis of kinematic, force and muscle synergies.
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Disruptions in brain functional connectivity: The hidden risk for oxygen-intolerant professional divers in simulated deep water Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-02-08 Emanuela Formaggio, Lucio Pastena, Massimo Melucci, Lucio Ricciardi, Silvia Francesca Storti
In this study, we investigated the effects of oxygen toxicity on brain activity and functional connectivity (FC) in divers using a closed-circuit oxygen breathing apparatus. We acquired and analyzed electroencephalographic (EEG) signals from a group of normal professional divers (PD) and a group that developed oxygen intolerance, i.e., oxygen-intolerant professional divers (OPD), to evaluate the potential
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A systematic review of artificial neural network techniques for analysis of foot plantar pressure Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-02-03 Chongguang Wang, Kerrie Evans, Dean Hartley, Scott Morrison, Martin Veidt, Gui Wang
Plantar pressure distribution offers insights into foot function, gait mechanics, and foot-related issues. This systematic review presents an analysis of the use of artificial neural network techniques in the context of plantar pressure analysis. 60 studies were included in the review. Sample size, pathology, pressure sensor number, data collection device, utilization of other sensor devices, ground-truth
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Spatial characterization of functional neural activity during lower limb motion through functional connectivity Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-01-30 Aurora Espinoza-Valdez, Griselda Quiroz-Compean, Andrés A. González-Garrido, Ricardo A. Salido-Ruiz, Luis Mercado
Analyzing electroencephalographic signals (EEG) could provide valuable information about functional neural activity (FNA) during human motion. The hypothesis of this work is twofold: spatial patterns emerge in EEG signals from functional connectivity (FC) analysis during lower limb movements, and the spatial patterns are mosto robust in some frequency bands than in others. Accordingly, a set of human
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Application of context-dependent interpretation of biosignals recognition to control a bionic multifunctional hand prosthesis Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-01-22 Pawel Trajdos, Marek Kurzynski
The paper presents an original method for controlling a surface-electromyography-driven (sEMG) prosthesis. A context-dependent recognition system is proposed in which the same class of sEMG signals may have a different interpretation, depending on the context. This allowed the repertoire of performed movements to be increased. The proposed structure of the context-dependent recognition system includes
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Time-resolved near-infrared spectroscopy in monitoring acute ischemic stroke patients – Case study Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-01-20 Aleksandra Kuls-Oszmaniec, Michał Kacprzak, Magdalena Morawiec, Piotr Sawosz, Urszula Fiszer, Marta Leńska-Mieciek
Stroke is a leading cause of disability and death worldwide, with acute ischemic stroke (AIS) accounting for the majority of cases. Early and accurate diagnosis of AIS is crucial for improving patient outcomes. Non-invasive monitoring techniques, such as time domain near-infrared spectroscopy (tdNIRS), have shown potential for real-time monitoring of AIS patients at the bedside. However, there is a
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Breast cancer diagnosis: A systematic review Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-01-17 Xin Wen, Xing Guo, Shuihua Wang, Zhihai Lu, Yudong Zhang
The second-leading cause of death for women is breast cancer. Consequently, a precise early diagnosis is essential. With the rapid development of artificial intelligence, computer-aided diagnosis can efficiently assist radiologists in diagnosing breast problems. Mammography images, breast thermal images, and breast ultrasound images are the three ways to diagnose breast cancer. The paper will discuss
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On the prediction of the effect of bi-ventricular assistance after cardiac explantation on the vascular flow physiology: A numerical study Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-01-12 Louis Marcel, Mathieu Specklin, Smaine Kouidri, Mickael Lescroart, Jean-Louis Hébert
Heart failure is a chronic and progressive condition characterized by the heart’s inability to pump sufficient blood to meet the body’s metabolic demands. It is a significant public health concern worldwide, associated with high morbidity, mortality, and healthcare costs. For advanced heart failure cases not responding to medical therapy, heart transplantation or mechanical circulatory support with
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Imaging the retinal and choroidal vasculature using Spatio-Temporal Optical Coherence Tomography (STOC-T) Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-01-08 Kamil Liżewski, Slawomir Tomczewski, Dawid Borycki, Piotr Węgrzyn, Maciej Wojtkowski
