CardSegNet: An adaptive hybrid CNN-vision transformer model for heart region segmentation in cardiac MRI.

Cardiovascular MRI (CMRI) is a non-invasive imaging technique adopted for assessing the blood circulatory system's structure and function. Precise image segmentation is required to measure cardiac parameters and diagnose abnormalities through CMRI data. Because of anatomical heterogeneity and image variations, cardiac image segmentation is a challenging task. Quantification of cardiac parameters requires high-performance segmentation of the left ventricle (LV), right ventricle (RV), and left ventricle myocardium from the background. The first proposed solution here is to manually segment the regions, which is a time-consuming and error-prone procedure. In this context, many semi- or fully automatic solutions have been proposed recently, among which deep learning-based methods have revealed high performance in segmenting regions in CMRI data. In this study, a self-adaptive multi attention (SMA) module is introduced to adaptively leverage multiple attention mechanisms for better segmentation. The convolutional-based position and channel attention mechanisms with a patch tokenization-based vision transformer (ViT)-based attention mechanism in a hybrid and end-to-end manner are integrated into the SMA. The CNN- and ViT-based attentions mine the short- and long-range dependencies for more precise segmentation. The SMA module is applied in an encoder-decoder structure with a ResNet50 backbone named CardSegNet. Furthermore, a deep supervision method with multi-loss functions is introduced to the CardSegNet optimizer to reduce overfitting and enhance the model's performance. The proposed model is validated on the ACDC2017 (n=100), M&Ms (n=321), and a local dataset (n=22) using the 10-fold cross-validation method with promising segmentation results, demonstrating its outperformance versus its counterparts.

Automated Camera Lucida Method with Colored Images through Integration of Hardware and Software in Microscopic Zooming.

Automating the camera Lucida method which is a standard way for focusing microscopic images is a very challenging study for many scientists. Hence, actually combining hardware and software to automate microscopic imaging systems is one of the most important issues in the field of medicine as well. This idea reduces scanning time and increases the accuracy of user's results in this field. Closed-loop control system has been designed and implemented in the hardware part to move the stage in predefined limits of 15°. This system produces 50 consecutive images from parasites at the mentioned spatial distances in two directions of the z-axis. Then, by introducing our proposed relational software with combining images, a high-contrast image can be presented. This colored image is focused on many subparts of the sample even with different ruggedness. After implementing the closed-loop controller, stages movement was repeated eight times with an average step displacement of 20 µm which were measured in two directions of the z-axis by a digital micrometer. On average, the movement's error was 1 µm. In software, the edge intensity energy index has been calculated for image quality evaluation. The standard camera Lucida method has been simulated with acceptable results based on experts' opinions and also mean squared error parameters. Mechanical movement in stage has an accuracy of about 95% which will meet the expectations of laboratory user. Although output-focused colored images from our combining software can be replaced by the traditional fully accepted Camera Lucida method.

An insight into fluorescence and magnetic resonance bioimaging using a multifunctional polyethyleneimine-passivated gadocarbon dots nanoconstruct assembled with AS1411.

A nanoassembly of PEI-passivated Gd@CDs, a type of aptamer, is presented which was designed and characterized in order to target specific cancer cells based on their recognition of the receptor nucleolin (NCL), which is overexpressed on the cell membrane of breast cancer cells for fluorescence and magnetic resonance imaging and treatment. Using hydrothermal methods, Gd-doped nanostructures were synthesized, then modified by a two-step chemical procedure for subsequent applications: the passivating of Gd@CDs with branched polyethyleneimine (PEI) (to form Gd@CDs-PEI1 and Gd@CDs-PEI2), and using AS1411 aptamer (AS) as a DNA-targeted molecule (to generate AS/Gd@CDs-PEI1 and AS/Gd@CDs-PEI2). Consequently, these nanoassemblies were constructed as a result of electrostatic interactions between cationic Gd@CDs-passivated PEI and AS aptamers, offering efficient multimodal targeting nanoassemblies for cancer cell detection. It has been demonstrated through in vitro studies that both types of AS-conjugated nanoassemblies are highly biocompatible, have high cellular uptake efficiency (equivalent concentration of AS: 0.25 µΜ), and enable targeted fluorescence imaging in nucleolin-positive MCF7 and MDA-MB-231 cancer cells compared to MCF10-A normal cells. Importantly, the as-prepared Gd@CDs, Gd@CDs-PEI1, and Gd@CDs-PEI2 exhibit higher longitudinal relaxivity values (r1) compared with the commercial Gd-DTPA, equal to 5.212, 7.488, and 5.667 mM-1s-1, respectively. Accordingly, it is concluded that the prepared nanoassemblies have the potential to become excellent candidates for cancer targeting and fluorescence/MR imaging agents, which can be applied to cancer imaging and personalized nanomedicine.

Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake-Sleep Classification.

Many studies in the field of sleep have focused on connectivity and coherence. Still, the nonstationary nature of electroencephalography (EEG) makes many of the previous methods unsuitable for automatic sleep detection. Time-frequency representations and high-order spectra are applied to nonstationary signal analysis and nonlinearity investigation, respectively. Therefore, combining wavelet and bispectrum, wavelet-based bi-phase (Wbiph) was proposed and used as a novel feature for sleep-wake classification. The results of the statistical analysis with emphasis on the importance of the gamma rhythm in sleep detection show that the Wbiph is more potent than coherence in the wake-sleep classification. The Wbiph has not been used in sleep studies before. However, the results and inherent advantages, such as the use of wavelet and bispectrum in its definition, suggest it as an excellent alternative to coherence. In the next part of this paper, a convolutional neural network (CNN) classifier was applied for the sleep-wake classification by Wbiph. The classification accuracy was 97.17% in nonLOSO and 95.48% in LOSO cross-validation, which is the best among previous studies on sleep-wake classification.

A Multichannel Intraluminal Impedance Gastroesophageal Reflux Characterization Algorithm Based On Sparse Representation.

Gastroesophageal reflux disease (GERD) is a common digestive disorder with troublesome symptoms that has been affected millions of people worldwide. Multichannel Intraluminal Impedance-pH (MII-pH) monitoring is a recently developed technique, which is currently considered as the gold standard for the diagnosis of GERD. In this paper, we address the problem of characterizing gastroesophageal reflux events in MII signals. A GER detection algorithm has been developed based on the sparse representation of local segments. Two dictionaries are trained using the online dictionary learning approach from the distal impedance data of selected patches of GER and no specific patterns intervals. A classifier is then designed based on the lp-norm of dictionary approximations. Next, a preliminary permutation mask is obtained from the classification results of patches, which is then used in post-processing procedure to investigate the exact timings of GERs at all impedance sites. Our algorithm was tested on 33 MII episodes, resulting a sensitivity of 96.97% and a positive predictive value of 94.12%.

Automated Drawing Tube (Camera Lucida) Method in Light Microscopy Images Analysis Can Comes True.

BACKGROUND: In a light microscope, image acquisition with different component depths is difficult, and there are various approaches for solving this problem. One of the common approaches is Camera Lucida (CL). This method has some disadvantages such as time-consuming, handed problems in painting, causing user boring, and produce gray scale output images. AIMS AND OBJECTIVES: In this study, we purposed a novel-combined hardware and software method. In this article, we try to present an automated method for our designed microscope. MATERIALS AND METHODS: We have done a project with designed code number 377,694 to design and implement an upgraded light microscope. That project was about automatic movement of a stage with closed-looped control of a servomotor. Furthermore, automated camera catches images in predefined positions. That project has acceptable results in different parts, which encourage us to work on this study. This study help specialist have good fixative of all components in a sample. It is about trying to have useful Lucida Camera (drawing tube) in an automated scheme. RESULTS: This method is an acceptable usual way for microscopic specialists, but with some disadvantages. It is time-consuming and boring that effect on the accuracy of results. Hence, how can be good if automated similar method could be implemented is exciting and affective. This studies idea comes from the basis of manual drawing tube (CL) method. In this experimental study, we have taken 400 handed an image of microorganisms. Captured images are from its whole body or various organs. They have been captured in different z-axis positions of stage, and hence components with different depths could be focused. Each patch checked for its edge strength to choose highest resolutions sub image and reconstruct focused image like a puzzle. This process has been continued for all areas to merge and complete reconstructed image as output. CONCLUSION: Comparing edge strength with other images and mean square error with manual focused on confirm our method with pleasure outcomes. Furthermore, independent focusing of an internal component in a sample body has been surveyed. It helps to have better resolution in internal selected component for more analysis and replace in its primitive image. This article presents efficient consequences with good accuracy and saving time in process period, which could be useful in different microscopes types and various samples type.

