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  • bioengineering
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  • 1
    Language: English
    In: IEEE Transactions on Biomedical Engineering, March 2016, Vol.63(3), pp.664-675
    Description: Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. Results: The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Conclusion: Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Significance: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.
    Keywords: Electrocardiography ; Neurons ; Feature Extraction ; Kernel ; Databases ; Training ; Monitoring ; Patient-Specific ECG Classification ; Convolutional Neural Networks ; Real-Time Heart Monitoring ; Medicine ; Engineering
    ISSN: 0018-9294
    E-ISSN: 1558-2531
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  • 2
    Language: English
    In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, March 2016, Vol.24(3), pp.386-398
    Description: In this paper, the performance of the phase space representation in interpreting the underlying dynamics of epileptic seizures is investigated and a novel patient-specific seizure detection approach is proposed based on the dynamics of EEG signals. To accomplish this, the trajectories of seizure and nonseizure segments are reconstructed in a high dimensional space using time-delay embedding method. Afterwards, Principal Component Analysis (PCA) was used in order to reduce the dimension of the reconstructed phase spaces. The geometry of the trajectories in the lower dimensions is then characterized using Poincaré section and seven features were extracted from the obtained intersection sequence. Once the features are formed, they are fed into a two-layer classification scheme, comprising the Linear Discriminant Analysis (LDA) and Naive Bayesian classifiers. The performance of the proposed method is then evaluated over the CHB-MIT benchmark database and the proposed approach achieved 88.27% sensitivity and 93.21% specificity on average with 25% training data. Finally, we perform comparative performance evaluations against the state-of-the-art methods in this domain which demonstrate the superiority of the proposed method.
    Keywords: Electroencephalography ; Feature Extraction ; Trajectory ; Nonlinear Dynamical Systems ; Epilepsy ; Geometry ; Benchmark Testing ; Dynamics ; Electroencephalography (EEG) ; Phase Space ; Poincaré Section ; Seizure Detection ; Two-Layer Classifier Topology ; Occupational Therapy & Rehabilitation
    ISSN: 1534-4320
    E-ISSN: 1558-0210
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  • 3
    Language: English
    In: IEEE transactions on bio-medical engineering, May 2009, Vol.56(5), pp.1415-26
    Description: This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature space using principal component analysis, and temporal features from the ECG data. For the pattern recognition unit, feedforward and fully connected artificial neural networks, which are optimally designed for each patient by the proposed multidimensional particle swarm optimization technique, are employed. By using relatively small common and patient-specific training data, the proposed classification system can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and thus, achieves higher accuracy over larger datasets. The classification experiments over a benchmark database demonstrate that the proposed system achieves such average accuracies and sensitivities better than most of the current state-of-the-art algorithms for detection of ventricular ectopic beats (VEBs) and supra-VEBs (SVEBs). Over the entire database, the average accuracy-sensitivity performances of the proposed system for VEB and SVEB detections are 98.3%-84.6% and 97.4%-63.5%, respectively. Finally, due to its parameter-invariant nature, the proposed system is highly generic, and thus, applicable to any ECG dataset.
    Keywords: Signal Processing, Computer-Assisted ; Arrhythmias, Cardiac -- Physiopathology ; Electrocardiography -- Methods ; Pattern Recognition, Automated -- Methods
    ISSN: 00189294
    E-ISSN: 1558-2531
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  • 4
    Language: English
    In: 2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.3769-3774
    Description: In this paper, we propose a graph affinity learning method for a recently proposed graph-based salient object detection method, namely Extended Quantum Cuts (EQCut). We exploit the fact that the output of EQCut is differentiable with respect to graph affinities, in order to optimize linear combination coefficients and parameters of several differentiable affinity functions by applying error backpropagation. We show that the learnt linear combination of affinities improves the performance over the baseline method and achieves comparable (or even better) performance when compared to the state-of-the-art salient object segmentation methods.
    Keywords: Object Detection ; Object Segmentation ; Symmetric Matrices ; Image Segmentation ; Quantum Mechanics ; Graph Theory ; Computer Vision ; Graph Affinity Learning ; Salient Object Segmentation ; Spectral Graph Theory
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 5
    Language: English
    In: 2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.3645-3649
    Description: Recently, Approximate Nearest Neighbor (ANN) Search has become a very popular approach for similarity search on large-scale datasets. In this paper, we propose a novel vector quantization method for ANN, which introduces a joint multi-layer K-Means clustering solution for determination of the codebooks. The performance of the proposed method is improved further by a joint encoding scheme. Experimental results verify the success of the proposed algorithm as it outperforms the state-of-the-art methods.
