Independent Detection of T-Waves in Single Lead ECG Signal Using Continuous Wavelet Transform

Sabherwal, Pooja; Agrawal, Monika; Singh, Latika

doi:10.1007/s13239-022-00643-1

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Subsequently, more advanced techniques have been employed to identify also the P and T waves. These include digital signal processing such as the wavelet transform [ 2 – 4 , 15 ], the Hilbert transform [ 16 , 17 ], and the phasor transform [ 18 ]. Additionally, classical machine learning approaches like hidden Markov models [ 19 , 20 ] and Gaussian mixture models [ 21 ] have also been employed.…”

Section: Related Workmentioning

confidence: 99%

Deep learning based ECG segmentation for delineation of diverse arrhythmias

Joung,

Kim,

Paik

et al. 2024

PLoS ONE

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Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T waves have shown promising results, their ability to handle arrhythmias has not been studied in any detail. In this paper we investigate the effect of arrhythmias on delineation quality and develop strategies to improve performance in such cases. We introduce a U-Net-like segmentation model for ECG delineation with a particular focus on diverse arrhythmias. This is followed by a post-processing algorithm which removes noise and automatically determines the boundaries of P, QRS, and T waves. Our model has been trained on a diverse dataset and evaluated against the LUDB and QTDB datasets to show strong performance, with F1-scores exceeding 99% for QRS and T waves, and over 97% for P waves in the LUDB dataset. Furthermore, we assess various models across a wide array of arrhythmias and observe that models with a strong performance on standard benchmarks may still perform poorly on arrhythmias that are underrepresented in these benchmarks, such as tachycardias. We propose solutions to address this discrepancy.

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Section: Related Workmentioning

confidence: 99%

Deep learning based ECG segmentation for delineation of diverse arrhythmias

Joung,

Kim,

Paik

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Subsequently, more advanced techniques have been employed to identify also the P and T waves. These include digital signal processing such as the wavelet transform [2][3][4]15], the Hilbert transform [16,17], and the phasor transform [18]. Additionally, classical machine learning approaches like hidden Markov models [19,20] and Gaussian mixture models [21] have also been employed.…”

Section: Traditional Approaches For Ecg Delineationmentioning

confidence: 99%