Spatio-Temporal Optical Coherence Tomography (STOC-T) is a novel imaging technique using light with controlled spatial and temporal coherence. Retinal images obtained using the STOC-T system maintain high resolution in all three dimensions, on a sample of about 700 μm, without the need for mechanical scanning. In the present work, we use known data processing algorithms for optical coherence tomography
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Biomechanical behavior of customized splint for the patient with temporomandibular disorders: A three-dimensional finite element analysis Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2024-01-05 Yunfan Zhu, Fangjie Zheng, Yanji Gong, Deqiang Yin, Yang Liu
The mechanical overloading of temporomandibular joint (TMJ) is generally linked to temporomandibular disorders (TMD). However, in patients with a typical combination of maxillofacial morphology and occlusal features, the reduction of joint load and treatment with general occlusal splints are often ineffective. This study investigates the biomechanical behavior of the stomatognathic system in a TMD
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Insulin resistance: Risk factors, diagnostic approaches and mathematical models for clinical practice, epidemiological studies, and beyond Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-12-26 Janusz Krzymien, Piotr Ladyzynski
Insulin resistance (IR) is a multifactorial metabolic disorder associated with the development of cardiometabolic syndrome, cardiovascular diseases and obesity. Factors such as inflammation, hyperinsulinemia, hyperglucagonemia, mitochondrial dysfunction, glucotoxicity and lipotoxicity contribute to the development of IR. Despite being extensively studied for over 60 years, assessing the incidence of
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Video-based HR measurement using adaptive facial regions with multiple color spaces Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-12-27 Arpita Panigrahi, Hemant Sharma, Atin Mukherjee
Driven by the desire for feasible and convenient healthcare, non-contact heart rate (HR) monitoring based on consumer-grade cameras has gained significant recognition among researchers. However, this technology suffers from performance reliability and consistency in realistic situations of motion artifacts, illumination variations, and skin tones, limiting it to emerge as an alternative to conventional
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A hybrid lightweight breast cancer classification framework using the histopathological images Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-12-22 Daniel Addo, Shijie Zhou, Kwabena Sarpong, Obed T. Nartey, Muhammed A. Abdullah, Chiagoziem C. Ukwuoma, Mugahed A. Al-antari
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Hollow fiber bioreactor with genetically modified hepatic cells as a model of biologically active function block of the bioartificial liver Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-12-07 Malgorzata Jakubowska, Monika Joanna Wisniewska, Agnieszka Wencel, Cezary Wojciechowski, Monika Gora, Krzysztof Dudek, Andrzej Chwojnowski, Beata Burzynska, Dorota Genowefa Pijanowska, Krzysztof Dariusz Pluta
Chronic liver disease and cirrhosis, that can lead to liver failure, are major public health issues, with liver transplantation as the only effective treatment. However, the limited availability of transplantable organs has spurred research into alternative therapies, including bioartificial livers. To date, liver hybrid support devices, using porcine hepatocytes or hepatoma-derived cell lines, have
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Differentiating age and sex in vertebral body CT scans – Texture analysis versus deep learning approach Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-12-09 Karolina Nurzynska, Adam Piórkowski, Michał Strzelecki, Marcin Kociołek, Robert Paweł Banyś, Rafał Obuchowicz
The automated analysis of computed tomography (CT) scans of vertebrae, for the purpose of determining an individual's age and sex constitutes a vital area of research. Accurate assessment of bone age in children facilitates the monitoring of their growth and development. Moreover, the determination of both age and sex has significant relevance in various legal contexts involving human remains. We have
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System and approach to detecting of gastric slow wave and environmental noise suppression based on optically pumped magnetometer Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-12-06 Shuang Liang, Kexin Gao, Junhuai He, Yikang Jia, Hongchen Jiao, Lishuang Feng
Gastric slow waves (SWs) are commonly used for the quantitative assessment of gastric functional disorders. Compared with surface electrogastrography, using of magnetic signals to record SWs can achieve higher-quality signal recording. In this study, we discovered that optically pumped magnetometers (OPM) based on the spin exchange relaxation-free method have comparable weak magnetic detection capabilities
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Automated detection of abnormal respiratory sound from electronic stethoscope and mobile phone using MobileNetV2 Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-11-27 Ximing Liao, Yin Wu, Nana Jiang, Jiaxing Sun, Wujian Xu, Shaoyong Gao, Jun Wang, Ting Li, Kun Wang, Qiang Li
