A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices
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 d...
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| Format: | Article |
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MDPI AG
2020-07-01
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| Online Access: | https://www.mdpi.com/1099-4300/22/7/733 |
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| author | Álvaro Huerta Herraiz Arturo Martínez-Rodrigo Vicente Bertomeu-González Aurelio Quesada José J. Rieta Raúl Alcaraz |
| author_facet | Álvaro Huerta Herraiz Arturo Martínez-Rodrigo Vicente Bertomeu-González Aurelio Quesada José J. Rieta Raúl Alcaraz |
| author_sort | Álvaro Huerta Herraiz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:45:13Z |
| format | Article |
| id | doaj.art-1f6a28abda654abbb60e5c76861dfd39 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T18:45:13Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-1f6a28abda654abbb60e5c76861dfd392023-11-20T05:33:03ZengMDPI AGEntropy1099-43002020-07-0122773310.3390/e22070733A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable DevicesÁlvaro Huerta Herraiz0Arturo Martínez-Rodrigo1Vicente Bertomeu-González2Aurelio Quesada3José J. Rieta4Raúl Alcaraz5Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, SpainResearch Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, SpainClinical Medicine Department, Miguel Hernandez University, 03202 Elche, SpainCardiology Department, Hospital General Universitario de Valencia, 46014 Valencia, SpainBioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, SpainResearch Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, SpainAtrial 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.https://www.mdpi.com/1099-4300/22/7/733atrial fibrillationcontinuous wavelet transformconvolutional neural networkdeep learningquality assessmentsingle-lead ECG |
| spellingShingle | Álvaro Huerta Herraiz Arturo Martínez-Rodrigo Vicente Bertomeu-González Aurelio Quesada José J. Rieta Raúl Alcaraz A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices Entropy atrial fibrillation continuous wavelet transform convolutional neural network deep learning quality assessment single-lead ECG |
| title | A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices |
| title_full | A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices |
| title_fullStr | A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices |
| title_full_unstemmed | A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices |
| title_short | A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices |
| title_sort | deep learning approach for featureless robust quality assessment of intermittent atrial fibrillation recordings from portable and wearable devices |
| topic | atrial fibrillation continuous wavelet transform convolutional neural network deep learning quality assessment single-lead ECG |
| url | https://www.mdpi.com/1099-4300/22/7/733 |
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