Showing papers in "International Journal of Biomedical Engineering and Technology in 2014"
TL;DR: A survey on intelligent techniques applied to liver disorders between the years January 1995 and January 2013 identifies which ITs are applied for what types of liver disorders and on which types of disorders maximum works has been done.
Abstract: Liver disease is one of the leading causes of mortality in India, as it is in rest of the world. This paper presents a survey on intelligent techniques applied to liver disorders between the years January 1995 and January 2013. Individual ITs include artificial neural network (ANN), data mining (DM), fuzzy logic (FL) etc. Integrated ITs combine methods as artificial neural network-case-based reasoning (ANN-CBR), artificial immune system-artificial neural network-fuzzy logic (AIS-ANN-FL) etc. The different types of liver disorders covered in the study are hepatitis, liver fibrosis, liver cirrhosis, liver cancer, fatty liver, liver disorders data set, hepatitis data set and hepatobiliary disorders data set. The study identifies which ITs are applied for what types of liver disorders and on which types of disorders maximum works has been done. Another imperative fact emerging from this survey is that large part of the research work on liver disorders has been done from 2007 onwards.
27 citations
TL;DR: The novel insole measurement system and pressure sensor chips are utilised to obtain plantar and dorsal pressure of three types of sports shoes and maximal Lateral Metatarsophalangeal (LM) joint of TS is distinctively different from BS and RS.
Abstract: The purpose of the study is to probe into the difference in plantar pressure and dorsal pressure of three types of sports shoes: Basketball Shoes (BS); Running Shoes (RS); Tennis Shoes (TS). The novel insole measurement system and pressure sensor chips are utilised to obtain plantar and dorsal pressure. As to dorsal pressure, maximal Lateral Metatarsophalangeal (LM) joint of TS is distinctively different from BS and RS, mean LM of RS is apparently distinct from BS and TS. These distinctions might be of importance while designing shoes; factors like athletic performance, perceived comfort and injury prevention should be taken into consideration.
15 citations
TL;DR: A novel strategy for detecting Premature Ventricular Contraction (PVC) using a Swarm-based Support Vector Machine (SSVM), an SVM optimised by using Particle Swarm Optimisation (PSO).
Abstract: A novel strategy for detecting Premature Ventricular Contraction (PVC) is proposed and investigated. The strategy employs a Swarm-based Support Vector Machine (SSVM). An SSVM is an SVM optimised by using Particle Swarm Optimisation (PSO). The strategy proposes new inputs. The inputs involve the width and the gradient of the electrocardiographic QRS wave. Experiments with different inputs and different SVM kernel functions are conducted to find the best one for PVC detection. On a test using clinical data, SSVM performs well in PVC detection with sensitivity, specificity and accuracy of 98.94%, 99.99% and 99.46%, respectively.
14 citations
TL;DR: In this article, a comparative study of different Daubechies wavelets (db2-db14) for analysis of arm motions is presented, and it is inferred that wavelet db4 performs denoising the best among the wavelets and is suitable for accurate classification of surface electromyogram signal.
Abstract: In this study, wavelet analysis has been exercised to understand the quality of surface electromyogram signal for class separability. The surface electromyogram signals were estimated with the following steps. First, the obtained signal was decomposed using wavelet transform. The decomposed coefficients were then analysed with threshold methods. With the appropriate choice of wavelet, it is possible to remove interference noise effectively in order to analyse the signal. This paper presents a comparative study of different Daubechies wavelets (db2-db14) family for analysis of arm motions. From the analysed results, it was inferred that wavelet db4 performs denoising the best among the wavelets and is suitable for accurate classification of surface electromyogram signal. Further, one-way repeated factorial Analysis of Variance (ANOVA) statistical technique was also implemented to investigate the voluntary muscular contraction relationship for different arm movements.
14 citations
TL;DR: A new method is proposed for detection of microaneurysms from the colour fundus retinal images to assist the eye care specialist to examine large populations of patient.