On the Bioactivity and Mechanical Properties of Gehlenite Nanobioceramic: A Comparative Study.

BACKGROUND: For a new biomaterial which is going to be applied in bone tissue regeneration, bioactivity (bone bonding ability) and desirable mechanical properties are very essential parameters to take into consideration. In the present study, the gehlenite's mechanical properties and bioactivity are assessed and compared with hydroxyapatite (HA) for bone tissue regeneration. METHOD: Gehlenite and HA nanoparticles are synthesized through sol-gel method and coprecipitation technique, respectively, and their physical and chemical properties are characterized through X-ray diffraction, Fourier transform infrared spectroscopy, and transmission electron microscopy. RESULTS: The results prove that the gehlenite and HA phases without any undesirable phase are obtained, and the particles of both compounds are in the nanometer range with spherical morphology. The compressive strength of both compounds are assessed, and the values for gehlenite and HA disks are 144 ± 5 and 150 ± 4.8 MPa, respectively. Next, their bioactivity potential is assessed into simulated body fluid (SBF) up to 21 days, and the results show that after 14 days, gehlenite disk's surface is completely covered with newly formed Ca-P particles. However, some sporadic precipitations after 21 days soaking into SBF are formed onto the HA disk's surface. CONCLUSION: This comparative study shows that nanostructured gehlenite disk with desirable mechanical properties and faster bioactivity kinetic than HA can be considered as a promising bioceramic for bone tissue regeneration.

NF-RCNN: Heart localization and right ventricle wall motion abnormality detection in cardiac MRI.

Convolutional neural networks (CNNs) are extensively used in cardiac image analysis. However, heart localization has become a prerequisite to these networks since it decreases the size of input images. Accordingly, recent CNNs benefit from deeper architectures in gaining abstract semantic information. In the present study, a deep learning-based method was developed for heart localization in cardiac MR images. Further, Network in Network (NIN) was used as the region proposal network (RPN) of the faster R-CNN, and then NIN Faster-RCNN (NF-RCNN) was proposed. NIN architecture is formed based on "MLPCONV" layer, a combination of convolutional network and multilayer perceptron (MLP). Therefore, it could deal with the complicated structures of MR images. Furthermore, two sets of cardiac MRI dataset were used to evaluate the network, and all the evaluation metrics indicated an absolute superiority of the proposed network over all related networks. In addition, FROC curve, precision-recall (PR) analysis, and mean localization error were employed to evaluate the proposed network. In brief, the results included an AUC value of 0.98 for FROC curve, a mean average precision of 0.96 for precision-recall curve, and a mean localization error of 6.17 mm. Moreover, a deep learning-based approach for the right ventricle wall motion analysis (WMA) was performed on the first dataset and the effect of the heart localization on this algorithm was studied. The results revealed that NF-RCNN increased the speed and decreased the required memory significantly.

A hybrid graph-based approach for right ventricle segmentation in cardiac MRI by long axis information transition.

Right ventricle segmentation is a challenging task in cardiac image analysis due to its complex anatomy and huge shape variations. In this paper, we proposed a semi-automatic approach by incorporating the right ventricle region and shape information into livewire framework and using one slice segmentation result for the segmentation of adjacent slices. The region term is created using our previously proposed region growing algorithm combined with the SUSAN edge detector while the shape prior is obtained by forming a signed distance function (SDF) from a set of binary masks of the right ventricle and applying PCA on them. Short axis slices are divided into two groups: primary and secondary slices. A primary slice is segmented by the proposed modified livewire and the livewire seeds are transited to a pre-processed version of upper and lower slices (secondary) to find new seed positions in these slices. The shortest path algorithm is applied on each pair of seeds for segmentation. This method is applied on 48 MR patients (from MICCAI'12 Right Ventricle Segmentation Challenge) and yielded an average Dice Metric of 0.937 ±â€¯0.58 and the Hausdorff Distance of 5.16 ±â€¯2.88 mm for endocardium segmentation. The correlation with the ground truth contours were measured as 0.99, 0.98, and 0.93 for EDV, ESV and EF respectively. The qualitative and quantitative results declare that the proposed method outperforms the state-of-the-art methods that uses the same dataset and the cardiac global functional parameters are calculated robustly by the proposed method.

