International Journal of Biomedical Engineering and Technology (17 papers in press)

Regular Issues

• Skull Part Relationships and Shape Prediction Toward the Missing Part Completion  
  by Tan-Nhu Nguyen, Ngoc-Bich Le, Xuan-Hien Quach-Nguyen, Thi-Hiep Nguyen, Van-Toi Vo, Tien-Tuan Dao 
  Abstract: Accurate cranial reconstruction needs need clear relation among skull parts due to the asymmetry of the skull structures. Consequently, this study investigated the relation among skull parts for enhancing the skull missing part prediction. The relationship was trained from three-dimensional skull shapes reconstructed from 329 head-and-neck computed tomography images. We automatically defined the skull parts throughout all skull shapes. The skull parts were parameterised using the principal component analysis (PCA). Skull part relations were trained through their PCA-based shape parameters. The output skull parts could be predicted from the input skull parts with the trained shape relation with good and acceptable accuracy in cranial reconstruction. The best and worst mean errors were 1.32 mm and 2.54 mm when the number of missing skull parts was one and ten, respectively. The investigated procedure was employed in a computer-aided system for automatically predicting and printing skull missing parts directly in 3D spaces.
  Keywords: skull part relationship; skull shape prediction; statistical shape modelling; skull part fixing; cranial reconstruction.
  DOI: 10.1504/IJBET.2024.10065355
   
  
• Detection of Acute Lymphoblastic Leukemia Using Extreme Learning Machine based on Deep Features from Microscopic Blood Cell Images  
  by Sunita Chand 
  Abstract: Leukaemia is the medical term for blood cancer. This paper proposes an automatic disease diagnosis model to detect leukaemia from microscopic blood cell images by classifying these images into malignant and benign cells. It uses extreme learning machine (ELM) as the classifier and uses the transfer learning on AlexNet to obtain the 4,096 features required to train the classifier. The training of AlexNet is performed on 864 and 2,080 images, obtained after augmentation. The experiments are repeated five times each for nine different values of number of hidden neurons in the hidden layer of the classifier, to obtain nine average accuracies. The best average accuracy obtained for IDB1 is 99.4% at 3,000 and 4,500 hidden neurons, while for IDB2, it is 99.8% at 3,500 hidden neurons. The grand average is calculated over these nine averages and is found to be 98.6% and 99.2% for IDB1 and IDB2 respectively, while obtaining best accuracy as 100% for both the datasets.
  Keywords: Extreme Learning Machine; Deep Neural Network; Feature Extraction; AlexNet; Transfer Learning; Image Augmentation.
  DOI: 10.1504/IJBET.2024.10065360
   
  
• Design of a Bionic Arm using EMG Signal Processing and Artificial Intelligence  
  by Olusola Kunle Akinde, Oreoluwa V. Akanbi, Oluseyi A. Adeyemi 
  Abstract: Bionic arm, its development for those who are disadvantaged is the focus of this work. Electrocardiogram (ECG) was utilised in the work for the reception of brain electromyography (EMG) signal functions to control the arm which is later deciphered using digital signal processing (DSP) through empirical mathematical equations to distinguish the EMG signals from different finger movements giving more room for recognising more complex finger movements rather than the commonly used method for transcription of EMG signals. Furthermore, utilising computer vision with python, another functional mode of operation was implemented for technical testing of the arm, controlled through the receptive movements of human fingers read from a personal computer (PC) camera. The architecture employed in this work generally improves the response time of the bionic arm through reduction in time needed to read and translate EMG signals and error calculations before primary usage by the amputees to prevent issues during usage.
  Keywords: Arduino; electromyogram; EMG; digital signal processing; DSP; bionic arm; OpenCV; electrocardiogram; ECG.
  DOI: 10.1504/IJBET.2024.10065936
   