Abstract: Diabetic retinopathy is the most common diabetic eye disease and causes blindness if not treated on time. Microaneurysms are one of the first clinical signs of diabetic retinopathy and appear as small red dots on fundus images. The incidence of blindness can be reduced by detecting microaneurysms at an earlier stage. In this paper, a new method is proposed for detection of microaneurysms from the colour fundus retinal images to assist the eye care specialist to examine large populations of patient. The microaneurysms are detected from the colour fundus image by applying the pre-processing techniques in order to remove the optic disc and similar blood vessels using morphological operations. The pre-processed image was then used for feature extraction, and these features were used for the purpose of classification. The classifiers used are support vector machine, Meta-cognitive Neural Network (McNN) and Self-adaptive Resource Allocation Network (SRAN), and their performances are compared and presented.
13 citations
TL;DR: This paper discusses the suitability of implementing Sugeno- and Mamdani-type FISs for heartbeat case determination based on the Electrocardiogram (ECG) signals, and found that the Sugeno’s system processing time is always less compared to Mamd...
Abstract: This paper discusses the suitability of implementing Sugeno- and Mamdani-type FISs for heartbeat case determination based on the Electrocardiogram (ECG) signals. The heartbeat cases are Normal Sinus Rhythm (NORM), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Ventricular Premature Contractions (VPC) and Atrial Premature Contractions (APC). Overall, fuzzy system with Sugeno-type FIS was developed based on the FL method used in a paper. System modifications were carried out to create an alternative system for the application, implemented using Mamdani-type FIS. Both systems were verified with 3000 sets of random data for systems’ performance comparison. Sugeno’s system sensitivities in determining each heartbeat case are 100%, which leads to a TCA value of 100%, whereas in Mamdani’s system, the sensitivities are all 100%, except for NORM heartbeat case which is 99.8% and thus TCA value is 99.9667%. It is also found that the Sugeno’s system processing time is always less compared to Mamd...
13 citations
TL;DR: A review work on computerised Cobb angle measurement from digital X-ray images based on the different popular state-of-the-art de-noising techniques and generates a more reliable angular measurement is presented.
Abstract: Scoliosis is a 3D deformity of the spine, which is characterised by both lateral curvature and vertebral rotation. Cobb angle measurement is the golden method for diagnosis of scoliosis. With the increasing trend of digital imaging-based diagnosis system, computerised Cobb angle measurement is gaining much popularity. In general, digital X-ray images are very sensitive to noise, so a good pre-processing step for noise removal is necessary to increase the accuracy and reduce the variability of computerised Cobb angle measurement method. In this paper, we have presented a review work on computerised Cobb angle measurement from digital X-ray images based on the different popular state-of-the-art de-noising techniques. This study helps in selecting the de-noising technique that best suits with different steps of the Cobb angle method and generates a more reliable angular measurement. This type of study based on de-noising technique for reducing Cobb angle measurement variability is the first of its kind. MATLAB2011Rb image processing toolbox was used for the simulation and verification of our proposed methodology.
13 citations
TL;DR: In this paper, the authors discuss mobile-based interventions for management of non-communicable diseases such as hypertension and Type 2 diabetes, especially in developing countries such as Bangladesh, where they build upon the Persuasive System Design (PSD) model for developing mobilebased multi-intervention services for the prevention and management of the above-mentioned diseases.
Abstract: This paper discusses mobile-based interventions for management of non-communicable diseases such as hypertension and Type 2 diabetes. It builds upon the Persuasive System Design (PSD) model for developing mobile-based multi-intervention services for the prevention and management of the above- mentioned diseases, especially in developing countries such as Bangladesh. Identified gaps include lack of studies on system framework or design component for behaviour change services; no studies on smartphone based multi-intervention services for behaviour change; no user acceptance studies on smartphone services for management. This paper uses (a) Hevner's model for identifying gaps in the existing literature and developing a framework to address some of the gaps (b) the PSD model and social cognitive theory for developing the framework and content for behaviour change service to manage hypertension and Type 2 diabetes respectively. This paper has potential design implications for a broader research in mobile-based PSD and multi-intervention service for future developments.
