HeartSearcher: finds patients with similar arrhythmias based on heartbeat classification

Long‐term electrocardiogram data can be acquired by linking a Holter monitor to a mobile phone. However, most systems of this variety are simply designed to detect arrhythmia through heartbeat classification, and do not provide any additional support for clinical decisions. HeartSearcher identifies patients with similar arrhythmias from heartbeat classifications, by summarising each patient''s typical heartbeat pattern in the form of a regular expression, and then ranking patients according to the similarities of their patterns. Results obtained using electrocardiogram data from the MIT‐BIH arrhythmia database show that this abstraction reduces the volume of heartbeat classifications by 98% on average, offering great potential to support clinical decisions.Inspec keywords: electrocardiography, medical information systems, medical signal detection, medical signal processing, signal classification, pattern matching, diseases, patient monitoring, decision making, data mining, sorting, abstractingOther keywords: clinical decision support, heartbeat classification volume reduction, abstraction, MIT‐BIH arrhythmia database, heartbeat pattern similarity ranking, patient ranking, regular expression, patient typical heartbeat pattern summarisation, similar arrhythmia patient identification, arrhythmia detection, mobile phone, Holter monitor, long‐term electrocardiogram data acquisition, similar arrhythmia patient search, HeartSearcher     

Sparse electrocardiogram signals recovery based on solving a row echelon‐like form of system

The study of biology and medicine in a noise environment is an evolving direction in biological data analysis. Among these studies, analysis of electrocardiogram (ECG) signals in a noise environment is a challenging direction in personalized medicine. Due to its periodic characteristic, ECG signal can be roughly regarded as sparse biomedical signals. This study proposes a two‐stage recovery algorithm for sparse biomedical signals in time domain. In the first stage, the concentration subspaces are found in advance. Then by exploiting these subspaces, the mixing matrix is estimated accurately. In the second stage, based on the number of active sources at each time point, the time points are divided into different layers. Next, by constructing some transformation matrices, these time points form a row echelon‐like system. After that, the sources at each layer can be solved out explicitly by corresponding matrix operations. It is noting that all these operations are conducted under a weak sparse condition that the number of active sources is less than the number of observations. Experimental results show that the proposed method has a better performance for sparse ECG signal recovery problem.Inspec keywords: electrocardiography, matrix algebra, medical signal processingOther keywords: sparse electrocardiogram signal recovery, row echelon‐like form of system, noise environment, biological data analysis, personalised medicine, dictionary learning algorithm, transformation matrices, sparse biomedical signal recovery     

Fabrication of conductive human bio‐nanoelectrode from graphene oxide modified with polyvinyl alcohol

It is time for electrodes prepared from graphene oxide (GO) to replace the traditional electrodes. However, GO is an electrically insulating material. However, in this study, a conductive electrode was prepared from GO modification with glycerol (GL) under the esterification reaction at 90°C for 3 h with sulphuric acid as a catalyst under vacuum conditions. Polyvinyl alcohol (PVA) acts as a polymer host. It was mixed with GO and modification was carried out under heating conditions. The mixture of the GO/GL/PVA nanocomposite was rapidly cooled and poured into the electrode mould. Finally, it is placed in a desiccator at room temperature for two days. The characterisation (Fourier transform infrared spectroscopy, X‐ray diffraction, and scanning electron microscopy) proved that the ester bond was formed and a complete distribution of GO/GL into the matrix of PVA was verified. The GO/GL/PVA nanocomposite was tested for electrocardiogram (ECG) electrodes. The biopic instrument was used to compare the behaviour of the GO/GL/PVA plastic electrode and the commercial one. The results indicated that the GO/GL/PVA plastic electrode efficiently detected ECG signals after two months with high conductivity and lower noise than the commercial electrode. The GO/GL/PVA plastic electrode has been reported for the first time in the literature.Inspec keywords: catalysts, scanning electron microscopy, filled polymers, nanofabrication, X‐ray diffraction, moulding, nanocomposites, graphene compounds, Fourier transform infrared spectra, nanomedicine, biomedical electrodes, electrocardiography, electrical conductivity, medical signal detection, bonds (chemical)Other keywords: graphene oxide, polyvinyl alcohol, electrode mould, electrocardiogram electrodes, conductive human bionanoelectrode, electrically insulating material, GO‐GL‐PVA nanocomposite, GO‐GL‐PVA plastic electrode, esterification reaction, sulphuric acid, catalyst, vacuum conditions, polymer host, heating conditions, desiccator, glycerol, Fourier transform infrared spectroscopy, X‐ray diffraction, scanning electron microscopy, ester bond, biopic instrument, ECG signal detection, electrical conductivity, temperature 90.0 degC, time 3.0 hour, temperature 293 K to 298 K, time 2 day, CO     