Auscultation, a traditional clinical examination method using a stethoscope to quickly assess airway abnormalities, remains valuable due to its real-time, non-invasive, and easy-to-perform nature. Recent advancements in computerized respiratory sound analysis (CRSA) have provided a quantifiable approach for recording, editing, and comparing respiratory sounds, also enabling the training of artificial
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Decoding motor imagery based on dipole feature imaging and a hybrid CNN with embedded squeeze-and-excitation block Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-11-21 Linlin Wang, Mingai Li
Motor imagery (MI) decoding is the core of an intelligent rehabilitation system in brain computer interface, and it has a potential advantage by using source signals, which have higher spatial resolution and the same time resolution compared to scalp electroencephalography (EEG). However, how to delve and utilize the personalized frequency characteristic of dipoles for improving decoding performance
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Automated detection of crystalline retinopathy via fundus photography using multistage generative adversarial networks Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-11-04 Eun Young Choi, Seung Hoon Han, Ik Hee Ryu, Jin Kuk Kim, In Sik Lee, Eoksoo Han, Hyungsu Kim, Joon Yul Choi, Tae Keun Yoo
Purpose Crystalline retinopathy is characterized by reflective crystal deposits in the macula and is caused by various systemic conditions including hereditary, toxic, and embolic etiologies. Herein, we introduce a novel application of deep learning with a multistage generative adversarial network (GAN) to detect crystalline retinopathy using fundus photography. Methods The dataset comprised major
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Corrigendum to “Multi-stage fully convolutional network for precise prostate segmentation in ultrasound images” [Biocybern. Biomed. Eng. 43(3) (2023) 586–602] Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-10-28 Yujie Feng, Chukwuemeka Clinton Atabansi, Jing Nie, Haijun Liu, Hang Zhou, Huai Zhao, Ruixia Hong, Fang Li, Xichuan Zhou
Abstract not available
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End-to end decision support system for sleep apnea detection and Apnea-Hypopnea Index calculation using hybrid feature vector and Machine learning Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-10-13 Recep Sinan Arslan, Hasan Ulutas, Ahmet Sertol Köksal, Mehmet Bakir, Bülent Çiftçi
Sleep apnea is a disease that occurs due to the decrease in oxygen saturation in the blood and directly affects people's lives. Detection of sleep apnea is crucial for assessing sleep quality. It is also an important parameter in the diagnosis of various other diseases (diabetes, chronic kidney disease, depression, and cardiological diseases). Recent studies show that detection of sleep apnea can be
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Surgical phase classification and operative skill assessment through spatial context aware CNNs and time-invariant feature extracting autoencoders Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-10-13 Chakka Sai Pradeep, Neelam Sinha
Automated surgical video analysis promises improved healthcare. We propose novel spatial context aware combined loss function for end-to-end Encoder-Decoder training for Surgical Phase Classification (SPC) on laparoscopic cholecystectomy (LC) videos. Proposed loss function leverages on fine-grained class activation maps obtained from fused multi-layer Layer-CAM for supervised learning of SPC, obtaining
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Automated detection of multi-class urinary sediment particles: An accurate deep learning approach Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-09-29 He Lyu, Fanxin Xu, Tao Jin, Siyi Zheng, Chenchen Zhou, Yang Cao, Bin Luo, Qinzhen Huang, Wei Xiang, Dong Li
Urine microscopy is an essential diagnostic tool for kidney and urinary tract diseases, with automated analysis of urinary sediment particles improving diagnostic efficiency. However, some urinary sediment particles remain challenging to identify due to individual variations, blurred boundaries, and unbalanced samples. This research aims to mitigate the adverse effects of urine sediment particles while
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Simulation on human respiratory motion dynamics and platform construction Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-09-29 Yudong Bao, Xu Li, Wen Wei, Shengquan Qu, Yang Zhan
Bronchoscopy has a crucial role in the current treatment of lung diseases, and it is typical of interventional medical instruments led by manual intervention. The scientific study of bronchoscopy is now of primary importance in eliminating problems associated with manual intervention by scientific means. However, for its intervention environment, the trachea is often treated statically, without considering
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A dual-stage transformer and MLP-based network for breast ultrasound image segmentation Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-09-09 Guidi Lin, Mingzhi Chen, Minsheng Tan, Lingna Chen, Junxi Chen
Automatic segmentation of breast lesions from ultrasound images plays an important role in computer-aided breast cancer diagnosis. Many deep learning methods based on convolutional neural networks (CNNs) have been proposed for breast ultrasound image segmentation. However, breast ultrasound image segmentation is still challenging due to ambiguous lesion boundaries. We propose a novel dual-stage framework