12 citations
TL;DR: In this article, an implantable Coplanar Waveguide (CPW) fed dipole antenna operates in the Industrial, Scientific Medical (ISM) band and Wireless Medical Telemetry Services (WMTS) band for biomedical applications.
Abstract: An implantable Coplanar Waveguide (CPW) fed dipole antenna operates in the Industrial, Scientific Medical (ISM) band and Wireless Medical Telemetry Services (WMTS) band for biomedical applications. The proposed implanted antenna is made compatible for implantation by embedding it in an alumina ceramic substrate. The proposed antenna was simulated using the method of moment's software IE3D by assuming the predetermined dielectric constant for the human body muscle, fat and skin tissues, and the parameters of an implantable antennas such as return loss, radiation pattern and Voltage Standing Wave Ratio (VSWR) with human body phantom liquid are plotted and it is verified with simulated results. The proposed antenna shows the lower return loss, perfect impedance matching and high gain as compared to other implanted antennas. The experiments permitted to identifying the most efficient tissue placements, and proposing some quantitative and general guidelines useful to characterise and design this kind of new system.
11 citations
TL;DR: A new improved classification technique using Fully Complex-Valued Relaxation Networks (FCRN) based ensemble technique for classifying mammogram images based on three stages of breast cancer, namely normal, benign and malignant defined by the MIAS database is presented.
Abstract: This paper presents a new improved classification technique using Fully Complex-Valued Relaxation Networks (FCRN) based ensemble technique for classifying mammogram images. The system is developed based on three stages of breast cancer, namely normal, benign and malignant defined by the MIAS database. Features like binary object features, RST invariant features, histogram features, texture features and spectral features are extracted from the MIAS database. Extracted features are then given to the proposed FCRN-based ensemble classifier. FCRN networks are ensembled together for improving the classification rate. Receiver Operating Characteristic (ROC) analysis is used for evaluating the system. The results illustrate the superior classification performance of the ensembled FCRN. The resultant ensembled FCRN approximates the desired output more accurately with a lower computational effort.
11 citations
TL;DR: In this paper, the analytical investigation of the Casson model for axisymmetric pulsatile blood flow through an inclined stenosed artery of a periodically accelerated body under the influence of a magnetic field is presented.
Abstract: This paper deals with the analytical investigation of Casson model for axisymmetric pulsatile blood flow through an inclined stenosed artery of a periodically accelerated body under the influence of a magnetic field. Invoking suitable transformations, the flow governing partial differential equations are non-dimensionalised. For these non-dimensionalised equations, an exact solution representing the different flow characteristic has been derived by employing the perturbation method. Plug flow radius, plug flow velocity, flow rate and impedance analysis of the Casson fluid have been done graphically by varying the yield stress, inclination of artery, body acceleration and pressure gradient. Some important results are obtained pertaining to the medical interest.
TL;DR: The technological developments in wireless sensor nodes for biomedical applications during the last decade are reviewed and some of the recent technologies that can be used to enhance the future WBAN systems are suggested.
Abstract: Using miniature wearable devices, it is possible to follow the health of individuals from outside the hospital in a convenient and easy way that does not restrict their movements or their daily activities. The wearable biomedical sensors or the Wireless Body Area Network (WBAN) systems is about to become an important tool to match the express way of life nowadays. This paper reviews the technological developments in wireless sensor nodes for biomedical applications during the last decade. The review covers the electronics hardware design, challenges, safety and ending by the standards required to implement those systems. The designer of the wearable medical sensor should be aware of the standards and the safety issues at the acquisition and storage level, wireless transmission protocol level, powering level, security and privacy level, and overall safety level. Finally, this paper suggests some of the recent technologies that can be used to enhance the future WBAN systems.