Glucose monitoring system using nanopellets

The combination of the fields of software engineering, gadgets, and science has stood out among the most revolutionary future innovations. Health issues have been the focus of various engaging and explanatory studies. One such health‐related dilemma is diabetes. Diabetes at its serious stage results in impaired vision. Increase in the glucose level is a critical parameter that could result in hyperglycaemia, hypoglycaemia, massive heart attack, strokes, and aneurysms. Monitoring the glucose level in blood is one of the control measures for diabetes in the affected population. A glucose monitoring framework interminably measures and screens the glucose level in blood. A novel framework for measuring the glucose level is proposed in this study. This study employs nanopellets that evaluate the glucose level. When the glucose level increases or decreases, it is continuously recorded and displayed using a microcontroller (mixed signal processor (MSP) 430). The data are then sent to the physician through global system for mobile communication. The typical blood glucose level of human being ranges from 70 to 110 mg/dl. When the insulin level builds up to certain point, hyperglycaemia occurs. When decreases, hypoglycaemia occurs. Hyperglycaemia leads to cataracts, oedema, hypertension, polyuria, and polydipsia. Hypoglycaemia causes perplexity, energy, insensateness, coma, and death.Inspec keywords: sugar, patient monitoring, nanomedicine, diseases, microcontrollers, blood, biomedical equipment, medical control systems, biomedical communicationOther keywords: mobile communication, coma, polydipsia, polyuria, hypertension, oedema, cataracts, insulin level, blood glucose level, microcontroller, glucose level monitoring system, aneurysms, strokes, hypoglycaemia, massive heart attack, hyperglycaemia, impaired vision, software engineering, nanopellets     

Signal amplification strategy using gold/N ‐trimethyl chitosan/iron oxide magnetic composite nanoparticles as a tracer tag for high‐sensitive electrochemical detection

This study presents a novel signal amplification method for high‐sensitive electrochemical immunosensing. Gold (Au)/N ‐trimethyl chitosan (TMC)/iron oxide (Fe₃ O₄) (shell/shell/core) nanocomposite was used as a tracing tag to label antibody. The tag was shown to be capable of amplifying the recognition signal by high‐density assembly of Au nanoparticles (NPs) on TMC/Fe₃ O₄ particles. The remarkable conductivity of AuNPs provides a feasible pathway for electron transfer. The method was found to be simple, reliable and capable of high‐sensitive detection of human serum albumin as a model, down to 0.2 pg/ml in the range of 0.25–1000 pg/ml. Findings of the present study would create new opportunities for sensitive and rapid detection of various analytes.Inspec keywords: gold, filled polymers, conducting polymers, iron compounds, magnetic particles, nanoparticles, nanocomposites, nanosensors, electrochemical sensors, proteins, molecular biophysics, biomagnetism, biosensorsOther keywords: signal amplification strategy, gold‐N‐trimethyl chitosan‐iron oxide magnetic composite nanoparticles, tracer tag, high‐sensitive electrochemical detection, high‐sensitive electrochemical immunosensing, antibody, high‐density assembly, AuNP conductivity, electron transfer, human serum albumin, FeO‐Au     

SPA enhanced FPIA‐based detection of pesticide residue with ppb/ppt level detection limit

