P-QRS-T localization in ECG using deep learning

Abrishami, Hedayat; Campbell, Matthew; Han, Chia Y.; Czosek, Richard J.; Zhou, Xin‐Da

doi:10.1109/bhi.2018.8333406

Cited by 19 publications

(15 citation statements)

References 17 publications

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“…The proposed algorithm scored a lower sensitivity and a higher +P than that achieved using wavelet transform in [35,37,39]. Fortunately, our proposed algorithm achieved a higher accuracy than that achieved by the ConvNet proposed in [44]. Moreover, the proposed algorithm's performance is comparable to other algorithm performances found in the literature [53].…”

Section: Manually Annotated Qt Databasementioning

confidence: 47%

“…This output was then evaluated using the automatically annotated QT database. In addition, the proposed algorithm was applied to the manually annotated beats in the QT database and the results were compared to [35,37,39,40,44]. The authors in [35,37] detected P-waves depending on wavelet transform.…”

Section: Performance Validation and Discussionmentioning

confidence: 99%

“…However, they only detect monophasic P-waves and are not suitable for biphasic waves. In [44], three different deep neural network architectures were implemented and their results were compared to each other's, showing that the best P-wave detection architecture is the ConvNet.…”

Section: Performance Validation and Discussionmentioning

confidence: 99%

“…When comparing the output to the manual annotation, the detection performance over the manually annotated QT database obtained an average SE of 97.24%, a +P of 97.22%, a training accuracy of 99.92%, and a test accuracy of 98.9%. Table 6 summarizes the performance comparison of the proposed P-wave detection algorithm over the manually annotated QT database along with [35,37,39,40,44]. The comparison is in terms of the number of beats, the methodology, SE, and accuracy.…”

Section: Manually Annotated Qt Databasementioning

confidence: 99%

“…Although the algorithm in [42] achieved high sensitivity, it was not validated with any standard database. In [43][44][45], the authors introduced deep neural networks for P-wave detection. The authors of [43], used a neural network with asymmetric basis functions for the purpose of extracting the features of P-waves in the MIT-BIH Arrhythmia Database [27], but the detection rate is not mentioned.…”

Section: Introductionmentioning

confidence: 99%

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P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

et al. 2020

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Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist.

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