    Keywords: Encoding ; Training ; Hamming Distance ; Optimization ; Vector Quantization ; Search Problems
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 6
    Language: English
    In: 2015 International Conference on Information and Communication Technology Research (ICTRC), May 2015, pp.116-119
    Description: In this paper, we propose an improved spectrum access algorithm for cognitive radio applications using a Hidden Markov Model (HMM) for learning the primary user channel usage pattern. The proposed scheme maximizes the channel utilization without causing significant interference to the primary user. Simulation results show that the proposed algorithm provides about 3 times improvement in channel utilization compared to the system proposed in [1], with a slight degradation in collision probability. It is also observed that the proposed scheme performance is robust to variations in the primary user behavior.
    Keywords: Hidden Markov Models ; Cognitive Radio ; Prediction Algorithms ; Sensors ; Interference ; Signal to Noise Ratio ; Cognitive Radio ; Hidden Markov Models ; Spectrum Access
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 7
    Language: English
    In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010, Vol.2010, pp.4695-8
    Description: In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system.
    Keywords: Algorithms ; Cluster Analysis ; Expert Systems ; Arrhythmias, Cardiac -- Diagnosis ; Diagnosis, Computer-Assisted -- Methods ; Electrocardiography, Ambulatory -- Methods ; Pattern Recognition, Automated -- Methods
    ISBN: 9781424441235
    ISSN: 1557-170X
    ISSN: 1094687X
    E-ISSN: 15584615
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  • 8
    Language: English
    In: 2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.2276-2281
    Description: Aquatic macroinvertebrate biomonitoring is an efficient way of assessment of slow and subtle anthropogenic changes and their effect on water quality. It is imperative to have reliable identification and counts of the various taxa occurring in samples as these form the basis for the quality indices used to infer the ecological status of the aquatic ecosystem. In this paper, we try to close the gap between human taxa identification accuracy (typically 90-95% on 30-40 classes of macroinvertebrates) and results of automatic fine-grained classification by introducing a novel technique based on Convolutional Neural Networks (CNN). CNN learns optimal features for macroinvertebrate classification and achieves near human accuracy when tested on 29 macroinvertebrate classes. Moreover, we perform comparative evaluation of the learned features against the hand-crafted features, which have been commonly used in classical approaches, and confirm superiority of the learned deep features over the engineered ones.
    Keywords: Feature Extraction ; Ecosystems ; Water Resources ; Machine Vision ; Microscopy ; Databases
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 9
    Language: English
    In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 2008, pp.5474-5477
    Description: In this paper, we present an automated patient-specific electrocardiogram (ECG) beat classifier designed for accurate detection of premature ventricular contractions (PVCs). In the proposed feature extraction scheme, the principal component analysis (PCA) is applied to the dyadic wavelet transform (DWT) of the ECG signal to extract morphological ECG features, which are then combined with the temporal features to form a resultant efficient feature vector. For the classification scheme, we selected the feed-forward artificial neural networks (ANNs) optimally designed by the multi-dimensional particle swarm optimization (MD-PSO) technique, which evolves the structure and weights of the network specifically for each patient. Training data for the ANN classifier include both global (total of 150 representative beats randomly sampled from each class in selected training files) and local (the first 5 min of a patient's ECG recording) training patterns. Simulation results using 40 files in the MIT/BIH arrhythmia database achieved high average accuracy of 97% for differentiating normal, PVC, and other beats.
    Keywords: Engineering
    ISBN: 9781424418145
    ISBN: 1424418143
    ISSN: 1557170X
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  • 10
    Language: English
    In: IEEE Journal of Biomedical and Health Informatics, 26 March 2019, pp.1-1
    Description: Nonlinear dynamics has recently been extensively used to study epilepsy due to the complex nature of the neuronal systems. This study presents a novel method that characterizes the dynamic behavior of pediatric seizure events and introduces a systematic approach to locate the nullclines on the phase space when the governing differential equations are unknown. Nullclines represent the locus of points in the solution space where the components of the velocity vectors are zero. A simulation study over 5 benchmark nonlinear systems with well-known differential equations in 3D exhibits the characterization efficiency and accuracy of the proposed approach that is solely based on the reconstructed solution trajectory. Due to their unique characteristics in the nonlinear dynamics of epilepsy, discriminative features can be extracted based on the nullclines concept. Using a limited training data (only 25% of each EEG record) in order to mimic the real-world clinical practice, the proposed approach achieves 91.15% average sensitivity and 95.16% average specificity over the benchmark CHB-MIT dataset. Together with an elegant computational efficiency, the proposed approach can, therefore, be an automatic and reliable solution for patient-specific seizure detection in long EEG recordings.
    Keywords: Differential Equations ; Feature Extraction ; Nonlinear Dynamical Systems ; Trajectory ; Electroencephalography ; Stability Analysis ; Systematics ; EEG ; Seizure Detection ; Nonlinear Dynamics ; Phase Space ; Nullcline ; Lda ; Ann ; Medicine
    ISSN: 2168-2194
    E-ISSN: 2168-2208
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