Pesticide residue in fruits & vegetables is one of the key issues affecting the export of rural products in India. Pesticide exposure or intake causes major nervous system problems in children. The solutions to quantitate them in field are rare and the pesticide residue detection in the parts per billion (ppb) ranges is challenging. Except ELISA, none of the existing methods can detect pesticide residues in ppb range in the field. We employed a new approach of concentrating field samples and used sodium polyacrylate (SPA) as water absorbing material. The SPA beads concentrate the field samples and obtained a sub ppb range detection using an existing FPIA system and could improve overall sensitivity by 10‐100 fold. The developed assay can be done in few seconds. We have used three pesticides 2,4‐D, atrazine and methyl parathion with 0.1, 0.5 and 3 ppb detection limit respectively. We developed a simple field ready FPIA device and used sodium poly acrylate (SPA) in this biochemical FPIA to enhance sensitivity. Our tests with spiked field samples offers a possibility of using SPA concentration assisted FPIA in field. This study will have far reaching applications of both qualitative & quantitative analysis chemical analytes in field samples.Inspec keywords: agrochemicals, organic compounds, enzymesOther keywords: SPA enhanced FPIA‐based detection, ppb‐level detection limit, rural product export, India, pesticide exposure, Pesticide intake, nervous system problems, pesticide residue detection, parts‐per billion ranges, enzyme‐linked immunosorbent assay, ELISA, sodium polyacrylate, water absorbing material, SPA beads, fluorescence polarisation immunoassay system, overall sensitivity improvement, antigen‐antibody pairs, 2,4‐dichlorophenoxyacetic acid, atrazine, methyl parathion, biochemical FPIA, interstitial spaces, bulk medium, qualitative analysis, quantitative analysis, ppt‐level detection limit     

Blood glucose concentration control for type 1 diabetic patients: a non‐linear suboptimal approach

In this study, a closed‐loop treatment strategy is proposed for the control of blood glucose levels in type 1 diabetic patients. Toward this end, a non‐linear technique for designing suboptimal tracking controllers, called the state‐dependent Riccati equation tracker, is used based on a mathematical model of the glucose–insulin regulatory system. Since two state variables of the utilised model are not available to the controller, a non‐linear filter is also designed to estimate these variables using the measured blood glucose concentration. Effects of unannounced meals and regular exercise are investigated for a nominal patient and nine diabetic patients with unknown parameters. Numerical simulations are given to show the effectiveness of the proposed treatment strategy even in the presence of parametric uncertainties and the observation noise.Inspec keywords: blood, biochemistry, diseases, patient treatment, Riccati equations, nonlinear filters, medical signal processingOther keywords: blood glucose concentration control, type 1 diabetic patients, nonlinear suboptimal approach, closed‐loop treatment strategy, suboptimal tracking controllers, state‐dependent Riccati equation tracker, glucose‐insulin regulatory system, nonlinear filter, unannounced meals, regular exercise, treatment strategy, parametric uncertainties, observation noise     

Small RNA‐mediated switch‐like regulation in bacterial quorum sensing

Quorum sensing (QS) is a signalling mechanism by which bacteria produce, release and then detect and respond to changes in their density and biosignals called autoinducers (AIs). There are multiple feedback loops in the QS system of Vibrio harveyi. However, how these feedback loops function to control signal processing remains unclear. In this study, the authors present a computational model for the switch‐like regulation of signal transduction by small regulatory RNA‐mediated QS based on intertwined network involving AIs, LuxO, LuxU, Qrr sRNAs and LuxR. In agreement with experimental observations, the model suggests that different feedbacks play critical roles in the switch‐like regulation. The authors results reveal that V. harveyi uses multiple feedbacks to precisely control signal transduction.Inspec keywords: biocommunications, biocontrol, biology computing, cellular biophysics, physiological models, RNAOther keywords: RNA‐mediated switch‐like regulation, bacterial quorum sensing, signaling mechanism, autoinducers, Vibrio harveyi, feedback loops function, signal processing control, switch‐like regulation     

Effect of external periodic signals and electromagnetic radiation on autaptic regulation of neuronal firing