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Efficient simultaneous segmentation and classification of brain tumors from MRI scans using deep learning Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-09-05 Akshya Kumar Sahoo, Priyadarsan Parida, K. Muralibabu, Sonali Dash
Brain tumors can be difficult to diagnose, as they may have similar radiographic characteristics, and a thorough examination may take a considerable amount of time. To address these challenges, we propose an intelligent system for the automatic extraction and identification of brain tumors from 2D CE MRI images. Our approach comprises two stages. In the first stage, we use an encoder-decoder based
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A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-09-05 Ayse Erdogan Yıldırım, Murat Canayaz
In recent years, many diseases can be diagnosed in a short time with the use of deep learning models in the field of medicine. Most of the studies in this area focus on adult or pediatric patients. However, deep learning studies for the diagnosis of diseases in neonatal are not sufficient. Also, since it is known that respiratory disorders such as pneumonia have a large place among the causes of neonatal
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BA-Net: Brightness prior guided attention network for colonic polyp segmentation Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-08-21 Shuxiang Song, Haiying Xia, Yilin Qin, Yumei Tan
Automatic polyp segmentation at colonoscopy plays an important role in the early diagnosis and surgery of colorectal cancer. However, the diversity of polyps in different images greatly increases the difficulty of accurately segmenting polyps. Manual segmentation of polyps in colonoscopic images is time-consuming and the rate of polyps missed remains high. In this paper, we propose a brightness prior
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Multi-stage fully convolutional network for precise prostate segmentation in ultrasound images Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-08-19 Yujie Feng, Chukwuemeka Clinton Atabansi, Jing Nie, Haijun Liu, Hang Zhou, Huai Zhao, Ruixia Hong, Fang Li, Xichuan Zhou
Prostate cancer is one of the most commonly diagnosed non-cutaneous malignant tumors and the sixth major cause of cancer-related death generally found in men globally. Automatic segmentation of prostate regions has a wide range of applications in prostate cancer diagnosis and treatment. It is challenging to extract powerful spatial features for precise prostate segmentation methods due to the wide
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Attention-guided multiple instance learning for COPD identification: To combine the intensity and morphology Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-07-05 Yanan Wu, Shouliang Qi, Jie Feng, Runsheng Chang, Haowen Pang, Jie Hou, Mengqi Li, Yingxi Wang, Shuyue Xia, Wei Qian
Chronic obstructive pulmonary disease (COPD) is a complex and multi-component respiratory disease. Computed tomography (CT) images can characterize lesions in COPD patients, but the image intensity and morphology of lung components have not been fully exploited. Two datasets (Dataset 1 and 2) comprising a total of 561 subjects were obtained from two centers. A multiple instance learning (MIL) method
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Transformer-based cross-modal multi-contrast network for ophthalmic diseases diagnosis Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-06-27 Yang Yu, Hongqing Zhu
Automatic diagnosis of various ophthalmic diseases from ocular medical images is vital to support clinical decisions. Most current methods employ a single imaging modality, especially 2D fundus images. Considering that the diagnosis of ophthalmic diseases can greatly benefit from multiple imaging modalities, this paper further improves the accuracy of diagnosis by effectively utilizing cross-modal
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Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-06-26 Md. Nahiduzzaman, Md Omaer Faruq Goni, Md. Robiul Islam, Abu Sayeed, Md. Shamim Anower, Mominul Ahsan, Julfikar Haider, Marcin Kowalski
Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases
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Non-invasive waveform analysis for emergency triage via simulated hemorrhage: An experimental study using novel dynamic lower body negative pressure model Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-06-22 Naimahmed Nesaragi, Lars Øivind Høiseth, Hemin Ali Qadir, Leiv Arne Rosseland, Per Steinar Halvorsen, Ilangko Balasingham
The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LBNP
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Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-05-24 Asmaa Maher, Saeed Mian Qaisar, N. Salankar, Feng Jiang, Ryszard Tadeusiewicz, Paweł Pławiak, Ahmed A. Abd El-Latif, Mohamed Hammad
The Brain-computer interface (BCI) is used to enhance the human capabilities. The hybrid-BCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to maximize the advantages of each while minimizing the drawbacks of individual methods. Recently, researchers have started focusing on the Electroencephalogram (EEG) and “Functional Near-Infrared Spectroscopy” (fNIRS) based hBCI.
