Comparison of seven approaches for holter ECG clustering and classification

Chudáček, Václav; Petrík, Milan; Georgoulas, George; Cepek, Miroslav; Lhotská, Lenka; Stylios, Chrysostomos

doi:10.1109/iembs.2007.4353171

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…In the literature of heartbeat clustering, there is no clear consensus about the superiority of one clustering technique. It is possible to obtain similar results with a wide variety of techniques [12,13,14,15,16,17,18,19]. This is consistent with the comparative analysis done in the field of machine learning [20]; none of the clustering techniques has shown a clear advantage over the other techniques.…”

Section: Introductionsupporting

confidence: 82%

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering

Márquez

Félix

García

et al. 2019

Sensors

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In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together. The algorithm has been validated in the electrocardiographic domain for heartbeat clustering, extracting positive evidence from the heartbeat morphology and negative evidence from the distances between heartbeats. The best result obtained on the MIT-BIH Arrhythmia database yielded an error of 1.44%. In the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database database (INCARTDB), an error of 0.601% was obtained when using two electrocardiogram (ECG) leads. When increasing the number of leads to 4, 6, 8, 10 and 12, the algorithm obtains better results (statistically significant) than with the previous number of leads, reaching an error of 0.338%. To the best of our knowledge, this is the first clustering algorithm that is able to process simultaneously any number of ECG leads. Our results support the use of PN-EAC to combine different sources of information and the value of the negative evidence.

show abstract

Section: Introductionsupporting

confidence: 82%

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering

Márquez

Félix

García

et al. 2019

Sensors

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show abstract

“…Most algorithms of APC detection are time based and use the QRS morphology information for APC heartbeat classification [ 19 - 23 ]. On the other hand, some APC detection algorithms [ 24 - 26 ] are frequency based and adopt the Fourier transform or the wavelet transform. In these R-wave peak detection and APC detection algorithms, the support vector machine (SVM), the rule-based decision tree, the artificial neural network, or fuzzy logic are used as classifiers.…”

Section: Introductionmentioning

confidence: 99%

A Telesurveillance System With Automatic Electrocardiogram Interpretation Based on Support Vector Machine and Rule-Based Processing

Ho¹,

Huang²,

Lin³

et al. 2015

JMIR Med Inform

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BackgroundTelehealth care is a global trend affecting clinical practice around the world. To mitigate the workload of health professionals and provide ubiquitous health care, a comprehensive surveillance system with value-added services based on information technologies must be established.ObjectiveWe conducted this study to describe our proposed telesurveillance system designed for monitoring and classifying electrocardiogram (ECG) signals and to evaluate the performance of ECG classification.MethodsWe established a telesurveillance system with an automatic ECG interpretation mechanism. The system included: (1) automatic ECG signal transmission via telecommunication, (2) ECG signal processing, including noise elimination, peak estimation, and feature extraction, (3) automatic ECG interpretation based on the support vector machine (SVM) classifier and rule-based processing, and (4) display of ECG signals and their analyzed results. We analyzed 213,420 ECG signals that were diagnosed by cardiologists as the gold standard to verify the classification performance.ResultsIn the clinical ECG database from the Telehealth Center of the National Taiwan University Hospital (NTUH), the experimental results showed that the ECG classifier yielded a specificity value of 96.66% for normal rhythm detection, a sensitivity value of 98.50% for disease recognition, and an accuracy value of 81.17% for noise detection. For the detection performance of specific diseases, the recognition model mainly generated sensitivity values of 92.70% for atrial fibrillation, 89.10% for pacemaker rhythm, 88.60% for atrial premature contraction, 72.98% for T-wave inversion, 62.21% for atrial flutter, and 62.57% for first-degree atrioventricular block.ConclusionsThrough connected telehealth care devices, the telesurveillance system, and the automatic ECG interpretation system, this mechanism was intentionally designed for continuous decision-making support and is reliable enough to reduce the need for face-to-face diagnosis. With this value-added service, the system could widely assist physicians and other health professionals with decision making in clinical practice. The system will be very helpful for the patient who suffers from cardiac disease, but for whom it is inconvenient to go to the hospital very often.

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“…Ghongade and Ghatol used six morphological features for the classification of four types of ECG beats by using MLP, RBFNN and support vector machines (SVM) (Ghongade & Ghatol, 2008). Chudacek et al extracted 13 morphological features to compare performance of seven methods including RBFNN, on ECG beat classification (Chudacek et al, 2007). In these studies RBFNN is trained with the traditional methods that have been mentioned before.…”

Section: Introductionmentioning

confidence: 99%

ECG beat classification using particle swarm optimization and radial basis function neural network

Korürek

Doğan

2010

Expert Systems with Applications

177

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Comparison of seven approaches for holter ECG clustering and classification

Cited by 12 publications

References 17 publications

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering

A Telesurveillance System With Automatic Electrocardiogram Interpretation Based on Support Vector Machine and Rule-Based Processing

ECG beat classification using particle swarm optimization and radial basis function neural network

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