Evaluation of Machine Learning Techniques for ECG T-Wave Alternans

Karnaukh, Oleksandra; Karplyuk, Yevgeniy; Nataliia, Nikitiuk

doi:10.1109/elnano.2018.8477528

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…To date, we have not found previous works tackling TWA detection based on ML methods published in international indexed journals, but two conference papers [32] , [33] reporting an F1 score of 89% and 95.9% respectively, with the Physionet TWA dataset [34] , [53] . Although this database is of great value, it may not be appropriate for ML methods since it contains different segments from the same patients, and they are unidentified.…”

Section: Discussionmentioning

confidence: 99%

“…There are few attempts to address the TWA detection problem employing ML and DL. In [32] different ML classifiers are tested using the T-Wave alternans Database from PhysioNet website. However, this database is not labeled but ranked according to the level of T–wave alternans present in the ECG, therefore the authors had to set a threshold on this rank in order to have classification labels (+ TWA and - TWA).…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning approach for TWA detection relying on ensemble data design

Fernández–Calvillo

Goya–Esteban

Cruz–Roldán

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning approach for TWA detection relying on ensemble data design

Fernández–Calvillo

Goya–Esteban

Cruz–Roldán

et al. 2023

Heliyon

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“…Machine learning algorithms, such as support vector machines (SVMs), 678 artificial neural networks (ANNs), 679 and deep learning models, 680 have been applied for ECG signal classification tasks, including arrhythmia detection, 681 my-ocardial infarction diagnosis, 682 and heart rate variability (HRV) analysis. 683 Various features, such as ventricular depolarization, 684 ST segment, 685 and T-wave 686 characteristics, are extracted and used as inputs for these algorithms. Specifically, HRV analysis provides insights into autonomic nervous system activity and cardiovascular health.…”

Section: Machine Learning and Algorithm Developmentmentioning

confidence: 99%

Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare

Gong,

Lu,

Yin

et al. 2024

Chem. Rev.

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In the era of Internet-of-things, many things can stay connected; however, biological systems, including those necessary for human health, remain unable to stay connected to the global Internet due to the lack of soft conformal biosensors. The fundamental challenge lies in the fact that electronics and biology are distinct and incompatible, as they are based on different materials via different functioning principles. In particular, the human body is soft and curvilinear, yet electronics are typically rigid and planar. Recent advances in materials and materials design have generated tremendous opportunities to design soft wearable bioelectronics, which may bridge the gap, enabling the ultimate dream of connected healthcare for anyone, anytime, and anywhere. We begin with a review of the historical development of healthcare, indicating the significant trend of connected healthcare. This is followed by the focal point of discussion about new materials and materials design, particularly low-dimensional nanomaterials. We summarize material types and their attributes for designing soft bioelectronic sensors; we also cover their synthesis and fabrication methods, including top-down, bottom-up, and their combined approaches. Next, we discuss the wearable energy challenges and progress made to date. In addition to front-end wearable devices, we also describe back-end machine learning algorithms, artificial intelligence, telecommunication, and software. Afterward, we describe the integration of soft wearable bioelectronic systems which have been applied in various testbeds in real-world settings, including laboratories that are preclinical and clinical environments. Finally, we narrate the remaining challenges and opportunities in conjunction with our perspectives.

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“…Sequential backward floating search (SBFS) is a well-known feature selection method which has been used to process various physiological signals (Tork et al, 2013;Karnaukh et al, 2018;Ahirwal, 2021) and to perform body state assessments (Dreißig et al, 2020). In this paper, SBFS is utilized in EEG channel selection for MI-based BCI.…”

Section: Introductionmentioning

confidence: 99%

EEG channel selection based on sequential backward floating search for motor imagery classification

Tang

Gao

et al. 2022

Front. Neurosci.

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Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods.

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Evaluation of Machine Learning Techniques for ECG T-Wave Alternans

Cited by 4 publications

References 11 publications

Machine Learning approach for TWA detection relying on ensemble data design

Machine Learning approach for TWA detection relying on ensemble data design

Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare

EEG channel selection based on sequential backward floating search for motor imagery classification

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