TP-CNN: A Detection Method for atrial fibrillation based on transposed projection signals with compressed sensed ECG. (October 2021)

• Record Type: Journal Article
• Authors: Zhang, Hongpo; Dong, Zhongren; Sun, Mengya; Gu, Hongzhuang; Wang, Zongmin
• Is Part Of: Computer methods and programs in biomedicine. Volume 210(2021)
• Publication Date: 2021-10
• Subjects: Electrocardiogram -- Compressed sensing -- Atrial fibrillation -- Deep learning -- Convolutional neural network; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106358
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Inspired by transposed convolution, we obtained approximate ECG signals of original ECG signals based on SDMM, and effectively detected atrial fibrillation on the approximate ECG signals We verify the similarity between the approximate signal and the original signal. The experimental results show that the approximate ECG signal can be used to detect atrial fibrillation effectively. TP-CNN is proposed to classify approximate signals. When the compressed signals are 1/10 of the original signals, competitive results can still be obtained compared with the related works of AF detection of the original signals. Abstract: Background and Objective: Atrial fibrillation (AF) is the most prevalent arrhythmia, which increases the mortality of several complications. The use of wearable devices to detect atrial fibrillation is currently attracting a great deal of attention. Patients use wearable devices to continuously collect individual ECG signals and transmit them to the cloud for diagnosis. However, the ECG acquisition and transmission of wearable devices consumes a lot of energy. In order to solve this problem, some scholars have skipped the complex reconstruction process of compressed ECG signals and directly classified the compressed ECG signals, but the AF recognition rate is not high by this method. There is no explanation as to why the compressed ECG signals can be used for AF detection. Methods: Firstly, a simple deterministic measurement matrix (SDMM) is used to perform random projection operation on the ECG signals to complete the compression. Then, we use the transpose of the SDMM to perform transpose projection operation on the compressed signals in the cloud to obtain the approximate signals. We verify the similarity between the approximate ECG signal and the original ECG signal to explain why the compressed ECG signals are effective in AF detection. Finally, the Transposed Projection - Convolutional Neural Network (TP-CNN) is used to effectively detect AF on the obtained approximate ECG signals. Our proposed method is validated in the MIT-BIH AFDB. Results: The experimental results show that when compression ratios (CRs) are from 2 to 10, the average Pearson correlation coefficients between the approximate signals and the original signals are from 0.9867 to 0.8326, the average cosine similarities between the four frequency domain-based HRV features (including mean RR, RMSSD, SDNN and R density) are from 1.00 to 0.9958, from 1.00 to 0.9959, from 0.9978 to 0.8619 and from 0.9982 to 0.8707, respectively. Furthermore, when C R = 10 (ECG was compressed to 1/10 of the original signal), the accuracy, specificity, f1 score and matthews correlation coefficient for AF detection of approximate signals were 99.32%, 99.43%, 99.14% and 98.57%, respectively. Conclusion: Our proposed method illustrates the approximate signals have significant characteristics of the original signals and they are valid to classify the approximate signals. Meanwhile, comparing with the state-of-the-art methods, TP-CNN exceeded the results of the method for compressed signals and were also competitive compared with the classification results of the original signals, and is a promising method for AF detection in wearable application scenarios.