The design and validation of a hybrid digital-signal-processing plug-in for traditional cochlear implant speech processors.

BACKGROUND AND OBJECTIVE: Cochlear implants (CIs) are electronic devices restoring partial hearing to deaf individuals with profound hearing loss. In this paper, a new plug-in for traditional IIR filter-banks (FBs) is presented for cochlear implants based on wavelet neural networks (WNNs). Having provided such a plug-in for commercially available CIs, it is possible not only to use available hardware in the market but also to optimize their performance compared with the-state-of-the-art. METHODS: An online database of Dutch diphone perception was used in our study. The weights of the WNNs were tuned using particle swarm optimization (PSO) on a training set (speech-shaped noise (SSN) of 2 dB SNR), while its performance was assessed on a test set in terms of objective and composite measures in the hold-out validation framework. The cost function was defined based on the combination of mean square error (MSE), short­time objective intelligibility (STOI) criteria on the training set. Variety of performance indices were used including segmental signal- to -noise ratio (SNRseg), MSE, STOI, log-likelihood ratio (LLR), weighted spectral slope (WSS), and composite measures Csig,Cbak and Covl. Meanwhile, the following CI speech processing techniques were used for comparison: traditional FBs, dual resonance nonlinear (DRNL) and simple dual path nonlinear (SPDN) models. RESULTS: The average SNRseg, MSE, and LLR values for the WNN in the entire data set were 2.496 ±â€¯2.794, 0.086 ±â€¯0.025 and 2.323 ±â€¯0.281, respectively. The proposed method significantly improved MSE, SNR, SNRseg, LLR, Csig Cbak and Covl compared with the other three methods (repeated-measures analysis of variance (ANOVA); P < 0.05). The average running time of the proposed algorithm (written in Matlab R2013a) on the training and test sets for each consonant or vowel on an Intel dual-core 2.10 GHz CPU with 2GB of RAM was 9.91 ±â€¯0.87 (s) and 0.19 ±â€¯0.01 (s), respectively. CONCLUSIONS: The proposed algorithm is accurate and precise and is thus a promising new plug-in for traditional CIs. Although the tuned algorithm is relatively fast, it is necessary to use efficient vectorized implementations for real-time CI speech signal processing.

Designing an Inverter-based Operational Transconductance Amplifier-capacitor Filter with Low Power Consumption for Biomedical Applications.

The operational transconductance amplifier-capacitor (OTA-C) filter is one of the best structures for implementing continuous-time filters. It is particularly important to design a universal OTA-C filter capable of generating the desired filter response via a single structure, thus reducing the filter circuit power consumption as well as noise and the occupied space on the electronic chip. In this study, an inverter-based universal OTA-C filter with very low power consumption and acceptable noise was designed with applications in bioelectric and biomedical equipment for recording biomedical signals. The very low power consumption of the proposed filter was achieved through introducing bias in subthreshold MOSFET transistors. The proposed filter is also capable of simultaneously receiving favorable low-, band-, and high-pass filter responses. The performance of the proposed filter was simulated and analyzed via HSPICE software (level 49) and 180 nm complementary metal-oxide-semiconductor technology. The rate of power consumption and noise obtained from simulations are 7.1 nW and 10.18 nA, respectively, so this filter has reduced noise as well as power consumption. The proposed universal OTA-C filter was designed based on the minimum number of transconductance blocks and an inverter circuit by three transconductance blocks (OTA).

A novel feature ranking method for prediction of cancer stages using proteomics data.

Proteomic analysis of cancers' stages has provided new opportunities for the development of novel, highly sensitive diagnostic tools which helps early detection of cancer. This paper introduces a new feature ranking approach called FRMT. FRMT is based on the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS) which select the most discriminative proteins from proteomics data for cancer staging. In this approach, outcomes of 10 feature selection techniques were combined by TOPSIS method, to select the final discriminative proteins from seven different proteomic databases of protein expression profiles. In the proposed workflow, feature selection methods and protein expressions have been considered as criteria and alternatives in TOPSIS, respectively. The proposed method is tested on seven various classifier models in a 10-fold cross validation procedure that repeated 30 times on the seven cancer datasets. The obtained results proved the higher stability and superior classification performance of method in comparison with other methods, and it is less sensitive to the applied classifier. Moreover, the final introduced proteins are informative and have the potential for application in the real medical practice.