TL;DR: A general framework that is a combination of paradigms, in order to have a hybrid segmentation method, automatic and unsupervised, for MRI brain tumour segmentation is proposed.
Abstract: We present in this paper a method for MRI brain tumour segmentation, so we propose a general framework that is a combination of paradigms, in order to have a hybrid segmentation method, automatic and unsupervised. In the first phase, expertise and characteristics derived from MRI images are combined to define heuristics for the development of the classification approach. In the second phase, refinement of the tumour contour is achieved by using the region growing method. The results are good and visually validate by radiologists.
TL;DR: Hip anatomy, musculoskeletal model and FEA models are shown, forces acting on hip shown by various researchers are discussed and experimental studies are made.
Abstract: The purpose of the present paper is to provide an overview of the current biological and biomechanical knowledge on the hip. Various model formulations by researchers are discussed. Hip geometry is one of the crucial parts of human body movement. Researching on how various views of the hip is shown in different planes and how the forces act on the femur leads to learning of the various forces and torques acting on the hip joint. Hip anatomy, musculoskeletal model and FEA models are shown, forces acting on hip shown by various researchers are discussed and experimental studies are made. In addition, joint kinematics of the hip is also discussed to some extent.
TL;DR: An improved method for electrocardiogram (ECG) signal compression using Set Partitioning in Hierarchical Trees (SPIHT) algorithm that yields good compression with controlled quantity of signal degradation and requires computational time as compared to earlier published SPIHT algorithms.
Abstract: In this paper, an improved method for electrocardiogram (ECG) signal compression using Set Partitioning in Hierarchical Trees (SPIHT) algorithm is proposed. ECG signals are compressed based on different transform such as discrete cosine transform and discrete wavelet transform with modified SPIHT. The modified SPIHT algorithm yields good compression with controlled quantity of signal degradation and requires computational time as compared to earlier published SPIHT algorithms. The proposed algorithm is suitable for the ECG signal compression for telemedicine or e-health system due to minimum computational time.
TL;DR: Development of a symptom-based decision support system will help in effective diagnosis of asthma and accuracy of the system and value of kappa coefficient is computed and reported here.
Abstract: Asthma is a chronic lung disease caused due to shorten airway path of the patient. Development of a symptom-based decision support system will help in effective diagnosis, which is the focus of this paper. In this paper first phase is to diagnose asthma using data mining tools and in the second phase asthma control level is measured using fuzzy inference system. The diagnosis is based on the symptoms like sneezing, dry cough, sore throat etc. The asthma level of control is based on the symptoms like shortness of breath, limitation of activities, day time symptoms etc. Finally accuracy of the system and value of kappa coefficient is computed and reported here.
TL;DR: This paper reviews some of the applications of three new algorithms, i.e. biogeography-based optimisation, cuckoo search and bat algorithm, in various domains of biomedical engineering, and shows how these fields have benefitted from the use of these recently introduced MOA based on evolution.
Abstract: With the increasing complexity of real-world optimisation problems, researchers from various domains of engineering sciences are constantly looking for accurate, fast and robust optimisers. Over the past few decades, studies on Metaheuristic Optimisation Algorithms (MOA) have shown that these methods can be efficiently used to eliminate most of the difficulties of classical methods. These algorithms have inherent capability to explore a large region of the solution space, are computationally robust and efficient, and can avoid premature convergence. This paper reviews some of the applications of three new algorithms, i.e. biogeography-based optimisation, cuckoo search and bat algorithm, in various domains of biomedical engineering, namely clinical diagnosis, biomedical instrumentation, artificial neural networks, biomedical image processing, bioelectronics, biological control system and biomechanics, and show how these fields have benefitted from the use of these recently introduced MOA based on evolution...
TL;DR: A new detector paradigm based on synchronous detection, which is synchronised with cardiac rhythm, was compared to a gold standard and a standard energy detector and showed that it was possible to detect inaudible microemboli.