  
• Comparative Biomechanical Analysis of Lumbar spine in Trainees with Varied Barbell Positions: A Finite Element Study  
  by Diwei Chen, Datao Xu, Huiyu Zhou, Xuanzhen Cen, Yang Song, Dong Sun 
  Abstract: Considering various training goals, barbell squat variations like high bar back squat (HBBS) and front barbell squat (FBS) are popular. However, it's crucial to understand their differing effects on lumbar spine biomechanics. The objective is to comprehensively investigate the biomechanical mechanisms of lumbar during squatting movements. Data from 16 participants underwent analysis using musculoskeletal and finite element models. In the FBS, the lumbar vertebral joints exhibit a significantly greater extension angle compared to HBBS. Additionally, significant disparities in extension moment of the lumbar vertebral joints are observed between HBBS and FBS during the 0-26% and 34-77% phases of the movement. Moreover, Von Mises stress values on both the vertebrae and intervertebral discs are lower than those experienced in HBBS. The research results indicate that, with a focus on lumbar spine protection, FBS can effectively reduce the load on the lumbar spine under comparable conditions.
  Keywords: finite element model; lumber spine; squat exercise; lower back pain; kinetics; training injuries.
  DOI: 10.1504/IJBET.2024.10065991
   
  
• Wavelet-Based Methodology for Non-Invasive Detection and Multiclass Classification of Voice Disorders: A Comprehensive Study across Multilingual Datasets  
  by Avinash Shrivas, Shrinivas Deshpande, Girish Gidaye 
  Abstract: Impaired voice function affects 1.2% of the global population and is often diagnosed through invasive procedures. Past efforts in automated voice disorder detection mainly tackled the binary "healthy vs. unhealthy" classification. In this study, we suggest a non-invasive alternative based on speech analysis, diverging from the conventional invasive surgical methods. Both binary and multiclass classification is carried out in the present work by decomposing the speech signal extracted from German, Spanish, English, and Arabic datasets using discrete wavelet transform (DWT). The impact of varying decomposition levels on detection and classification accuracy is evident, with the fifth level of decomposition demonstrating the highest recognition rate of 90% to 99% for tasks involving voice disorder identification and multiclass classification. Results indicate that energy and statistical features derived from DWT offer richer information on pathological voices. Consequently, the proposed system could serve as a valuable adjunct for clinical diagnosis of laryngeal pathologies.
  Keywords: Voice disorder; wavelet transform; statistical features; multiclass classification.
  DOI: 10.1504/IJBET.2024.10066255
   
  
• Synergistic Augmentation of EtOH and 4-Watt Ultraviolet-C for Rapid Surface Decontamination  
  by Jahanzeb Sheikh, Tan Tian Swee, Syafiqah Saidin, Chua Lee Suan, Sameen Malik 
  Abstract: Bacterial contamination poses significant health risks, especially in densely populated settings like educational institutions. This study in a Malaysian educational institute examined bacterial deposition on frequently touched surfaces and evaluated the efficacy of 70% ethanol (EtOH) with 10-s of contact time, combined with ultraviolet (UV) light irradiation under varied time exposures durations. Results showed that EtOH-only treatment was least effective on lift-2, with a 20% inactivation rate, while other surfaces revealed efficiencies between 69.19% and 84.4%. However, employment of EtOH-UV treatment achieved highest inactivation across all the samples treated within 60-s requiring 0.15 mJ/cm2 of dose. However, swab obtained from lift-1 could sustain 1.41-log10 inactivation under maximum exposure settings. Scanning electron microscopy (SEM) further validated the persistence of Bacillus spp, Staphylococcus spp, and E. coli colonies. This study underscores the need for comprehensive disinfection strategies in educational facilities to reduce bacterial contamination, highlighting the enhanced efficacy of the combined EtOH-UV treatment.
  Keywords: bacteria; decontamination; disinfectants; environment; high-touch surface; low-touch surface; pathogens; Ultraviolet-C.
  DOI: 10.1504/IJBET.2024.10066427
   
  
• Cardiac Disorder Classification: An Efficient Novel Deep Kronecker Neural Network with Sand Cat Swarm Optimisation Algorithm for Feature Selection  
  by Meghavathu S.S. Nayak, Hussain Syed 
  Abstract: Diagnosing a disease takes time and requires highly technical methods. These days, predicting and diagnosing cardiovascular disease (CVD) is crucial to lowering the death rate and catching them in early stages. Prior research employed machine learning (ML) techniques for disease prediction; however, adequate attention should have been paid to feature identification through appropriate methods for selecting features. This research introduced a novel deep learning (DL)-based deep Kronecker neural network (DKNN) for CVD classification. Essential features are extracted using the DenseNet-201 approach, and feature selection techniques help highlight the most important traits while reducing diagnosis costs. Therefore, the Sand Cat Swarm Optimization (SCSO) method is used to identify the most relevant features for diagnosing heart disease. Furthermore, the imbalanced data problem is resolved, and overfitting is decreased through cycle generative adversarial network (CGAN) based data augmentation. Differentiating from other approaches, the proposed approach obtains above 99% accuracy, precision, recall, and F1-score.
  Keywords: cardiovascular disease; CVD; deep Kronecker neural network; DKNN; DenseNet-201; sand cat swarm optimisation; SCSO; cycle generative adversarial network; CGAN.
  DOI: 10.1504/IJBET.2024.10066762
   
