ECG Artefact Detection Using Ensemble Decision Trees

HighlightsA novel way for ECG quality assessment is proposed, based on the posterior probability of an artefact detection classifier.A good performance was obtained when testing the classifier on two independent (re)labelled datasets, thereby showing its robustness. The performance was better, compared to a heuristic method and comparable to another machine learning algorithm.A significant correlation was observed between the proposed quality assessment and the annotators level of agreement.Significant decreases in quality level were observed for different noise levels.

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“…Based on previous work, we selected three features from the ACF’s to characterize the ECG segments [15]:…”

Section: Methodsmentioning

confidence: 99%

Artefact detection and quality assessment of ambulatory ECG signals

Moeyersons

Smets

Morales

et al. 2019

Computer Methods and Programs in Biomedicine

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“…A network intrusion detection scheme using an ensemble classifier is proposed in [63]. An electrocardiograph artifact is detected and analyzed using the ensemble decision trees method in [59]. In [60], abnormal propagation echo for weather radar is detected using an ensemble classifier.…”

Section: Step 2: Training Using Boosted Trees Algorithmmentioning

confidence: 99%

“…Ensemble learning is a useful method to achieve more detection accuracy compared with the detection performance of the individual classifiers. Depression detection and intrusion detection schemes are analyzed in abnormal echo propagation for weather radar, which are investigated in [59,60]. A smart grid environment using AdaBoost for islanding detection is proposed in [61].…”

Section: Introductionmentioning

confidence: 99%

Boosted Trees Algorithm as Reliable Spectrum Sensing Scheme in the Presence of Malicious Users

et al. 2020

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Cooperative spectrum sensing (CSS) has the ability to accurately identify the activities of the primary users (PUs). As the secondary users’ (SUs) sensing performance is disturbed in the fading and shadowing environment, therefore the CSS is a suitable choice to achieve better sensing results compared to individual sensing. One of the problems in the CSS occurs due to the participation of malicious users (MUs) that report false sensing data to the fusion center (FC) to misguide the FC’s decision about the PUs’ activity. Out of the different categories of MUs, Always Yes (AY), Always No (AN), Always Opposite (AO) and Random Opposite (RO) are of high interest these days in the literature. Recently, high sensing performance for the CSS can be achieved using machine learning techniques. In this paper, boosted trees algorithm (BTA) has been proposed for obtaining reliable identification of the PU channel, where the SUs can access the PU channel opportunistically with minimum disturbances to the licensee. The proposed BTA mitigates the spectrum sensing data falsification (SSDF) effects of the AY, AN, AO and RO categories of the MUs. BTA is an ensemble method for solving spectrum sensing problems using different classifiers. It boosts the performance of some weak classifiers in the combination by giving higher weights to the weak classifiers’ sensing decisions. Simulation results verify the performance improvement by the proposed algorithm compared to the existing techniques such as genetic algorithm soft decision fusion (GASDF), particle swarm optimization soft decision fusion (PSOSDF), maximum gain combination soft decision fusion (MGCSDF) and count hard decision fusion (CHDF). The experimental setup is conducted at different levels of the signal-to-noise ratios (SNRs), total number of cooperative users and sensing samples that show minimum error probability results for the proposed scheme.

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“…The use of ensemble learning methods is another practical way of achieving higher detection accuracy. Recently, ensemble classifiers have been used in many detection problems and have shown promising results [26][27][28][29][30][31][32][33]. Ensemble classifiers make use of multiple learning algorithms in order to achieve a prediction efficiency higher than any of their base learners [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…In [27], ensemble classifier is used for network intrusion detection. In [29], ensemble decision trees are used for electrocardiograph artifact detection. In [31], ensemble classifier is used for weather radar anomalous propagation echo detection.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

Ahmad

2019

Wireless Communications and Mobile Computing

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Spectrum sensing is one of the most important and challenging tasks in cognitive radio. To develop methods of dynamic spectrum access, robust and efficient spectrum sensors are required. For most of these sensors, the main constraints are the lack of information about the primary user's (PU) signal, high computational cost, performance limits in low signal-to-noise ratio (SNR) conditions, and difficulty in finding a detection threshold. This paper proposes a machine learning based novel detection method to overcome these limits. To address the first constraint, detection is achieved using cyclostationary features. The constraints of low SNR, finding detection threshold, and computational cost are addressed by proposing an ensemble classifier. First, a dataset is generated containing different orthogonal frequency-division multiplexing signals at different SNRs. Then, cyclostationary features are extracted using FFT accumulation method. Finally, the proposed ensemble classifier has been trained using the extracted features to detect PU's signal in low SNR conditions. This ensemble classifier is based on decision trees and AdaBoost algorithm. A comparison of the proposed classifier with another machine learning classifier, namely, support vector machine (SVM), is presented, clearly showing that the ensemble classifier outperforms SVM. The results of the simulation also prove the robustness and superior efficiency of the detector proposed in this paper in comparison with a cyclostationary detector without machine learning as well as the classical energy detector.

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ECG Artefact Detection Using Ensemble Decision Trees

Abstract: Abstract

Cited by 10 publications

References 7 publications

Artefact detection and quality assessment of ambulatory ECG signals

Artefact detection and quality assessment of ambulatory ECG signals

Boosted Trees Algorithm as Reliable Spectrum Sensing Scheme in the Presence of Malicious Users

Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

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