Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model

Abstract

Objectives Biosignal data captured by patient monitoring systems could provide key evidence for detecting or predicting critical clinical events; however, noise in these data hinders their use. Because deep learning algorithms can extract features without human annotation, this study hypothesized that they could be used to screen unacceptable electrocardiograms (ECGs) that include noise. To test that, a deep learning-based model for unacceptable ECG screening was developed, and its screening results were compared with the interpretations of a medical expert. Methods To develop and apply the screening model, we used a biosignal database comprising 165,142,920 ECG II (10-second lead II electrocardiogram) data gathered between August 31, 2016 and September 30, 2018 from a trauma intensive-care unit. Then, 2,700 and 300 ECGs (ratio of 9:1) were reviewed by a medical expert and used for 9-fold cross-validation (training and validation) and test datasets. A convolutional neural network-based model for unacceptable ECG screening was developed based on the training and validation datasets. The model exhibiting the lowest cross-validation loss was subsequently selected as the final model. Its performance was evaluated through comparison with a test dataset. Results When the screening results of the proposed model were compared to the test dataset, the area under the receiver operating characteristic curve and the F1-score of the model were 0.93 and 0.80 (sensitivity = 0.88, specificity = 0.89, positive predictive value = 0.74, and negative predictive value = 0.96). Conclusions The deep learning-based model developed in this study is capable of detecting and screening unacceptable ECGs efficiently.

Deep Learning in Physiological Signal Data: A Survey

“…For example, emotion classification [126] analyzes emotion into groups of sad, happy, neutral, and fear. The feature-extraction task [43] focuses on input data enhancement, in which the unsupervised learning technique is used to label the dataset to avoid a heavy burden from manual labeling. The data compression task [33] focuses on decreasing the data size while still retaining the high quality of data for storage and transmission.…”

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

“…Although a good classification result was reported, the authors only selected about 5000 out of 18,000 ECG excerpts available from the PhysioNet/CinC Challenge 2011 database, thus ignoring AF and other atrial arrhythmias. Yoon et al [ 23 ] introduced four models based on combining two similar 1-D CNNs to work in parallel, such that one network was fed with the original ECG and another one with its spectral distribution. Discerning between 2700 ECG segments divided into two groups (acceptable and non-acceptable for further diagnosis), the best model achieved an accuracy about 88%.…”

“…Moreover, strong artifacts and interferences have also been identified as responsible for most false alarms of AF occurrence in real-time ECG monitoring systems [ 21 , 22 ]. To palliate these problems, many algorithms have recently been proposed for ECG denoising [ 23 ]. However, their performance has been limited, since time and frequency components of most noises overlap with the ECG signal [ 24 ].…”

“…On the other hand, although most previous ECG quality indices have reported promising results on recordings from healthy subjects, their performance on signals acquired from patients with different pathological cardiac conditions has shown to be fairly limited [ 31 ]. This is the case of many algorithms whose ability to discern between clean and noisy ECG excerpts has been significantly decreased when dealing with ECG recordings obtained from patients with atrial arrhythmias, including AF [ 23 , 30 , 32 , 33 ]. Hence, the present work aims at introducing a novel algorithm for quality assessment of single-lead ECG recordings acquired with portable and wearable devices from patients with intermittent AF.…”

Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

“…To prevent noise interference, several approaches have been proposed to denoise ECG signals based on adaptive filtering [5][6][7], wavelet methods [8,9], and empirical mode decomposition [10,11]. However, all these proposed techniques require analytical calculation and high computation; also, because cut-off processing can lose clinically essential components of the ECG signal, these techniques run the risk of misdiagnosis [12]. Currently, one machine learning (ML) technique, named denoising autoencoders (DAEs), can be applied to reconstruct clean data from its noisy version.…”