  
• Investigation of Heat Transfer in Trocar System for Minimal Invasive Surgery  
  by Wassim Salameh, Ali Cherry, Bassam Hussein, Ali Shaito, Rawan Mrad, Mohamad Haj-Hassan 
  Abstract: Laparoscopic surgery has transformed surgical procedures with its minimally invasive approach, involving small incisions and quicker patient recovery. Surgeons use different techniques to access deep tissues within the body. One common step in laparoscopic surgery is the insertion of a trocar system through one of these incisions. This allows for the introduction of instruments and the insufflation of the abdominal cavity with heated carbon dioxide to maintain a suitable operating environment. Here, we report a parametric study investigating the behavior of different materials utilized in trocars in response to thermal exposure. The results obtained indicate that polycarbonate, acrylic, polyetherimide and polysulfone are favorable materials based on small CO2 temperature difference between inlet and outlet of the trocar due to heat loss to surrounding medium. The study provides insights for appropriate selection of materials for a trocar system.
  Keywords: minimal invasive surgery; trocar; fluid flow; heat transfer.
  DOI: 10.1504/IJBET.2024.10066904
   
  
• Automated Breast Cancer Segmentation and Classification in Mammogram Images Using Deep Learning Approach  
  by Dhanalaxmi B., Venkatesh N., Yeligeti Raju, G. Jagan Naik, Channapragada Rama Seshagiri Rao, V.Prema Tulasi 
  Abstract: One of the most prevalent cancers among women is breast cancer. The mortality rate of this cancer may be lowered with an early diagnosis. In the literature, a wide range of AI-based techniques have been proposed. Nevertheless, they face several difficulties, including inadequate training models, irrelevant feature extraction, and similarities between cancerous and non-cancerous regions. Therefore, we propose a novel improved deep learning based model for the segmentation and classification of breast cancer in this research. An enhanced UNet++ (EUNet++) model is used to segment the affected part of the lesion region. The improved ResNext (IResNext) model classifies mammogram images into benign and malignant classes. The findings showed that the suggested framework outperformed other models trained on the same dataset, achieving an exceptional 99.56% classification accuracy for the CBIS-DDSM dataset and 99.64% for the INbreast dataset.
  Keywords: Breast cancer; Deep learning; Enhanced UNet++; Improved ResNext; and data augmentation. The ResNet50V2 model extracts high-level statistical and texture data.
  DOI: 10.1504/IJBET.2024.10067021
   
  
• Design, Optimisation and Pharmacodynamic Evaluation of Thymoquinone Nanosponges for the Treatment of Rheumatoid Arthritis  
  by Zuha Rahiqa, Preeti Karwa, Ayesha Syed, Ansari A.L.I. Ansari 
  Abstract: The seeds of the Nigella sativa (NS) plant contain a significant amount of bioactive substance called thymoquinone (TQ). It is non-toxic and has numerous potential uses in the treatment of human illnesses, such as cancer, diabetes. Since it is hydrophobic in nature it has limited medication solubility and lessens the negative effects of hepatic, gastrointestinal, rheumatoid, and asthma conditions. The goal of the current study is to optimise the solvent emulsion method for preparing TQ loaded nanosponge (TQ-NS) gel by employing the fundamentals of design of experiments. Particle size and entrapment efficiency (EE%) critical parameters were assessed using a hybrid design technique consisting of Mini Run Resolution IV design and Box-Behnken design. The improved TQ-NS was added to 1% w/w Carbopol gel along with an equivalent amount of TQ. The particle size, PDI, zeta potential, and EE% of improved TQ-NS formulations were 254.1
  Keywords: Thymoquinone; Rheumatoid Arthritis; Quality by Design; Box Behnken design; Nanosponges.
  DOI: 10.1504/IJBET.2024.10067231
   
