Clustering and Symbolic Analysis of Cardiovascular Signals: Discovery and Visualization of Medically Relevant Patterns in Long-Term Data Using Limited Prior Knowledge

Syed, Zeeshan; Guttag, John V.; Stultz, Collin M.

doi:10.1155/2007/67938

Cited by 33 publications

(34 citation statements)

References 23 publications

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“…As a result, the proposed systematic approach by temporal segmentation and the dynamic clustering technique produces such key-beats that represent all possible physiological heart activities in patient's ECG data. Therefore, finding the true number of clusters by the proposed systematic approach is the key factor that makes a major difference from some earlier works such as [9] and [12], both of which iteratively determine this number by an empirical threshold parameter. Table I shows the overall results of the proposed systematic approach over all patients from the MIT-BIH Long-Term ECG database.…”

Section: Resultsmentioning

confidence: 96%

“…Yet due to the lack of discrimination power of the morphological or temporal features or the distance metric used, the dynamic clustering operation may create more than one cluster for each anomaly. Furthermore, the normal beats have a broad range of morphological characteristics [9] and within a long time span of 24 hours or longer, it is obvious that the temporal characteristics of the normal beats may vary significantly, too. Therefore, it is reasonable to represent normal beats with multiple clusters rather than only one.…”

Section: Classification Of Holter Registers Bymentioning

confidence: 99%

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Classification of holter registers by dynamic clustering using multi-dimensional particle swarm optimization

Kıranyaz

İnce

Pulkkinen

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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Abstract-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. I. INTRODUCTIONATA clustering is a multi-modal problem especially in high dimensions. There are many suboptimal solutions and well-known deterministic methods such as K-means,, etc. are susceptible to get trapped to the closest local optimum since they are nothing but greedy descent methods, which start from a random point in the solution space and perform a localized search. Furthermore, all the mentioned clustering algorithms require the number of clusters to be specified in advance.Multi-dimensional particle swarm optimization (MD PSO) [6] is a dynamic extension of the basic particle swarm optimization (bPSO) algorithm, where the particles can make inter-dimensional passes during their search. Thus they are not restricted to search for an optimal solution in a single dimension, but the optimal solution dimension is solved in parallel. As a stochastic optimization technique MD PSO can avoid some local optima but is still susceptible 1 This work was supported by the Academy of Finland, project No. 213462 (Finnish Centre of Excellence Program (2006 -2011 to premature convergence due to lack of divergence. Fractional global best formation (FGBF), on the other hand, is an effective cure for the premature convergence. It basically collects all promising components of the particle positions and fractionally creates an artificial global best solution that has a potential to be a better guide than the best solution found by the swarm, which is used in the bPSO. Combined, MD PSO and FGBF form an efficient dynamic clustering technique with a high resistance to local optima.Long-term continuous electrocardiogram (ECG) monitoring and recording, also known as Holter electrocard...

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Section: Resultsmentioning

confidence: 96%

Section: Classification Of Holter Registers Bymentioning

confidence: 99%

Classification of holter registers by dynamic clustering using multi-dimensional particle swarm optimization

Kıranyaz

İnce

Pulkkinen

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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“…The algorithm uses dynamic programming to search for an alignment that minimizes the overall distortion. Distortion is measured using the method described in [4], which captures differences in both amplitude and timing of ECG waves.…”

Section: Methodsmentioning

confidence: 99%

Risk-stratification following acute coronary syndromes using a novel electrocardiographic technique to measure variability in morphology

Syed

Scirica

Stultz

et al. 2008

2008 Computers in Cardiology

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“…Here, K is the length of the alignment [15]. From the alignment, we obtain the point f inŴ that is matched with the segment boundary 2 in Z.…”

Section: Weighted Time Warping Based Template Matchingmentioning

confidence: 99%

Real time reconstruction of quasiperiodic multi parameter physiological signals

Ganeshapillai

Guttag

2012

EURASIP J. Adv. Signal Process.

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A modern intensive care unit (ICU) has automated analysis systems that depend on continuous uninterrupted real time monitoring of physiological signals such as electrocardiogram (ECG), arterial blood pressure (ABP), and photo-plethysmogram (PPG). These signals are often corrupted by noise, artifacts, and missing data. We present an automated learning framework for real time reconstruction of corrupted multi-parameter nonstationary quasiperiodic physiological signals. The key idea is to learn a patient-specific model of the relationships between signals, and then reconstruct corrupted segments using the information available in correlated signals. We evaluated our method on MIT-BIH arrhythmia data, a two-channel ECG dataset with many clinically significant arrhythmias, and on the CinC challenge 2010 data, a multi-parameter dataset containing ECG, ABP, and PPG. For each, we evaluated both the residual distance between the original signals and the reconstructed signals, and the performance of a heartbeat classifier on a reconstructed ECG signal. At an SNR of 0 dB, the average residual distance on the CinC data was roughly 3% of the energy in the signal, and on the arrhythmia database it was roughly 16%. The difference is attributable to the large amount of diversity in the arrhythmia database. Remarkably, despite the relatively high residual difference, the classification accuracy on the arrhythmia database was still 98%, indicating that our method restored the physiologically important aspects of the signal.

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Clustering and Symbolic Analysis of Cardiovascular Signals: Discovery and Visualization of Medically Relevant Patterns in Long-Term Data Using Limited Prior Knowledge

Cited by 33 publications

References 23 publications

Classification of holter registers by dynamic clustering using multi-dimensional particle swarm optimization

Classification of holter registers by dynamic clustering using multi-dimensional particle swarm optimization

Risk-stratification following acute coronary syndromes using a novel electrocardiographic technique to measure variability in morphology

Real time reconstruction of quasiperiodic multi parameter physiological signals

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