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Combining homomorphic filtering and recurrent neural network in gait signal analysis for neurodegenerative diseases detection Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-05-16 Masume Saljuqi, Peyvand Ghaderyan
Automatic, cost-effective, and reliable detection of neurodegenerative diseases (NDs) is one of the important issues in clinical practice. The main idea of the proposed method in this study is to utilize the advantages of gait time series that can provide low-cost and non-invasive measures, homomorphic filtering that can effectively separate muscle activity from body dynamic and recurrent neural network
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MDCF_Net: A Multi-dimensional hybrid network for liver and tumor segmentation from CT Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-05-09 Jian Jiang, Yanjun Peng, Qingfan Hou, Jiao Wang
The segmentation of the liver and liver tumors is critical in the diagnosis of liver cancer, and the high mortality rate of liver cancer has made it one of the most popular areas for segmentation research. Some deep learning segmentation methods outperformed traditional methods in terms of segmentation results. However, they are unable to obtain satisfactory segmentation results due to blurred original
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Wavelet-Hilbert transform based bidirectional least squares grey transform and modified binary grey wolf optimization for the identification of epileptic EEGs Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-05-04 Chang Liu, Wanzhong Chen, Tao Zhang
Wavelet based seizure detection is an importance topic for epilepsy diagnosis via electroencephalogram (EEG), but its performance is closely related to the choice of wavelet bases. To overcome this issue, a fusion method of wavelet packet transformation (WPT), Hilbert transform based bidirectional least squares grey transform (HTBiLSGT), modified binary grey wolf optimization (MBGWO) and fuzzy K-Nearest
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Predicting muscle fatigue during dynamic contractions using wavelet analysis of surface electromyography signal Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-04-18 MohammadJavad Shariatzadeh, Ehsan Hadizadeh Hafshejani, Cameron J.Mitchell, Mu Chiao, Dana Grecov
Muscle fatigue is defined as a reduction in the capability of muscle to exert force or power. Although surface electromyography (sEMG) signals during exercise have been used to assess muscle fatigue, analyzing the sEMG signal during dynamic contractions is difficult because of the many signal distorting factors such as electrode movements, and variations in muscle tissue conductivity. Besides the non-deterministic
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PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention U-Net Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-03-08 Anjali Chandra, Shrish Verma, A.S. Raghuvanshi, Narendra Kuber Bodhey
Background The Corpus callosum (Cc) in the cerebral cortex is a bundle of neural fibers that facilitates inter-hemispheric communication. The Cc area and area of its sub-regions (also known as parcels) have been examined as a biomarker for cortical pathology and differential diagnosis in neurodegenerative diseases such as Autism, Alzheimer’s disease (AD), and more. Manual segmentation and parcellation
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In vitro examinations of the anti-inflammatory interleukin functionalized polydopamine based biomaterial as a potential coating for cardiovascular stents Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-02-26 Przemysław Sareło, Beata Sobieszczańska, Edyta Wysokińska, Marlena Gąsior-Głogowska, Wojciech Kałas, Halina Podbielska, Magdalena Wawrzyńska, Marta Kopaczyńska
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FRE-Net: Full-region enhanced network for nuclei segmentation in histopathology images Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-02-24 Xuping Huang, Junxi Chen, Mingzhi Chen, Yaping Wan, Lingna Chen
Accurate nuclei segmentation is a critical step for physicians to achieve essential information about a patient’s disease through digital pathology images, enabling an effective diagnosis and evaluation of subsequent treatments. Since pathology images contain many nuclei, manual segmentation is time-consuming and error-prone. Therefore, developing a precise and automatic method for nuclei segmentation
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SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19 Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-02-15 Kiran Kumar Patro, Jaya Prakash Allam, Mohamed Hammad, Ryszard Tadeusiewicz, Paweł Pławiak