Prediction of myocardial infarction by assessing regional cardiac wall in CMR images through active mesh modeling.

Myocardial infarction is a leading cause of morbidity and mortality. In this study, using Cine MRI images, the infarct region was precisely determined by examining the local migration path length of critical points on myocardium borders and the fractional thickening effects. First, MRI Cine images of Epi/Endocardium were processed in 3D for all slices, and then incorporated in all frames to build a dynamic model. Epi/Endocardium images were segmented using Heiberg algorithm, and then by a robust restricted block matching algorithm, the sparse points were tracked. Finally, by fitting a 3D active mesh model to the sparse point displacements, a dense motion field was obtained, and some useful local parameters of left ventricle in patients with myocardial infarction were estimated. The local parameters are path length, fractional thickening, and strain. Using this process, the cardiac wall motion was quantized to determine the region and extent of infarct lesion. The process was implemented, and the results were examined and modified against the cardiac perfusion scan. Data were acquired from 10 healthy individuals and 20 patients with the myocardial infarction. The findings also reveal that the infarct region can be determined by locating less than 20% in the wall thickening. In all the patients, the process was able to precisely determine the affected region. The cardiac wall kinesis in damaged regions was properly evaluated by normalized path length and presented in standard bull's-eye format. The above approach is promising and can be extended in prognosis of acute heart infraction by prediction of prone to the wall kinesis regions in the patients close to MI by examining the local indexes of the myocardium in the cardiac MRI images.

Application of Hyperelastic-based Active Mesh Model in Cardiac Motion Recovery.

Considering the nonlinear hyperelastic or viscoelastic nature of soft tissues has an important effect on modeling results. In medical applications, accounting nonlinearity begets an ill posed problem, due to absence of external force. Myocardium can be considered as a hyperelastic material, and variational approaches are proposed to estimate stiffness matrix, which take into account the linear and nonlinear properties of myocardium. By displacement estimation of some points in the four-dimensional cardiac magnetic resonance imaging series, using a similarity criterion, the elementary deformations are estimated, then using the Moore-Penrose inverse matrix approach, all point deformations are obtained. Using this process, the cardiac wall motion is quantized to mechanically determine local parameters to investigate the cardiac wall functionality. This process was implemented and tested over 10 healthy and 20 patients with myocardial infarction. In all patients, the process was able to precisely determine the affected region. The proposed approach was also compared with linear one and the results demonstrated its superiority respect to the linear model.

Computer aided detection and classification of acute lymphoblastic leukemia cell subtypes based on microscopic image analysis.

Acute lymphoblastic leukemia (ALL) is a cancer that starts from the early version of white blood cells called lymphocytes in the bone marrow. It can spread to different parts of the body rapidly and if not treated, would probably be deadly within a couple of months. Leukemia cells are categorized into three types of L1, L2, and L3. The cancer is detected through screening of blood and bone marrow smears by pathologists. But manual examination of blood samples is a time-consuming and boring procedure as well as limited by human error risks. So to overcome these limitations a computer-aided detection system, capable of discriminating cancer from noncancer cases and identifying the cancerous cell subtypes, seems to be necessary. In this article an automatic detection method is proposed; first cell nucleus is segmented by fuzzy c-means clustering algorithm. Then a rich set of features including geometric, first- and second-order statistical features are obtained from the nucleus. A principal component analysis is used to reduce feature matrix dimensionality. Finally, an ensemble of SVM classifiers with different kernels and parameters is applied to classify cells into four groups, that is noncancerous, L1, L2, and L3. Results show that the proposed method can be used as an assistive diagnostic tool in laboratories.

Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier.

Acute lymphoblastic leukemia is the most common form of pediatric cancer which is categorized into three L1, L2, and L3 and could be detected through screening of blood and bone marrow smears by pathologists. Due to being time-consuming and tediousness of the procedure, a computer-based system is acquired for convenient detection of Acute lymphoblastic leukemia. Microscopic images are acquired from blood and bone marrow smears of patients with Acute lymphoblastic leukemia and normal cases. After applying image preprocessing, cells nuclei are segmented by k-means algorithm. Then geometric and statistical features are extracted from nuclei and finally these cells are classified to cancerous and noncancerous cells by means of support vector machine classifier with 10-fold cross validation. These cells are also classified into their sub-types by multi-Support vector machine classifier. Classifier is evaluated by these parameters: Sensitivity, specificity, and accuracy which values for cancerous and noncancerous cells 98%, 95%, and 97%, respectively. These parameters are also used for evaluation of cell sub-types which values in mean 84.3%, 97.3%, and 95.6%, respectively. The results show that proposed algorithm could achieve an acceptable performance for the diagnosis of Acute lymphoblastic leukemia and its sub-types and can be used as an assistant diagnostic tool for pathologists.

