A Novel Neural Network based Classification for ECG Signals

Cardiac Arrhythmia represents heart abnormalities. This problem is faced by people, irrespective of age. Even the physicians feel difficulty in diagnosing the abnormal behavior of heart accurately. Accurate detection of cardiac abnormalities helps to provide right treatment. Classification plays an important role in predicting abnormal behaviors of heart and it helps the physician to treat the patients who are having cardiac arrhythmia. Extracted features from ECG (Electrocardiogram) signals are used for classification. It is possible to extract multiple features from ECG signal regardless of the features used for classification. Classification performed using all the extracted features leads to misclassification of abnormalities. So feature selection is an important concept in classifying the normal and abnormal behavior of heart. MIT BIH Arrhythmia dataset is used in our proposed work where the classification is done in MATLAB using Fitting Neural Network. DOI: 10.17762/ijritcc2321-8169.150314

COMPUTER AIDED DIAGNOSIS OF VENTRICULAR ARRHYTHMIAS FROM ELECTROCARDIOGRAM LEAD II SIGNALS

In this work, we use computer aided diagnosis (CADx) to extract features from ECG signals and detect different types of cardiac ventricular arrhythmias including Ventricular Tachycardia (VT),Ventricular Fibrillation (VF), Ventricular Couplet (VC), and Ventricular Bigeminy (VB).Our methodology is unique in computing features of lower and higher order statistical parameters from six different data domains: time domain, Fourier domain, and four Wavelet domains (Daubechies, Coiflet, Symlet, and Meyer). These features proved to give superior classification performance, in general, regardless of the type of classifier used as compared with previous studies. However, Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers got better performance than other classifiers tried including KNN and Naïve Bayes classifiers. Our unique features enabled classifiers to perform better in comparison with previous studies: for VT, 100% accuracy while best previous work got 95.8%, for VF, 100% accuracy while best previous work got 97.5%, for VC, 100% sensitivity while best previous work got 71.8%, and for VB, 100% sensitivity while best previous work got 84.6%

Diagnosis of Arrhythmia Using Neural Networks

This dissertation presents an intelligent framework for classification of heart arrhythmias. It is a framework of cascaded discrete wavelet transform and the Fourier transform as preprocessing stages for the neural network. This work exploits the information about heart activity contained in the ECG signal; the power of the wavelet and Fourier transforms in characterizing the signal and the power learningpower of neural networks. Firstly, the ECG signals are four-level discrete wavelet decomposed using a filter-bank and mother wavelet 'db2'. Then all the detailed coefficients were discarded, while retaining only the approximation coefficients at the fourth level. The retained approximation coefficients are Fourier transformed using a 16-point FFT. The FFT is symmetrical, therefore only the first 8-points are sufficient to characterize the spectrum. The last 8-points resulting from theFFTare discarded during feature selection. The 8-point feature vector is then used to train a feedforward neural network with one hidden layer of 20-units and three outputs. The neural network is trained by using the Scaled Conjugate Gradient Backpropagation algorithm (SCG). This was implemented in a MATLAB environment using the MATLAB GUINeural networktoolbox.. This approach yields an accuracy of 94.66% over three arrhythmia classes, namely the Ventricular Flutter (VFL), the Ventricular Tachycardia (VT) and the Supraventricular Tachyarrhythmia (SVTA). We conclude that for the amount of information retained and the number features used the performance is fairly competitive

Diagnosis of Arrhythmia Using Neural Networks

This dissertation presents an intelligent framework for classification of heart arrhythmias. It is a framework of cascaded discrete wavelet transform and the Fourier transform as preprocessing stages for the neural network. This work exploits the information about heart activity contained in the ECG signal; the power of the wavelet and Fourier transforms in characterizing the signal and the power learningpower of neural networks. Firstly, the ECG signals are four-level discrete wavelet decomposed using a filter-bank and mother wavelet 'db2'. Then all the detailed coefficients were discarded, while retaining only the approximation coefficients at the fourth level. The retained approximation coefficients are Fourier transformed using a 16-point FFT. The FFT is symmetrical, therefore only the first 8-points are sufficient to characterize the spectrum. The last 8-points resulting from theFFTare discarded during feature selection. The 8-point feature vector is then used to train a feedforward neural network with one hidden layer of 20-units and three outputs. The neural network is trained by using the Scaled Conjugate Gradient Backpropagation algorithm (SCG). This was implemented in a MATLAB environment using the MATLAB GUINeural networktoolbox.. This approach yields an accuracy of 94.66% over three arrhythmia classes, namely the Ventricular Flutter (VFL), the Ventricular Tachycardia (VT) and the Supraventricular Tachyarrhythmia (SVTA). We conclude that for the amount of information retained and the number features used the performance is fairly competitive

