Representativeness consideration in the selection of classification algorithms for the ECG signal quality assessment

Keskes, Nesrine; Kobbi-Fakhfakh, Sameh; Kanoun, Olfa; Derbel, Nabil

doi:10.1016/j.bspc.2022.103686

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…This introduces two substantial issues: (1) once the real prevalence of the phenomenon of interest is markedly over-represented in the training and testing data, extra care is required when interpreting the results and inferring about performance in a real-world scenario (Sun et al 2015, Lever 2016, Yijing et al 2016, Fernando and Tsokos 2021; (2) the same over-representation might have induced the developers of other algorithms to underestimate the fact that they were dealing with an extreme class imbalance problem (i.e. P = N) (Yijing et al 2016, Thabtah et al 2020, which is known to have potentially disruptive effects on the robustness performance of classifiers based on machine learning (Keskes et al 2022). This class imbalance leads these classifiers to have unsatisfactory results when tested on extremely imbalanced dataset (Yijing et al 2016, Thabtah et al 2020, Albaba et al 2021, Fernando and Tsokos 2021.…”

Section: Discussionmentioning

confidence: 99%

A novel algorithm to assess the quality of 12-lead ECG recordings: validation in a real telecardiology application

et al. 2023

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Objective. Automatic detection of Electrocardiograms (ECG) quality is fundamental to minimize costs and risks related to delayed diagnosis due to low ECG quality. Most algorithms to assess ECG quality include non-intuitive parameters. Also, they were developed using data non-representative of a real-world scenario, in terms of pathological ECGs and overrepresentation of low-quality ECG. Therefore, we introduce an algorithm to assess 12-lead ECG quality, Noise Automatic Classification Algorithm (NACA) developed in Telehealth Network of Minas Gerais (TNMG). Approach. NACA estimates a signal-to-noise ratio (SNR) for each ECG lead, where ‘signal’ is an estimated heartbeat template, and ‘noise’ is the discrepancy between the template and the ECG heartbeat. Then, clinically-inspired rules based on SNR are used to classify the ECG as acceptable or unacceptable. NACA was compared with Quality Measurement Algorithm (QMA), the winner of Computing in Cardiology Challenge 2011 (ChallengeCinC) by using five metrics: sensitivity (Se), specificity (Sp), positive predictive value (PPV), F 2, and cost reduction resulting from adoption of the algorithm. Two datasets were used for validation: TestTNMG, consisting of 34 310 ECGs received by TNMG (1% unacceptable and 50% pathological); ChallengeCinC, consisting of 1000 ECGs (23% unacceptable, higher than real-world scenario). Main results. Both algorithms reached a similar performance on ChallengeCinC, although NACA performed considerably better than QMA in TestTNMG (Se = 0.89 versus 0.21; Sp = 0.99 versus 0.98; PPV = 0.59 versus 0.08; F 2 = 0.76 versus 0.16 and cost reduction 2.3 ± 1.8% versus 0.3 ± 0.3%, respectively). Significance. Implementing of NACA in a telecardiology service results in evident health and financial benefits for the patients and the healthcare system.

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Section: Discussionmentioning

confidence: 99%

A novel algorithm to assess the quality of 12-lead ECG recordings: validation in a real telecardiology application

et al. 2023

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“…The precision is interested in measuring the correctness among the total positive labels. The recall is signi cant for the true positive rate and expresses how the positive class is predicted (Keskes et al 2022). The ACC is used to measure the percent for the true labels.…”

Section: Discussionmentioning

confidence: 99%

Research on Apple Origins Classification Optimization Based on Least Angle Regression in Instance Selection

Wang

et al. 2022

Preprint

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The combined near-infrared spectral analysis technology and machine learning is widely used in fruit quality detection. The train set split from the heavy sample set contains redundant samples, and modeling directly may cause larger errors. Instance selection methods can be used to improve the performance of the raw train set by optimally selecting the effective samples. So, the least angle regression-based instance selection (LARIS) method was proposed to improve the model accuracy of fruits classification in this paper, an experiment was conducted on a total of 952 apples from four origins. Before splitting, the spectral reconstruction methods were used for preprocessing and the 19 outliers were eliminated by statistics. The sample set partitioning based on joint x-y distance (SPXY) was used to split the sample set into the raw train set of 699 samples and the test set of 234 samples. 511 samples with a compression ratio of 26.90% and the random train set with the same compression ratio were built based on training samples. Compared these classifiers trained by three train sets, the model’s accuracy established by the optimal train set is 96.6%, which are 4.7% and 6.4% higher than the raw and random train sets. And the average precision and recall of four origins are higher 6% than the raw and random train set. Therefore, the prediction accuracy of apple origins classification model is improved by LARIS. The LARIS method enriches the application and it provides an experimental support for the least angle regression algorithm in instance selection.

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“…The SQA approach, i.e., the definition of certain signal quality indexes, has gained large attention in scientific literature up to today [ 29 , 30 ]. A variety of SQA approaches have been proposed; the majority include classifying the signals to fulfill certain conditions—in trivial cases: acceptable or unacceptable (e.g., [ 31 , 32 ]. However, approaches where more conditions have been proposed exist (e.g., [ 33 ]).…”

Section: Signal Quality Assessment For Ecg Signalsmentioning

confidence: 99%

“…However, approaches where more conditions have been proposed exist (e.g., [ 33 ]). Today, the machine learning and heuristic-classifiers-based approaches for SQA of ECG signals constitute the vast amount of research literature [ 22 , 32 ].…”

Section: Signal Quality Assessment For Ecg Signalsmentioning

confidence: 99%

Non-Standard Electrode Placement Strategies for ECG Signal Acquisition

Metshein

Krivošei

Abdullayev

et al. 2022

Sensors

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Background: Wearable technologies for monitoring cardiovascular parameters, including electrocardiography (ECG) and impedance cardiography (ICG), propose a challenging research subject. The expectancy for wearable devices to be unobtrusive and miniaturized sets a goal to develop smarter devices and better methods for signal acquisition, processing, and decision-making. Methods: In this work, non-standard electrode placement configurations (EPC) on the thoracic area and single arm were experimented for ECG signal acquisition. The locations were selected for joint acquisition of ECG and ICG, targeted to suitability for integrating into wearable devices. The methodology for comparing the detected signals of ECG was developed, presented, and applied to determine the R, S, and T waves and RR interval. An algorithm was proposed to distinguish the R waves in the case of large T waves. Results: Results show the feasibility of using non-standard EPCs, manifesting in recognizable signal waveforms with reasonable quality for post-processing. A considerably lower median sensitivity of R wave was verified (27.3%) compared with T wave (49%) and S wave (44.9%) throughout the used data. The proposed algorithm for distinguishing R wave from large T wave shows satisfactory results. Conclusions: The most suitable non-standard locations for ECG monitoring in conjunction with ICG were determined and proposed.

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Representativeness consideration in the selection of classification algorithms for the ECG signal quality assessment

Cited by 11 publications

References 25 publications

A novel algorithm to assess the quality of 12-lead ECG recordings: validation in a real telecardiology application

A novel algorithm to assess the quality of 12-lead ECG recordings: validation in a real telecardiology application

Research on Apple Origins Classification Optimization Based on Least Angle Regression in Instance Selection

Non-Standard Electrode Placement Strategies for ECG Signal Acquisition

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