An improved Hindmarsh–Rose (HR) neuron model, where the memristor is a bridge between membrane potential and magnetic flux, can be used to investigate the effect of periodic signals on autaptic regulation of neurons under electromagnetic radiation. Based on the improved HR model driven by periodic high–low‐frequency current and electromagnetic radiation, the responses of electrical autaptic regulation with diverse high–low‐frequency signals are investigated using bifurcation analysis. It is found that the electrical modes of neurons are determined by the selecting parameters of both periodic high and low‐frequency current and electromagnetic radiation, and the Hamiltonian energy depends on the neuronal firing modes. The effects of Gaussian white noise on the membrane potential are discussed using numerical simulations. It is demonstrated that external high–low‐frequency stimulus plays a significant role in the autaptic regulation of neural firing mode, and the electrical mode of neurons can be affected by the angular frequency of both high–low‐frequency forcing current and electromagnetic radiation. The mechanism of neuronal firing regulated by high–low‐frequency signal and electromagnetic radiation discussed here could be applied to research neuronal networks and synchronisation modes.Inspec keywords: bioelectric potentials, neural nets, bifurcation, synchronisation, memristors, neurophysiology, numerical analysis, white noiseOther keywords: synchronisation modes, external periodic signals, electromagnetic radiation, improved Hindmarsh–Rose neuron model, membrane potential, periodic high–low‐frequency current, electrical autaptic regulation, diverse high–low‐frequency signals, neuronal firing modes, external high–low‐frequency stimulus, high–low‐frequency forcing current, high–low‐frequency signal, research neuronal networks     

Nano‐biosensor based on reduced graphene oxide and gold nanoparticles,for detection of phenylketonuria‐associated DNA mutation

Phenylketonuria (PKU)‐associated DNA mutation in newborn children can be harmful to his health and early detection is the best way to inhibit consequences. A novel electrochemical nano‐biosensor was developed for PKU detection, based on signal amplification using nanomaterials, e.g. gold nanoparticles (AuNPs) decorated on the reduced graphene oxide sheet on the screen‐printed carbon electrode. The fabrication steps were checked by field emission scanning electron microscope imaging as well as cyclic voltammetry analysis. The specific alkanethiol single‐stranded DNA probes were attached by self‐assembly methodology on the AuNPs surface and Oracet blue was used as an intercalating electrochemical label. The results showed the detection limit of 21.3 fM and the dynamic range of 80–1200 fM. Moreover, the selectivity results represented a great specificity of the nano‐biosensor for its specific target DNA oligo versus other non‐specific sequences. The real sample simulation was performed successfully with almost no difference than a synthetic buffer solution environment.Inspec keywords: biosensors, nanosensors, nanoparticles, graphene compounds, gold, nanomedicine, DNA, molecular biophysics, biomedical equipment, electrochemical sensors, electrochemical electrodes, field emission scanning electron microscopy, voltammetry (chemical analysis), self‐assembly, biochemistryOther keywords: reduced graphene oxide, gold nanoparticles, phenylketonuria‐associated DNA mutation, newborn children, electrochemical nanobiosensor, signal amplification, nanomaterials, reduced graphene oxide sheet, screen‐printed carbon electrode, field emission scanning electron microscopy imaging, cyclic voltammetry, alkanethiol single‐stranded DNA probes, self‐assembly methodology, Oracet blue, intercalating electrochemical label, Au‐CO     

Synthesising gene clock with toggle switch and oscillator

The usefulness of a genetic clock lies in its role to stimulate a sequence of logic reactions for sequential biological circuits. A clock signal is a periodic square wave, its amplitude alternates at a steady frequency between fixed minimal and maximal levels. Transition between the minimum and the maximum is instantaneous for an ideal square wave; however, the function is unrealisable in physical bio‐systems. This research develops a new genetic clock generator based on a genetic oscillator, in which, a sine wave generator is adopted as a signal oscillator. It is shown that combination of a genetic oscillator with a toggle switch is able to generate clock signals forming an efficient way to generate a near square wave. In silico study confirms the proposed idea.Inspec keywords: genetics, oscillators, biological techniques, square‐wave generators, switchesOther keywords: toggle switch, genetic clock, logic reaction sequence, sequential biological circuits, clock signal, periodic square wave, physical biosystem, genetic clock generator, sine wave generator, signal oscillator, genetic oscillator     

Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control‐based approach

Here, a direct adaptive control strategy with parametric compensation is adopted for an uncertain non‐linear model representing blood glucose regulation in type 1 diabetes mellitus patients. The uncertain parameters of the model are updated by appropriate design of adaptation laws using the Lyapunov method. The closed‐loop response of the plasma glucose concentration as well as external insulin infusion rate is analysed for a wide range of variation of the model parameters through extensive simulation studies. The result indicates that the proposed adaptive control scheme avoids severe hypoglycaemia and gives satisfactory performance under parametric uncertainty highlighting its ability to address the issue of inter‐patient variability.Inspec keywords: patient monitoring, adaptive control, diseases, Lyapunov methods, closed loop systems, medical control systems, patient treatment, medical computing, sugar, uncertain systems, blood, nonlinear control systems, physiological modelsOther keywords: blood glucose regulation, type 1 diabetic patients, adaptive parametric compensation control‐based approach, direct adaptive control strategy, nonlinear model, type 1 diabetes mellitus patients, uncertain parameters, appropriate design, adaptation laws, closed‐loop response, plasma glucose concentration, external insulin infusion rate, model parameters, adaptive control scheme, parametric uncertainty, inter‐patient variability     

Optimal neuro‐fuzzy control of hepatitis C virus integrated by genetic algorithm

Hepatitis C blood born virus is a major cause of liver disease that more than three per cent of people in the world is dealing with, and the spread of hepatitis C virus (HCV) infection in different populations is one of the most important issues in epidemiology. In the present study, a new intelligent controller is developed and tested to control the hepatitis C infection in the population which the authors refer to as an optimal adaptive neuro‐fuzzy controller. To design the controller, some data is required for training the employed adaptive neuro‐fuzzy inference system (ANFIS) which is selected by the genetic algorithm. Using this algorithm, the best control signal for each state condition is chosen in order to minimise an objective function. Then, the prepared data is utilised to build and train the Takagi–Sugeno fuzzy structure of the ANFIS and this structure is used as the controller. Simulation results show that there is a significant decrease in the number of acute‐infected individuals by employing the proposed control method in comparison with the case of no intervention. Moreover, the authors proposed method improves the value of the objective function by 19% compared with the ordinary optimal control methods used previously for HCV epidemic.Inspec keywords: epidemics, diseases, blood, medical computing, microorganisms, genetic algorithms, fuzzy control, neurocontrollers, adaptive control, medical control systemsOther keywords: genetic algorithm, hepatitis C blood born virus, liver disease, hepatitis C virus infection, epidemiology, intelligent controller, optimal adaptive neuro‐fuzzy controller, adaptive neuro‐fuzzy inference system, ANFIS, genetic algorithm, control signal, state condition, objective function minimisation, Takagi‐Sugeno fuzzy structure, acute‐infected individuals, ordinary optimal control methods, HCV epidemic     

Controllability and observability analysis of basal ganglia model and feedback linearisation control

Deep brain stimulation (DBS) is a clinical remedy to control tremor in Parkinson''s disease. In DBS, one of the two main areas of basal ganglia (BG) is stimulated. This stimulation is produced with no feedback of the tremor and often causes a wide range of unpleasant side effects. Using a feedback signal from tremor, the stimulatory signal can be reduced or terminated to avoid extra stimulation and as a result decrease the side effects. To design a closed‐loop controller for the non‐linear BG model, a complete study of controllability and observability of this system is presented in this study. This study shows that the BG model is controllable and observable. The authors also propose the idea of stimulating the two BG areas simultaneously. A two‐part controller is then designed: a feedback linearisation controller for subthalamic nucleus stimulation and a partial state feedback controller for globus pallidus internal stimulation. The controllers are designed to decrease three indicators: the hand tremor, the level of delivered stimulation signal in disease condition, and the ratio of the level of delivered stimulation signal in health condition to disease condition. Considering these three indicators, the simulation results show satisfactory performance.Inspec keywords: feedback, brain, neurophysiology, diseases, medical control systems, closed loop systems, controllers, linearisation techniques, bioelectric phenomenaOther keywords: controllability analysis, observability analysis, basal ganglia model, feedback linearisation control, deep brain stimulation, clinical remedy, tremor control, Parkinson''s disease, feedback signal, closed‐loop controller, nonlinear BG model, feedback linearisation controller, two‐part controller, subthalamic nucleus stimulation, partial state feedback controller, globus pallidus internal stimulation, disease condition, delivered stimulation signal     

