Fig 1.
ECG signal compression classification process.
Fig 2.
(a-d) ECG plotted for the first 1000 data of the subject.
Fig 3.
Schematic diagram of heartbeat beat interception centered on the R-peak.
Table 1.
Number of each heartbeat beat category after cutting and the actual number used [49].
Fig 4.
Proposed compression process.
Table 2.
Detailed parameters of the compression network at CR = 0.2.
Fig 5.
Proposed classification network.
Table 3.
Detailed parameters of the classification network at CR = 0.2.
Fig 6.
Variation of model learning rate with epoch at CR = 0.1.
Fig 7.
Training and validation performances using a proposed model with ECG datasets.
Table 4.
Accuracy of different compression methods in the MIT-BIH arrhythmia database test set at different compression ratios.
Fig 8.
Accuracy trends in the MIT-BIH arrhythmia database using different compression methods at different compression ratios.
Fig 9.
Confusion matrix of the proposed model on the MIT-BIH arrhythmia database test set at CR = 0.5.
Fig 10.
Trends in accuracy using different classification methods at different compression ratios in the MIT-BIH arrhythmia database.
Fig 11.
ROC curve of each classification model when CR = 0.05.
Fig 12.
PR curve of each classification model when CR = 0.05.
Table 5.
Accuracy of different classification methods in the MIT-BIH arrhythmia database test set at different compression ratios.
Table 6.
The time (s) spent in parameter training for each classification network.
Table 7.
Average precision, sensitivity and F1-score of each classification network at CR = 0.4.
Table 8.
Average precision, sensitivity and F1-score of each classification network at CR = 0.2.
Fig 13.
Accuracy trends using different compression methods at different compression ratios in the TianChi ECG signal database.
Fig 14.
Confusion matrix of the proposed model on the TianChi ECG signal database test set at CR = 0.1.
Table 9.
Accuracy of different compression methods in TianChi ECG signal database test set at different compression ratios.
Fig 15.
Accuracy trends using different classification methods at different compression ratios in the TianChi ECG signal database.
Table 10.
Accuracy of different classification methods in the TianChi ECG signal database test set at different compression ratios.
Fig 16.
Accuracy trends using different classification methods at different compression ratios in the MIT-BIH ECG signal database.
Table 11.
Accuracy of different classification methods in the MIT-BIH ECG signal database test set at different compression ratios.