Background and Objective The global population has been heavily impacted by the COVID-19 pandemic of coronavirus. Infections are spreading quickly around the world, and new spikes (Delta, Delta Plus, and Omicron) are still being made. The real-time reverse transcription-polymerase chain reaction (RT-PCR) is the method most often used to find viral RNA in a nasopharyngeal swab. However, these diagnostic
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A deformable CNN architecture for predicting clinical acceptability of ECG signal Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-02-13 Jaya Prakash Allam, Saunak Samantray, Suraj Prakash Sahoo, Samit Ari
The degraded quality of the electrocardiogram (ECG) signals is the main source of false alarms in critical care units. Therefore, a preliminary analysis of the ECG signal is required to decide its clinical acceptability. In conventional techniques, different handcrafted features are extracted from the ECG signal based on signal quality indices (SQIs) to predict clinical acceptability. A one-dimensional
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Prediction of vortex structures in pulsatile flow through S-bend arterial geometry with different stenosis levels Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-02-06 Piru Mohan Khan, Apurva Raj, Md. Irshad Alam, Suman Chakraborty, Somnath Roy
Arterial stenosis poses a high cardiovascular risk, and clinical intervention is needed when these stenoses grow beyond a specific limit. The study of vortex dynamics in these diseased arteries can be beneficial to understand its severity. Therefore, in the present work, we have investigated the flow structures in an S-bend arterial geometry with different levels of stenosis using a sharp interface
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PCG signal classification using a hybrid multi round transfer learning classifier Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-02-08 Shahid Ismail, Basit Ismail
Diagnosis of cardiovascular diseases using Phonocardiography(PCG) is a challenging task as signal itself is cyclo-stationary. It has spectral contents which are overlapped by multiple sources having similar spectral contents but acting as noise. Moreover, length variation in the signals and sampling using different equipment also make analysis of these signal a testing task. In this research, authors
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Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D)2PCA and KELM Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-01-30 Tao Zhang, Wanzhong Chen, Xiaojuan Chen
In order to achieve the accurate identifications of various electroencephalograms (EEGs) and electrocardiograms (ECGs), a unified framework of wavelet scattering transform (WST), bidirectional weighted two-directional two-dimensional principal component analysis (BW(2D)2PCA) and grey wolf optimization based kernel extreme learning machine (KELM) was put forward in this study. To extract more discriminating
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Gray-level co-occurrence matrix of Smooth Pseudo Wigner-Ville distribution for cognitive workload estimation Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-01-24 Rezvan Mirzaeian, Peyvand Ghaderyan
Automatic, cost-effective, and reliable cognitive workload estimation (CWE) is one of the important issues in diagnosis and treatment of neurocognitive diseases, cognitive performance improvement and error preventive strategies. To address this issue, this paper has proposed a novel and robust CWE method by detecting the time–frequency (TF) changes of electrodermal activities (EDA). Firstly, the local
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Supervised denoising of diffusion-weighted magnetic resonance images using a convolutional neural network and transfer learning Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-01-09 Jakub Jurek, Andrzej Materka, Kamil Ludwisiak, Agata Majos, Kamil Gorczewski, Kamil Cepuch, Agata Zawadzka
In this paper, we propose a method for reducing thermal noise in diffusion-weighted magnetic resonance images (DWI MRI) of the brain using a convolutional neural network (CNN) trained on realistic, synthetic MR data. Two reference methods are considered: a) averaging of repeated scans, a widespread method used in clinics to improve signal-to-noise ratio of MR images and b) the blockwise Non-Local Means
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Heart failure disease prediction and stratification with temporal electronic health records data using patient representation Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-01-02 Ye Liang, Chonghui Guo
