Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation

Background: Brief episodes of atrial fibrillation (AF) may evolve into longer AF episodes increasing the chances of thrombus formation, stroke, and death. Classical methods for AF detection investigate rhythm irregularity or P-wave absence in the ECG, while deep learning approaches profit from the availability of annotated ECG databases to learn discriminatory features linked to different diagnosis. However, some deep learning approaches do not provide analysis of the features used for classification. This paper introduces a convolutional neural network (CNN) approach for automatic detection of brief AF episodes based on electrocardiomatrix-images (ECM-images) aiming to link deep learning to features with clinical meaning.Materials and Methods: The CNN is trained using two databases: the Long-Term Atrial Fibrillation and the MIT-BIH Normal Sinus Rhythm, and tested on three databases: the MIT-BIH Atrial Fibrillation, the MIT-BIH Arrhythmia, and the Monzino-AF. Detection of AF is done using a sliding window of 10 beats plus 3 s. Performance is quantified using both standard classification metrics and the EC57 standard for arrhythmia detection. Layer-wise relevance propagation analysis was applied to link the decisions made by the CNN to clinical characteristics in the ECG.Results: For all three testing databases, episode sensitivity was greater than 80.22, 89.66, and 97.45% for AF episodes shorter than 15, 30 s, and for all episodes, respectively.Conclusions: Rhythm and morphological characteristics of the electrocardiogram can be learned by a CNN from ECM-images for the detection of brief episodes of AF.

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Face Image Quality Assessment: A Literature Survey

ACM Computing Surveys ◽

10.1145/3507901 ◽

2022 ◽

Author(s):

Torsten Schlett ◽

Christian Rathgeb ◽

Olaf Henniger ◽

Javier Galbally ◽

Julian Fierrez ◽

...

Keyword(s):

Deep Learning ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Face Image ◽

Quality Data ◽

Learning Approaches ◽

The Face ◽

Future Work

The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions.

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Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings

2020 Computing in Cardiology Conference (CinC) ◽

10.22489/cinc.2020.367 ◽

2020 ◽

Author(s):

Alvaro Huerta Herraiz ◽

Arturo Martinez-Rodrigo ◽

Miguel Angel Arias ◽

Philip Langley ◽

José J Rieta ◽

...

Keyword(s):

Atrial Fibrillation ◽

Deep Learning ◽

Quality Assessment

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Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

JMIR mhealth and uhealth ◽

10.2196/12770 ◽

2019 ◽

Vol 7 (6) ◽

pp. e12770 ◽

Cited By ~ 17

Author(s):

Soonil Kwon ◽

Joonki Hong ◽

Eue-Keun Choi ◽

Euijae Lee ◽

David Earl Hostallero ◽

...

Keyword(s):

Atrial Fibrillation ◽

Deep Learning ◽

Learning Approaches ◽

Development Study

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Atrial fibrillation frequency tracking in ambulatory ECG signals: The significance of signal quality assessment

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2018.08.027 ◽

2018 ◽

Vol 102 ◽

pp. 227-233 ◽

Cited By ~ 3

Author(s):

Birutė Paliakaitė ◽

Andrius Petrėnas ◽

Mikael Henriksson ◽

Jurgita Skibarkienė ◽

Raimondas Kubilius ◽

...

Keyword(s):

Atrial Fibrillation ◽

Quality Assessment ◽

Signal Quality ◽

Frequency Tracking ◽

Ecg Signals ◽

Ambulatory Ecg

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A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

Entropy ◽

10.3390/e22070733 ◽

2020 ◽

Vol 22 (7) ◽

pp. 733

Author(s):

Álvaro Huerta Herraiz ◽

Arturo Martínez-Rodrigo ◽

Vicente Bertomeu-González ◽

Aurelio Quesada ◽

José J. Rieta ◽

...

Keyword(s):

Atrial Fibrillation ◽

Deep Learning ◽

Quality Assessment ◽

Heart Rhythm ◽

Wearable Devices ◽

Automatic Identification ◽

High Quality ◽

Learning Capability ◽

Ecg Signals ◽

Challenging Environment

Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient’s electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.

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Signal Quality Assessment of F-waves in Atrial Fibrillation

2017 Computing in Cardiology Conference (CinC) ◽

10.22489/cinc.2017.051-153 ◽

2017 ◽

Cited By ~ 2

Author(s):

Mikael Henriksson ◽

Andrius Petrenas ◽

Vaidotas Marozas ◽

Frida Sandberg ◽

Leif Sornmo

Keyword(s):

Atrial Fibrillation ◽

Quality Assessment ◽

Signal Quality

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Deep Learning Approaches for Whiteboard Image Quality Enhancement

Color and Imaging Conference ◽

10.2352/j.imagingsci.technol.2019.63.4.040404 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 360-368

Author(s):

Mekides Assefa Abebe ◽

Jon Yngve Hardeberg

Keyword(s):

Deep Learning ◽

Image Quality ◽

Image Data ◽

Quality Enhancement ◽

Network Architectures ◽

Learning Approaches ◽

Data Set ◽

Image Quality Enhancement ◽

Processing Techniques ◽

White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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Assessment of the Risk Factors of MDD Recurrence Based on Deep Learning Approaches

SSRN Electronic Journal ◽

10.2139/ssrn.3411719 ◽

2019 ◽

Author(s):

Qian Wu ◽

Weiling Zhao ◽

Xiaobo Yang ◽

Hua Tan ◽

Lei You ◽

...

Keyword(s):

Risk Factors ◽

Deep Learning ◽

Learning Approaches

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