Heart beat classification

Cieľom tejto práce bolo vyvinúť metódu klasifikácie EKG cyklov do dvoch tried, ischemických a neischemických cyklov. Ako vstupný signál boli použité spracované srdečné cykly (P-QRS-T cykly) získané meraním animálneho EKG ortogonálným zvodovým systémom. Príznakový vektor bol vytvorený ako výsledok analýzy vzájomnej koherencie spektier, analýzy primárnych komponentov, HRV parametrov a ich kombinácií. Výsledné cykly boli klasifikované za pomoci doprednej neurónovej siete so spätným šírením chyby. Klasifikátor bol navrhnutý v Matlabe. Výkon klasifikácie dosahoval hodnôt v rozmedzí 87,2 až 100%. Výsledky experimentu môžu byť vhodné pre budúce štúdie automatickej klasifikácie EKG.The aim of this work was to develop the method for classification of ECG beats into two classes, namely ischemic and non-ischemic beats. Heart beats (P-QRS-T cycles) selected from animals orthogonal ECGs were preprocessed and used as the input signals. Spectral features vectors (values of cross spectral coherency), principal component and HRV parameters were derived from the beats. The beats were classified using feedforward multilayer neural network designed in Matlab. Classification performance reached the value approx. from 87,2 to 100%. Presented results can be suitable in future studies aimed at automatic classification of ECG.

Development of empirical mode decomposition based neural network for power quality disturbances classification

The complexity of the electric power network causes a lot of distortion, such as a decrease in power quality (PQ) in the form of voltage variations, harmonics, and frequency fluctuations. Monitoring the distortion source is important to ensure the availability of clean and quality electric power. Therefore, this study aims to classify power quality using a neural network with empirical mode decomposition-based feature extraction. The proposed method consists of 2 main steps, namely feature extraction, and classification. Empirical Mode Decomposition (EMD) was also applied to categorize the PQ disturbances into several intrinsic mode functions (IMF) components, which were extracted using statistical parameters and the Hilbert transformation. The statistical parameters consist of mean, root mean squared, range, standard deviation, kurtosis, crest factor, energy, and skewness, while the Hilbert transformation consists of instantaneous frequency and amplitude. The feature extraction results from both parameters were combined into a set of PQ disturbances and classified using Multi-Layer Feedforward Neural Networks (MLFNN). Training and testing were carried out on 3 feature datasets, namely statistical parameters, Hilbert transforms, and a combination of both as inputs from 3 different MLFNN architectures. The best results were obtained from the combined feature input on the network architecture with 2 layers of ten neurons, by 98.4 %, 97.75, and 97.4 % for precision, recall, and overall accuracy, respectively. The implemented method is used to classify PQ signals reliably for pure sinusoids, harmonics with sag and swell, as well as flicker with 100 % precisio

Wavelet Signal Processing of Physiologic Waveforms

The prime objective of this piece of work is to devise novel techniques for computer based classification of Electrocardiogram (ECG) arrhythmias with a focus on less computational time and better accuracy. As an initial stride in this direction, ECG beat classification is achieved by using feature extracting techniques to make a neural network (NN) system more effective. The feature extraction technique used is Wavelet Signal Processing. Coefficients from the discrete wavelet transform were used to represent the ECG diagnostic information and features were extracted using the coefficients and were normalised. These feature sets were then used in the classifier i.e. a simple feed forward back propagation neural network (FFBNN). This paper presents a detail study of the classification accuracy of ECG signal by using these four structures for computationally efficient early diagnosis. Neural network used in this study is a well-known neural network architecture named as multi-Layered perceptron (MLP) with back propagation training algorithm. The ECG signals have been taken from MIT-BIH ECG database, and are used in training to classify 3 different Arrhythmias out of ten arrhythmias. These are normal sinus rhythm, paced beat, left bundle branch block. Before testing, the proposed structures are trained by back propagation algorithm. The results show that the wavelet decomposition method is very effective and efficient for fast computation of ECG signal analysis in conjunction with the classifier