Abstract: We propose here a new detector paradigm based on synchronous detection. Unlike standard energy detectors, this kind of detector uses the pseudo-cyclostationarity properties of blood Doppler signals. The new detector, which is synchronised with cardiac rhythm, was compared to a gold standard and a standard energy detector. Detectors were evaluated according to clinical recordings from patients with carotid stenoses. The results showed that it was possible to detect inaudible microemboli. The detection rate was improved by 30% and the false alarm rate was below 2%.
TL;DR: The proposed work intends to classify four commonly occurring arrhythmia classes along with normal class to achieve overall accuracy of 99.29% for a total 82,978 beats.
Abstract: Cardiac arrhythmias stand a great admonish for human beings nowadays. The proposed work intends to classify four commonly occurring arrhythmia classes along with normal class. For each beat of 300 samples, both morphological and rhythmic features are determined. A total of 129 morphological features are formed by 114 wavelets coefficients and 15 independent components having 300 coefficients of basis functions obtained by using ICA. PCA is applied on the morphological features to derive the best 11 principal components and to this, four rhythmic features are combined to have a final 15 feature coefficients. SVM classifier gets trained using the 15 features of 30% beats of every class in the total number of beats. The remaining 70% of beats are used for evaluating the individual class performance. Finally the SVM classifier with only 15 features is able to produce the overall accuracy of 99.29% for a total 82,978 beats.
TL;DR: The failure analysis of the ACL ligament in the human knee is done taking into consideration the significance of the kneecap and the articular cartilage, and the various forces acting on the knee.
Abstract: This paper gives a clear idea of knee movement. The knee is a very complicated structure. Here we discuss about the failure analysis of the ACL ligament and the various forces acting on the knee. Hertz contact stress on knee joint is determined for three different human postures (standing, staircase ascent and descent), and the significance of the kneecap and the articular cartilage are considered. The failure analysis of the ACL ligament in the human knee is done taking into consideration all these factors.
TL;DR: An attempt has been made here to develop a computer-based dual channel acquisition system for online detection of HRV from real time simultaneously acquired ECG and carotid waveforms of human subjects.
Abstract: Measurement and analysis of Heart Rate Variability (HRV) is progressively gaining attention as it enables a clinical and non-invasive system to assess human physiology. QRS complex of an ECG forms the basis of HRV, but literature also establishes its relation with carotid artery pulsation. HRV analysis is either simulated or done offline using ECG database. An attempt has been made here to develop a computer-based dual channel acquisition system for online detection of HRV from real time simultaneously acquired ECG and carotid waveforms of human subjects. An algorithm is developed in MATLAB to acquire and filter both signals simultaneously. Algorithm to find online threshold-based maxima and difference between the adjacent RR peaks is also made and tested to calculate time domain HRV parameters. Close correlation is found in HRV data calculated from ECG and carotid signal. Carotid pulsation is easy to acquire using simple electronics and thus can be potentially used for HRV analysis compared to ECG acquisition. The measurement unit developed is simple and relatively cost effective.
TL;DR: In this article, a new approach to investigate a radial artery pulse signal rhythmic structure is considered based on a simultaneous analysis of a set of oscillatory components determined by parameters of various elements of unit oscillations.
Abstract: A new approach to investigate a radial artery pulse signal rhythmic structure is considered based on a simultaneous analysis of a set of oscillatory components determined by parameters of various elements of unit oscillations. Based on a comparative analysis of the spectral density types defined by different parameters of a pulse signal, an essential distinction between them has been revealed. An opportunity of increasing the number of informative features by means of simultaneous analysis of pulse signal rhythmic structure oscillatory component set is shown. The study has been carried out using experimental material obtained during children’s clinical examinations focused on detecting the initial stage arterial hypertension in infancy and adolescence. The informativeness of the pulse signal rhythmic structure parameters was estimated as applied to this task, showing that maximum informativeness is inherent to indicators defined by oscillatory components of the pulse signal dicrotic wave temporal parameter.
