Automatic screening method for atrial fibrillation based on lossy compression of the electrocardiogram signal

Abstract

Objective: Compressed sensing (CS) is a low-complexity compression technology that has recently been proposed. It can be applied to long-term electrocardiogram (ECG) monitoring using wearable devices. In this study, an automatic screening method for atrial fibrillation (AF) based on lossy compression of the electrocardiogram signal is proposed. Approach: The proposed method combines the CS with the convolutional neural network (CNN). The sparse binary sensing matrix is first used to project the raw ECG signal randomly, transforming the raw ECG data from high-dimensional space to low-dimensional space to complete compression, and then using CNN to classify the compressed ECG signal involving AF. Our proposed model is validated on the MIT-BIH atrial fibrillation database. Main results: The experimental results show that the model only needs about 1 s to complete the 24 h ECG recording of AF, which is 3.41%, 69.84% and 67.56% less than the time required by AlexNet, VGGNet and GoogLeNet, respectively. Under different compression ratios of 10% to 90%, the maximum and minimum F1 scores reach 96.25% and 88.17%, respectively. Significance: The CS-CNN (compressed sensing convolutional neural network) model has high computational efficiency while ensuring prediction accuracy, and is a promising method for AF screening in wearable application scenarios.

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

“…After a full text review, 102 studies in total were included in the qualitative review. 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 …”

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

“…Although their proposed method alleviates the adverse impact of signal reconstruction on real-time performance, the accuracy of AF detection is significantly lower than that of AF detection on the reconstructed signal at the same compression ratio. In addition, Zhang et al [12] also eliminated the signal reconstruction process and proposed an end-to-end AF detection method for compressed ECG signals based on deep learning. They directly input the compressed ECG signal segments into the designed convolutional neural network model and output the result of AF detection at the end of the model.…”

“…Table 4 shows the classification performance of the proposed method when using different measurement matrices to compress the ECG. The results of Zhang's [12] method on different measurement matrices are also listed in the table for comparison. It can be seen that, when using the same measurement matrix, the classification performance of the proposed method on most evaluation metrics is better than that of Zhang's method.…”

“…Therefore, the sparse binary matrix is more suitable for practical applications. [12] when using the same measurement matrix. For the same method, there is no significant difference in classification performance using different measurement matrices.…”

“…However, the RR intervals extracted by Da Poian's method at a higher compression ratio is not accurate, resulting in unsatisfactory classification performance for AF detection. In addition, Zhang et al [12] proposed a deep learning method, which directly inputs the collected compressed ECG into the neural network model, and then outputs the results of AF detection. In fact, if there is no other prior information, the compressed ECG contains much less information than the original ECG, so it is difficult to obtain satisfactory classification performance by only relying on the compressed ECG data.…”