An Undecimated Wavelet-based Method for Cochlear Implant Speech Processing.

A cochlear implant is an implanted electronic device used to provide a sensation of hearing to a person who is hard of hearing. The cochlear implant is often referred to as a bionic ear. This paper presents an undecimated wavelet-based speech coding strategy for cochlear implants, which gives a novel speech processing strategy. The undecimated wavelet packet transform (UWPT) is computed like the wavelet packet transform except that it does not down-sample the output at each level. The speech data used for the current study consists of 30 consonants, sampled at 16 kbps. The performance of our proposed UWPT method was compared to that of infinite impulse response (IIR) filter in terms of mean opinion score (MOS), short-time objective intelligibility (STOI) measure and segmental signal-to-noise ratio (SNR). Undecimated wavelet had better segmental SNR in about 96% of the input speech data. The MOS of the proposed method was twice in comparison with that of the IIR filter-bank. The statistical analysis revealed that the UWT-based N-of-M strategy significantly improved the MOS, STOI and segmental SNR (P < 0.001) compared with what obtained with the IIR filter-bank based strategies. The advantage of UWPT is that it is shift-invariant which gives a dense approximation to continuous wavelet transform. Thus, the information loss is minimal and that is why the UWPT performance was better than that of traditional filter-bank strategies in speech recognition tests. Results showed that the UWPT could be a promising method for speech coding in cochlear implants, although its computational complexity is higher than that of traditional filter-banks.

Extremely low-frequency electromagnetic field influences the survival and proliferation effect of human adipose derived stem cells.

BACKGROUND: Extremely low-frequency electromagnetic fields (ELF-EMF) can effect on biological systems and alters some cell functions like proliferation rate. Therefore, we aimed to attempt the evaluation effect of ELF-EMF on the growth of human adipose derived stem cells (hADSCs). MATERIALS AND METHODS: ELF-EMF was generated by a system including autotransformer, multi-meter, solenoid coils, teslameter and its probe. We assessed the effect of ELF-EMF with intensity of 0.5 and 1 mT and power line frequency 50 Hz on the survival of hADSCs for 20 and 40 min/day for 7 days by MTT assay. One-way analysis of variance was used to assessment the significant differences in groups. RESULTS: ELF-EMF has maximum effect with intensity of 1 mT for 20 min/day on proliferation of hADSCs. The survival and proliferation effect (PE) in all exposure groups were significantly higher than that in sham groups (P < 0.05) except in group of 1 mT and 40 min/day. CONCLUSION: Our results show that between 0.5 m and 1 mT ELF-EMF could be enhances survival and PE of hADSCs conserving the duration of exposure.

A review of algorithms for segmentation of optical coherence tomography from retina.

Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues. OCT image segmentation is mostly introduced on retinal OCT to localize the intra-retinal boundaries. Here, we review some of the important image segmentation methods for processing retinal OCT images. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. Current researches in OCT segmentation are mostly based on improving the accuracy and precision, and on reducing the required processing time. There is no doubt that current 3-D imaging modalities are now moving the research projects toward volume segmentation along with 3-D rendering and visualization. It is also important to develop robust methods capable of dealing with pathologic cases in OCT imaging.

Desired Accuracy Estimation of Noise Function from ECG Signal by Fuzzy Approach.

Unknown noise and artifacts present in medical signals with non-linear fuzzy filter will be estimated and then removed. An adaptive neuro-fuzzy interference system which has a non-linear structure presented for the noise function prediction by before Samples. This paper is about a neuro-fuzzy method to estimate unknown noise of Electrocardiogram signal. Adaptive neural combined with Fuzzy System to construct a fuzzy Predictor. For this system setting parameters such as the number of Membership Functions for each input and output, training epochs, type of MFs for each input and output, learning algorithm and etc. is determined by learning data. At the end simulated experimental results are presented for proper validation.