Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix

Abstract

In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did not fully benefit from the existing prior information, resulting in unsatisfactory classification performance, especially in some applications that require high compression ratio (CR). In this paper, we propose a deep learning method to detect atrial fibrillation directly from compressed ECG without reconstruction. Specifically, we design a deep network model for one-dimensional ECG signals, and the measurement matrix is used to initialize the first layer of the model so that the proposed model can obtain more prior information which benefits improving the classification performance of atrial fibrillation detection from compressed ECG. The experimental results on the MIT-BIH Atrial Fibrillation Database show that when the CR is 10%, the accuracy and F1 score of the proposed method reach 97.52% and 98.02%, respectively. Compared with the atrial fibrillation detection from original ECG, the corresponding accuracy and F1 score are only reduced by 0.88% and 0.69%. Even at a high CR of 90%, the accuracy and F1 score are still only reduced by 6.77% and 5.31%, respectively. All of the experimental results demonstrate that the proposed method is superior to other existing methods for atrial fibrillation detection from compressed ECG. Therefore, the proposed method is promising for atrial fibrillation detection in wearable health monitoring based on compressed sensing.

Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram for Remote Monitoring

“…The slight increase in the performance at the 75% CR was previously studied, and it was found that the filtering effects of the DBBD matrix aided in highlighting the features of AF (Abdelazez et al, 2021). Poian et al (2017), Cheng et al (2020), and Zhang et al ( 2020) developed an equivalent of Stage 2. At 50% and 75% CR, the proposed Stage 2 precision and accuracy were better than the literature (Table 1).…”

“…Poian et al (2017) utilized the matched filtering technique to detect AF in compressively sensed ECG by locating the heartbeats, extracting RRI features, and using a support vector machine to differentiate ECG with AF from normal ECG. Cheng et al (2020), Zhang et al (2020) proposed using deep learning to detect AF in compressively sensed ECG.…”

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

“…Other techniques to accelerate arrhythmia detection include real-time data compression, signal processing, and data transmission [ 67 , 68 , 69 ]. Alternatively, ECG data may be compressed to enable real-time AF classification [ 70 , 71 ].…”

Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization

“…The first is biological signal processing to detect heart disease, and the second is data mining related to variables that trigger heart disease. Research mainly involves electrocardiogram signals processing for the detection of heart disease (Cheng et al, 2020;Hadiyoso & Rizal, 2017;Pestana et al, 2020) or processing heart sounds (Rizal & Suratman, 2020;Zeinali & Niaki, 2022). Heart imaging techniques through echocardiography are also an alternative for the detection of heart disease (Liastuti et al, 2022;Mabrouk et al, 2016).…”