16,396 research outputs found

    DWT and ANN Based Heart Arrhythmia Disease Diagnosis from MIT-BIH ECG Signal Data

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    Diagnosis of heart disease is complex. ECG plays an important role for analysis and diagnosis of heart disease. Normally ECG signals are affected by different noises. These noises pollute the ECG signal. For quality diagnosis it is necessary to de noise the ECG signal. After de noising ECG signals, a pure signal is used to detect ECG parameters. Detection of ECG parameters takes an important role in the analysis of ECG signal. The Feature extracted ECG signal applied to ANN for classification to detect cardiac arrhythmia. This paper introduces the Electrocardiogram (ECG) pattern recognition method based on wavelet transform and neural network technique with error back propagation method has been used to classify two different types of arrhythmias, namely, Left bundle branch block (LBBB), Right bundle Branch block (RBBB) with normal ECG signal. The MIT-BIH arrhythmias ECG Database has been used for training and testing our neural network based classifier. The simulation results shown at the end. DOI: 10.17762/ijritcc2321-8169.15015

    Wavelet-Based Kernel Construction for Heart Disease Classification

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    © 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERINGHeart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.Peer reviewedFinal Published versio

    Passively mode-locked laser using an entirely centred erbium-doped fiber

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    This paper describes the setup and experimental results for an entirely centred erbium-doped fiber laser with passively mode-locked output. The gain medium of the ring laser cavity configuration comprises a 3 m length of two-core optical fiber, wherein an undoped outer core region of 9.38 μm diameter surrounds a 4.00 μm diameter central core region doped with erbium ions at 400 ppm concentration. The generated stable soliton mode-locking output has a central wavelength of 1533 nm and pulses that yield an average output power of 0.33 mW with a pulse energy of 31.8 pJ. The pulse duration is 0.7 ps and the measured output repetition rate of 10.37 MHz corresponds to a 96.4 ns pulse spacing in the pulse train

    ART Neural Networks: Distributed Coding and ARTMAP Applications

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    ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

    DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

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    Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach
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