Deep Learning for ECG Segmentation

Moskalenko, V. A.; Zolotykh, Nikolai Yu.; Osipov, Grigory V.

doi:10.1007/978-3-030-30425-6_29

Cited by 35 publications

(27 citation statements)

References 7 publications

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“…Our original ECG is a 10-s 12-lead signal with a frequency of 500 Hz. Using the segmentation algorithms described by Moskalenko et al ( 2019 ), we determine beginnings and endings of all P and T waves and all the picks R. Then, we do the step forward and backward from the R pick at an equal distance. Thus, we obtain the set of cardiac cycles, each of which of vectors length is 400 (800 ms).…”

Section: Algorithmmentioning

confidence: 99%

Interpretable Feature Generation in ECG Using a Variational Autoencoder

et al. 2021

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We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Our goal was to encode the original ECG signal using as few features as possible. Using this method we extracted a vector of new 25 features, which in many cases can be interpreted. The generated ECG has quite natural appearance. The low value of the Maximum Mean Discrepancy metric, 3.83 × 10−3, indicates good quality of ECG generation too. The extracted new features will help to improve the quality of automatic diagnostics of cardiovascular diseases. Generating new synthetic ECGs will allow us to solve the issue of the lack of labeled ECG for using them in supervised learning.

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Section: Algorithmmentioning

confidence: 99%

Interpretable Feature Generation in ECG Using a Variational Autoencoder

et al. 2021

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“…We believe that architectures like U-net [37] will allow achieving better results than known algorithms. For some preliminary results from using QTDB to train Unet-like network, see [38].…”

Section: Algorithm Validationmentioning

confidence: 99%

LUDB: A New Open-Access Validation Tool for Electrocardiogram Delineation Algorithms

et al. 2020

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We report Lobachevsky University Database (LUDB) of ECG signals, an open tool for validating ECG delineation algorithms, that is superior to the existing publicly available data bases in several aspects. LUDB contains 200 recordings of 10-second 12-lead electrocardiograms (ECG) from different subjects, representative of a variety of signal morfologies. The boundaries and peaks of QRS complexes and P and T waves are manually annotated by cardiologists for all recordings and independently for each lead, and all records received an expert classification by abnormalities. We present a case study for the recently proposed wavelet-based algorithm and the broadly used ecg-kit tool, and demonstrate the advantage of multi-lead ECG data analysis. LUDB contributes to the diversity of public databases employed in developing and validating novel ECG analysis algorithms, including the most advanced based on deep learning neural networks.

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“…The MAB problem is a definitive problem that reveals capacities with exploration counter to the exploitation predicament of medicinal computations. There exist numerous techniques to unravel the MAB including working without explorations meaning that the supreme naive tactics and customary explorations at random and explorations vigorously with a predilection to hesitation ECG segmentation in [47]. The medical dilemma situations tend to be incomplete information gathered by traditional means, having gathered adequate material to make the preeminent inclusive resolutions though guardianship the risk under regulator possibilities.…”

Section: The Multiarmed Banditmentioning

confidence: 99%

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

Shafik

Matinkhah

Ahmad

2020

Wireless Communications and Mobile Computing

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The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, and search and rescue operations among others. The current technologies are using fixed base stations to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. This open gate for the UAV technology is to be used as a mobile alternative to increase accessibility with fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general and provides detailed steps that are involved in the multiarmed bandit (MAB) approach in solving the UAV biomedical engineering technology device and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal performance and efficiency of medical engineered devices. The simulated results depicted that a multiarmed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technology systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.

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Deep Learning for ECG Segmentation

Cited by 35 publications

References 7 publications

Interpretable Feature Generation in ECG Using a Variational Autoencoder

Interpretable Feature Generation in ECG Using a Variational Autoencoder

LUDB: A New Open-Access Validation Tool for Electrocardiogram Delineation Algorithms

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

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