Accurate early prediction of heart failure and identification of heart failure sub-phenotypes can enable in-time interventions and treatments, assist with policy decisions, and lead to a better understanding of disease pathophysiology in groups of patients. However, decision making more challenging for clinicians since the available data is complex, heterogeneous, temporal, and different in granularity
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Biphasic monolithic osteochondral scaffolds obtained by diffusion-limited enzymatic mineralization of gellan gum hydrogel Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-01-05 Krzysztof Pietryga, Katarzyna Reczyńska-Kolman, Janne E. Reseland, Håvard Haugen, Véronique Larreta-Garde, Elżbieta Pamuła
Biphasic monolithic materials for the treatment of osteochondral defects were produced from polysaccharide hydrogel, gellan gum (GG). GG was enzymatically mineralized by alkaline phosphatase (ALP) in the presence of calcium glycerophosphate (CaGP). The desired distribution of the calcium phosphate (CaP) mineral phase was achieved by limiting the availability of CaGP to specific parts of the GG sample
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Effects of sampling rate on multiscale entropy of electroencephalogram time series Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-01-04 Jinlin Zheng, Yan Li, Yawen Zhai, Nan Zhang, Haoyang Yu, Chi Tang, Zheng Yan, Erping Luo, Kangning Xie
A physiological system encompasses numerous components that function at various time scales. To characterize the scale-dependent feature, the multiscale entropy (MSE) analysis has been proposed to describe the complex processes on multiple time scales. However, MSE analysis uses the relative scale factors to reveal the time-related dynamics, which may cause in-comparability of results from diverse
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Deep brain stimulation of the entorhinal cortex modulates CA1 theta-gamma oscillations in mouse models of preclinical Alzheimer's disease Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-01-04 Yinpei Luo, Yuwei Sun, Huizhong Wen, Xing Wang, Xiaolin Zheng, Hongfei Ge, Yi Yin, Xiaoying Wu, Weina Li, Wensheng Hou
Deep brain stimulation (DBS) is a neuromodulation method that modulates neuronal activity. A trend in the treatment of Alzheimer’s disease (AD) is targeting key points of neural circuits with DBS. Here, we explored the effects of DBS targeted to the entorhinal cortex (EC) on neurons in the hippocampal CA1 in a mouse model of preclinical AD. Specifically, we recorded field potential signals from CA1
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Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-01-04 Alba Diaz-Martinez, Rogelio Monfort-Ortiz, Yiyao Ye-Lin, Javier Garcia-Casado, Mar Nieto-Tous, Félix Nieto-Del-Amor, Vicente Diago-Almela, Gema Prats-Boluda
The prolonged latent phase of Induction of Labour (IOL) is associated with increased risks of maternal mortality and morbidity. Electrohysterography (EHG) has outperformed traditional clinical measures monitoring labour progress. Although parity is agreed to be of particular relevance to the success of IOL, no previous EHG‐related studies have been found in the literature. We thus aimed to identify
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Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2023-01-02 K.C. Pavithra, Preetham Kumar, M. Geetha, Sulatha V. Bhandary
Diabetic Macular Edema (DME) is a potentially blinding consequence of Diabetic Retinopathy (DR) as well as the leading cause of vision loss in diabetics. DME is characterized by a buildup of extracellular fluid inside the macula through hyperpermeable vessels. The presence of DME can be spotted at any level of DR with varying degrees of severity using prominent imaging modalities such as Color Fundus
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Automated malarial retinopathy detection using transfer learning and multi-camera retinal images Biocybern. Biomed. Eng. (IF 6.4) Pub Date : 2022-12-21 Aswathy Rajendra Kurup, Jeff Wigdahl, Jeremy Benson, Manel Martínez-Ramón, Peter Solíz, Vinayak Joshi
Cerebral malaria (CM) is a fatal syndrome found commonly in children less than 5 years old in Sub-saharan Africa and Asia. The retinal signs associated with CM are known as malarial retinopathy (MR), and they include highly specific retinal lesions such as whitening and hemorrhages. Detecting these lesions allows the detection of CM with high specificity. Up to 23% of CM, patients are over-diagnosed