  
• Speech Signals as Biomarkers: using Glottal Features for Non-Invasive COVID-19 Testing  
  by Girish Gidaye, Abhay Barage, Nirmayee Dighe, Kadria Ezzine, Varsha Turkar, Gajanan Nagare 
  Abstract: The COVID-19 pandemic was the most significant global health crisis in recent history, with lasting impacts on societies worldwide. Current screening methods are invasive, slow, frequently inaccurate, and limited in capacity. To overcome these limitations researchers used conventional features extracted from speech signals. In the proposed methodology, the change in vibratory pattern due to COVID-19 is captured by extracting glottal features from glottal signal acquired by inverse filtering. Various machine learning models like na
  Keywords: COVID-19; Glottal signal; Glottal features; Speech signal; Machine learning; quasi-closed phase.
  DOI: 10.1504/IJBET.2024.10067292
   
  
• Evaluation of Weight-Bearing, Walking Stability, and Gait Symmetry in Patients undergoing Restoration following Hip joint Fractures  
  by Anam Raza, Imran Mahmood, Tayyaba Sultana 
  Abstract: This study numerically quantifies patients' restoration following a range of hip joint fractures. Ground reaction force (GRF) data collected from 221 subjects was grouped into four conditions: hip coxa fracture (HC), pelvis fracture (HP), femur fracture (HF), and normal hip joint (NH). The GRF data were windowed into three subphases: initial double-limb support, single-limb support, and terminal double-limb support. During each subphase, the thresholds of mass normalised GRF were calculated for both fractured and intact limbs. The results showed a significant decline (p < 0.001) in walking stability and weight-bearing ability for all hip fractures. Furthermore, the fractured patients showed a massive increase in interlimb weight-bearing dependency (up to 20%) in the vertical direction in comparison to normal subjects, and a significant decrease in interlimb symmetries (up to 28%) in the anterior-posterior (AP) and medial-lateral (ML) directions. The methods and findings provide a comprehensive package to evaluate fracture restoration clinically using 3D-GRF.
  Keywords: fracture; weight-bearing; stability; gait; rehabilitation.
  DOI: 10.1504/IJBET.2024.10067401
   
  
• Analysis of Fractal Dimension of Segmented Blood Vessels in Fundus Images Using U-Net Architecture  
  by Saranya M, Sunitha K.A, Sridhar P.Arjunan 
  Abstract: Precise segmentation of retinal blood vessels (RBVs) is pivotal in ophthalmology research, aiding in detecting diverse retinal abnormalities. This study proposes a contrast-limited adaptive histogram equalisation (CLAHE) technique to improve retinal image quality and visibility of microvascular structures. We aimed to determine the complexity of blood vessels using fractal dimensions (FD) and compare different metrics for their effectiveness. We employed the UNet architecture to separate blood vessels, and our results on the DRIVE retinal fundus image standard dataset showed an impressive accuracy rate of 97.24%, surpassing traditional filtering methods. Box counting, information, capacity, correlation, and probability dimensions are used in the FD analysis to help us understand the complex and irregular structures of retinal blood vessels. These metrics are valuable for detecting and monitoring retinal diseases in clinical settings. Our comparison with other techniques reveals promising results, particularly in the capacity and information dimensions, with statistical significance (P < 0.05). The potential of fractal dimensions as a screening tool for diabetic retinopathy underscores their importance in epidemiological studies.
  Keywords: blood vessels; fundus image; DRIVE dataset; filter techniques; U-Net architecture; fractal dimension; diabetic retinopathy; statistical analysis; deep learning.
  DOI: 10.1504/IJBET.2024.10067439
   
  
• A Systems Pharmacology based ADME Profiling and Molecular Docking Analysis Unveils the Potential Role of Baicalein as the Natural Drug Candidate for Gallbladder Cancer  
  by Aakansha Singh, Anjana Dwivedi 
  Abstract: Phytochemicals show promising anti-cancer properties with minimal side-effects, offering an alternative to conventional chemotherapy. This work employs systems pharmacology and molecular docking techniques to examine Baicalein, Cirsimaritin, Hispidulin, Kaempferol, and Sinensetin as therapeutic candidates for gallbladder cancer (GBC). Using Swiss Target Prediction and public databases, 100 potential flavonoid targets and 880 GBCrelated DEGs were identified. Common genes were identified using Venny 2.1.0. GO and KEGG pathway analysis (p-value <0.05) revealed their involvement in carbon metabolism and FoxO pathways. A PPI network (confidence score >0.40) constructed with STRING, identified six hub genes with connectivity degree 4. Expression dysregulation was confirmed by GEPIA2 (p-value <0.01, Log2FC >1). Molecular docking analysis using Autodock Vina reported stronger binding affinities of Baicalein for EGFR, MMP9, and TERT, and similar affinities for CCNB1 and MET, when compared to Gefitinib. This study brings forth baicalein as a possible natural alternative for GBC treatment.
  Keywords: Gallbladder cancer; flavonoids; network pharmacology; hub genes; molecular docking; bioinformatics.
  DOI: 10.1504/IJBET.2024.10067441
   