Improving adaptive receivers performance in molecular communication via diffusion

Forthcoming applications for molecular communications (MC) such as drug‐delivery and health monitoring will require robust receiver capabilities to mitigate channel memory and inter symbol interference caused by previous transmitted symbols. Here, the authors introduce an adaptive weighted algorithm to reduce the influence of these factors. This novel signal detection is deployed on to a concentration‐based MC system with absorbing receiver which is based on the so‐called first passage time concept. The proposed detector has low complexity and does not require explicit channel knowledge. To evaluate authors’ proposed algorithm, a theoretical approach is developed to derive the bit error rate (BER). Numerical results also carried out to verify the accuracy of these formulations and establish that the new detector will achieve better performance in comparison with other common low‐complex detectors under certain scenarios. Additionally, the authors propose a simple pre‐coding technique to combat the sequence of consecutive ones in low ISI scenarios. Also a comparison between detectors is given, which is based on the variation of distance, symbol period, signal‐to‐noise ratio (SNR), and number of molecules.Inspec keywords: radio receivers, error statistics, signal detection, intersymbol interferenceOther keywords: low‐complex detectors, low ISI scenarios, symbol period, signal‐to‐noise ratio, adaptive receivers performance, molecular communication, drug‐delivery, health monitoring, robust receiver capabilities, channel memory, inter symbol interference, previous transmitted symbols, adaptive weighted algorithm, novel signal detection, concentration‐based MC system, absorbing receiver, passage time concept, low complexity, explicit channel knowledge, authors, theoretical approach, bit error rate, numerical results     

Nanotechnological approaches for the development of herbal drugs in treatment of diabetes mellitus – a critical review

Diabetes mellitus (DM) is a chronic illness that requires continuing medical care and patient self‐management education to prevent acute complications and to reduce the risk of long‐term complications. The number of people with diabetes is increasing due to population growth, ageing, urbanisation and increasing prevalence of obesity and physical inactivity. Apart from currently available therapeutic options, many herbal medicines have been recommended for the treatment of diabetes. Herbal drugs are prescribed widely because of their effectiveness, less side effects and relatively low cost. Several pharmacopoeias have provided parameters to maintain quality and standardise procedures in identification/authentication of herbal inputs and their products. Available literature related to folklore medicine used in the treatment of diabetes extended to nanoformulation of herbal drugs up to date was cited. The use of bioactive compounds leads to new hope to improve the life expectancy and health status of the population for the formulation of novel drugs. Recently, many studies have shown that nanotechnology has the potential to be used in different biological and medical applications, mainly as targeted drug delivery systems to minimise and delay the chronic effects of diabetes. Herein, the authors presented a thorough review of the available herbal medicines and the possibilities of developing their nanoformulations in the treatment of DM.Inspec keywords: drugs, drug delivery systems, nanomedicine, reviews, diseases, patient careOther keywords: nanotechnological approaches, herbal drugs, diabetes mellitus treatment, critical review, chronic illness, medical care, patient self‐management education, long‐term complications, risk reduction, ageing, urbanisation, obesity, physical inactivity, pharmacopoeias, standardise procedures, herbal inputs identification‐authentication, folklore medicine, bioactive compounds, health status, biological applications, medical applications, targeted drug delivery systems, chronic effects, herbal medicines     

Deploying swarm intelligence in medical imaging identifying metastasis,micro‐calcifications and brain image segmentation

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X‐ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X‐rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence‐learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration‐dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.Inspec keywords: swarm intelligence, image segmentation, brain, neurophysiology, medical image processing, biomedical MRI, computerised tomography, diagnostic radiography, bone, diseases, learning (artificial intelligence), particle swarm optimisation, iterative methods, tumours, medical disordersOther keywords: medical imaging identifying metastasis, microcalcifications, umbrella deployment, stochastic diffusion, metastasis identification, bone scans, mammographs, CT imaging, aorta, nasogastric tube, chest X‐ray, hybrid swarm intelligence‐learning vector quantisation approach, magnetic resonance brain image segmentation, particle swarm optimisation, iteration‐dependent nature, tumour regions, abnormal MR brain imaging