TL;DR: An intelligent method to accurately classify the heartbeat of ECG signals through the Artificial Metaplasticity Multilayer Perceptron (AMMLP) is proposed and obtained an excellent result compared to the classical MLP and recent classification techniques applied to the same database.
Abstract: Electrocardiogram (ECG) arrhythmias such as ventricular and atrial arrhythmias are one of the common causes of death. These abnormalities of heart activity may cause immediate death or damage to the heart. If the abnormal symptoms can be detected and diagnosed early, time is saved to prevent the occurrence of heart attack. Therefore, it is necessary to have an effective method for early detection and early treatment. We propose, in this paper, an intelligent method to accurately classify the heartbeat of ECG signals through the Artificial Metaplasticity Multilayer Perceptron (AMMLP). The MIT-BIH database is used to classify arrhythmias into three different types: Premature Ventricular Contraction (PVC), Right Bundle Branch Block (RBBB) and Left Bundle Branch Block (LBBB); normal ECG signals are also used in the study. The obtained AMMLP classification accuracy of 98.25% is an excellent result compared to the classical MLP and recent classification techniques applied to the same database.
TL;DR: The objective of this research is to develop an automatic medical diagnostic system which can be readily available to the common man, especially to those who cannot receive proper medical care.
Abstract: The objective of this research is to develop an automatic medical diagnostic system which can be readily available to the common man, especially to those who cannot receive proper medical care. The approach basically includes a combination of soft and hard inputs. Soft inputs include a variety of common symptoms such as fever, headache and cough. Each selected disease corresponds to a variety of general symptoms. Hard inputs include images of the tongue as it is widely used by the doctor to diagnose the various disorders. The analysis of hard inputs was divided into two phases, namely chromatic colour analysis and texture-based statistical analysis. Once the feature vectors are encoded from the hard and soft inputs, they are fed to a neural network for developing a classification model. Neural network is trained with four different algorithms and the performance is analysed.
TL;DR: In this article, a 3D finite element model was constructed by incorporating implant's anatomical configuration, and the effects of radial clearance, head radius, cup thickness, shell thickness, and interfacial friction on contact pressure at articulating interfaces and von Mises stress within the cup were investigated.
Abstract: This paper aims to predict the contact mechanics with the effect of critical geometric parameters and bearing materials of a dual mobility implant. A 3D finite element model was constructed by incorporating implant’s anatomical configuration. Effects of radial clearance, head radius, cup thickness, shell thickness, and interfacial friction on contact pressure at articulating interfaces and von Mises stress within the cup were investigated. Influence of head material on the contact was studied as well. Simulations showed that contact pressure and stress decreased with the decrease in radial clearance and the increase in head radius, while the cup and shell thicknesses revealed a negligible influence. The contact was found to be insensitive to the head material. The contact pressure at the primary interface ruled out the overall contact of the implant. Results were discussed with studies available in literature, providing a guideline to further improve the design of the implants.
TL;DR: Experimental results show that the coding performance can be significantly improved by the hybrid DWT-DCT algorithm.
Abstract: In this research, we introduce an idea for Retinographic medical colour image compression, this new technique hybrid between Discrete Wavelet Transform (DWT) and Discrete Cosinus Transform (DCT). In recent times, developing hybrid schemes for effective image compression has gained enormous popularity among researchers. This research paper presents a proposed scheme for medical image compression based on hybrid compression technique (DWT and DCT). The goal is to achieve higher compression rates by applying different compression thresholds for the wavelet coefficients of each DWT band (LL and HH) while DCT transform is applied on LL bands with preserving the quality of reconstructed medical image. The retained coefficients are quantised by using adaptive quantisation according to the type of transformation. Finally the entropy coding (variable shift coding) is used to encode the quantisation indices. Experimental results show that the coding performance can be significantly improved by the hybrid DWT-DCT algorithm.