  
• Modifying the Power Spectrum of the LPC Model within Kalman Filtering for Speech Enhancement  
  by Tarek Mellahi, Adil Bouhous, Rachid Hamdi 
  Abstract: We focused on enhancing speech improvement algorithms by addressing the challenge of extracting high-quality LPC parameters from noisy speech. The Kalman filter is a widely used algorithm in speech enhancement, and we aim to improve it by modifying the power spectrum parameters through a new approach called the modified power spectrum method within the LPC model for the Kalman Filter algorithm (MPS-LPC-KF). We evaluated our method using the NOIZEUS corpus and found it outperformed other existing methods. We are excited to see that our research has the potential to advance speech enhancement algorithms and ultimately improve communication in noisy environments.
  Keywords: Kalman filtering; speech enhancement; modified power spectrum method.
  DOI: 10.1504/IJBET.2024.10067443
   
  
• Prediction of Type 2 Diabetes based on Feature Augmentation and Morlet Wavelet Assisted Deep Learning Network with FFT Overlap and Add Convolution  
  by Hitesh B. Patel, Keyur Brahmbhatt 
  Abstract: The enormous development in the technology makes a diagnosis easier in the medical field using various existing approaches. Even though, the approach possesses some disadvantages like lowering disease treatment costs; research showed that network features, which are important in decision making but have a low accuracy value, were critical. A new deep-learning technique for diabetes detection is proposed in this research that resolves the challenges in the existing. The approach combines adversarial variational auto-encoder (AVAE) for data/feature augmentation with a Morlet wavelet-assisted deep learning network featuring fast Fourier transform (FFT) overlap and add convolution (MW-FFT-OAconv) to enhance classification accuracy. A novel optimiser, the weighted mean of vectors (WMOV), is introduced to acquire the weight parameters of the MW-FFT-OAconv network. Experimental results evaluated using statistical measures such as accuracy, F1-Score, precision, true negative rate (TNR), true positive rate (TPR), classifier error percentage (CEP), and Mathew coefficient correlation (MCC), demonstrate the effectiveness of the proposed approach. Compared to previous machine learning prediction models (random foest, naive Bayes, and decision tree), the proposed technique achieves an accuracy level of 98.44% in predicting type 2 diabetes.
  Keywords: Adversarial Variational Auto-Encoder (AVAE); Morlet Wavelet assisted deep learning network with FFT Overlap and Add convolution (MW-FFT-OAconv); weIghted meaN oF vectOrs (WMOV); classifier error perce.
  DOI: 10.1504/IJBET.2024.10067777
   
  
• Effects of Atherosclerotic Plaque Characteristics on Haemodynamics during Interventional Robot Diagnosis and Treatment  
  by Haoyu Xia, Zongming Zhu, Suqiang Ji, Liang Liang 
  Abstract: Atherosclerosis alters blood flow dynamics, yet the interaction between blood flow and vascular deformation with interventional devices remains underexplored. This study investigates the impact of plaque morphology and interventional robot positioning on blood flow characteristics. A bidirectional fluid-structure interaction (FSI) model was applied to calculate flow characteristics under varying plaque heights and shoulder widths. Particle image velocimetry (PIV) and a magnetically controlled microrobot system measured pulsatile flow fields in an experimental setup. Results show that plaque geometry and robot position significantly affect blood flow streamlines, pressure distribution, vascular deformation, and wall shear stress. When the robot moved from a distance of 25 mm to 5 mm from the plaque, the above-mentioned haemodynamic parameters increased by 11.41%, 5.51%, 9.28%, and 147%, respectively. The close alignment between simulations and experimental data confirms the accuracy of the method. This research enhances understanding of haemodynamic changes during interventional procedures and informs future clinical applications.
  Keywords: atherosclerosis; haemodynamics; fluid-structure interaction; FSI; interventional robot; particle image velocimetry; PIV.
  DOI: 10.1504/IJBET.2024.10067787