Computer aided assessment of CT scans of traumatic brain injury patients

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyOne of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues

The development and assessment of a generic carbamazepine sustained release dosage form

Carbamazepine (CBZ) is a first-line drug used for the treatment of partial and tonic-clonic seizures. It is also the drug of choice for use during pregnancy and recommended for the treatment of seizure disorders in children. CBZ possesses the ability to induce metabolism of drugs that are transformed in the liver and has the unique ability to induce its own metabolism by a phenomenon known as ‘auto- induction’, where its biological half-life is significantly reduced during chronic administration. Large doses of CBZ are often prescribed as daily divided doses and this often adversely affects patient compliance, with the result that therapy is ineffective. A sustained-release dosage form containing CBZ is currently marketed as Tegretol® CR and the development of a generic product would provide patients with an equivalent product with a similar dosing frequency, at a reduced cost. Therefore, the development of a polymer-based matrix tablet was undertaken to produce a sustained-release dosage form of CBZ, since these dosage forms are relatively simple and cheap to produce when compared to other, more sophisticated forms of sustained-release technology. Preformulation studies were conducted to assess moisture content of excipients and dosage forms and to identify possible incompatibilities between CBZ and potential formulation excipients. Furthermore, studies were conducted to assess the potential for polymorphic transitions to occur during manufacture. Stability testing was conducted to assess the behaviour of the dosage forms under storage conditions that the product may be exposed to. Dissolution testing was undertaken using USP Apparatus 3, which allowed for a more realistic assessment and prediction of in vivo drug release rates. Samples were analysed using a high performance liquid chromatographic method that was developed and validated for the determination of CBZ. Tablets were manufactured by wet granulation and direct compression techniques, and the resultant drug release profiles were evaluated statistically by means of the f1 and f2 difference and similarity factors. The f2 factor was incorporated as an assessment criterion in the design of an artificial neural network that was used to predict drug release profiles and formulation composition. A direct compression tablet formulation was successfully adapted from a prototype wet granulation matrix formulation and a number of formulation variables were assessed to establish their effect(s) on the dissolution rate profile of CBZ that resulted from testing of the dosage forms. The particle size grade of CBZ was also investigated and it was ascertained that fine particle size grade CBZ showed improved drug release profiles when compared to the coarse grade CBZ which was desirable, since CBZ is a highly water insoluble compound. Furthermore, the impact of the viscosity grade and proportion of rate-controlling polymer, viz., hydroxypropyl methylcellulose was also investigated for its effect on drug release rates. The lower viscosity grade was found to be more appropriate for use with CBZ. The type of anti-frictional agent used in the formulations did not appear to affect drug release from the polymeric matrix tablets, however specific compounds may have an effect on the physical characteristics of the polymeric tablets. The resultant formulations did not display zero-order drug release kinetics and a first-order mathematical model was developed to provide an additional resource for athematical analysis of dissolution profiles. An artificial neural network was designed, developed and applied to predict dissolution rate profiles for formulation. Furthermore, the network was used to predict formulation compositions that would produce drug release profiles comparable to the reference product, Tegretol® CR. The formulation composition predicted by the network to match the dissolution profile of the innovator product was manufactured and tested in vitro. The formulation was further manipulated, empirically, so as to match the in vitro dissolution rate profile of Tegretol® CR, more completely. The test tablets that were produced were tested in two health male volunteers using Tegretol® CR 400mg as the reference product. The batch used for this “proof of concept” biostudy was produced in accordance with cGMP guidelines and the protocol in accordance with ICH guidelines. The test matrix tablets revealed in vivo bioavailability profiles for CBZ, however, bioequivalence between the test and reference product could not be established. It can be concluded that the polymeric matrix CBZ tablets have the potential to be used as a twice-daily dosage form for the treatment of relevant seizure disorders