TL;DR: In this work, subcortical regions of autism spectrum disorder are analysed using fuzzy Gaussian distribution model-based distance regularised multi-phase level set method in autistic MR brain images and it is found the segmented autistic subcorts have reduced area and are statistically significant.
Abstract: In this work, subcortical regions of autism spectrum disorder are analysed using fuzzy Gaussian distribution model-based distance regularised multi-phase level set method in autistic MR brain images. The fuzzy Gaussian distribution model is used as the intensity discriminator. The segmented images are validated with the ground truth using geometrical measure area. The results show that the fuzzy Gaussian distribution model-based multi-phase level set method is able to extract the subcortical tissue boundaries. The subcortical regions segmented using this method gives high correlation with ground truth. The corpus callosum area gives very high (R = 0.94) correlation. The brain stem and cerebellum present high correlations of 0.89 and 0.84, respectively. Also, it is found the segmented autistic subcortical regions have reduced area and are statistically significant (p < 0.0001). The ratio metric analysis proves the relation in reduction of the area in subcortical regions with total brain area.
TL;DR: Details of a simple and robust mechanism for online acquisition and real time processing of EEG signals in MATLAB without any loss of information, using custom developed API are given.
Abstract: Commercially available EEG acquisition units provide ability to acquire and view EEG signals in real time using their proprietary user interface software. Analysis of such EEG data is limited to the capabilities provided within these acquisition units, thereby making it necessary to process data in multi-paradigm computing environment. Offline uploading of the data into standard tools for further analysis may cause loss of information. This paper gives details of a simple and robust mechanism for online acquisition and real time processing of EEG signals in MATLAB without any loss of information, using custom developed API. Resulting wave decomposition into discrete samples and channels also reduce complexity in processing data. The designed system, using portable wireless Emotiv EEG neuroheadset, can easily be adopted for web-based remote monitoring of live EEG for applications in the field of mobile health. Moreover, developed BCI can be miniaturised and designed as SoC (System on Chip).
TL;DR: It is observed that even for higher Compression Ratio (CR) fidelity can be maintained and is verified by Cross-Correlation Coefficient (CCC) and the performance parameters can support the technique of data compression.
Abstract: Compression of digital Electrocardiogram (ECG) signals is desirable for two reasons: economic use of storage space for databases and reduction of the data for transmission on telephone lines. This paper deals with waveletbased compression method. This method of ECG data compression leads to substantial amount of ECG reduction with less amount of the data loss. The wavelet functions can be used to decompose the ECG signal and upon reconstruction, the signal can be presented without loss of signal morphology. The analysis and synthesis filters play a very important role in this process. The analysis filter decomposes the signal using a pair of low-pass and high-pass filters, whereas the synthesis filter reconstructs the decomposed part. There is a faithful reconstruction on applying the synthesis filter to the ECG signal which is acceptable to the cardiologists. The performance parameters can support the technique of data compression. It is observed that even for higher Compression Ratio (CR) fidelity can be maintained and is verified by Cross-Correlation Coefficient (CCC).
TL;DR: In this paper, the authors used Fibre Bragg Grating (FBG) sensors for plantar pressure monitoring during walking, which are embedded within arc shaped layers of carbon composite material.
Abstract: This paper presents Fibre Bragg Grating (FBG) sensors for plantar pressure monitoring during walking. For sensors fabrication, the FBGs are embedded within arc shaped layers of carbon composite material. This arc shape embedding technique shows average pressure sensitivity and resolution of 1.3 pm/kPa and 0.8 kPa, respectively. Two such sensors are attached at the sole of a ready to wear shoe at the locations of forefoot and hind foot. Pressure is monitored during various walking speeds. Higher rate of increase in the pressure is observed at the forefoot area as compared to heel when walking speed is increased. The result shows the feasibility of FBG-based compact sensor system for plantar pressure monitoring.