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A technology for evaluating the quality of ECG signals, applied in diagnostic recording/measurement, medical science, diagnosis, etc., can solve problems such as error analysis, poor quality ECG records, increase the workload of doctors, etc., and achieve the effect of improving accuracy
Active Publication Date: 2022-04-22
SHANDONG ARTIFICIAL INTELLIGENCE INST +1
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The traditional ECG requires doctors to analyze the characterization information of the patient's ECG signal waveform, but due to the influence of various noises and interferences, a large number of poor-quality ECG records will be generated, which will greatly increase the doctor's workload and even cause errors. Analysis, therefore, requires an automated assessment of the quality of the ECG signal
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Embodiment 1
[0037] Step b) The central electrical signal E(t) is segmented into equal-length segments of 10s, and the segmented ECG signals are marked as acceptable and unacceptable according to the annotation standards of the 2011 PhysioNet / Computingin Cardiology Challenge.
Embodiment 2
[0039] The feature in the statistical feature matrix StaF in step c) includes:
[0040] c-1) Take the maximum R-peak interval as the quality index 1 of the ECG record.
[0041] c-2) The minimum R-peak interval is used as the quality index 2 of the ECG record.
[0042] c-3) The average R peak interval is used as the quality index 3 of the ECG record.
[0043] c-4) The standard deviation of the R peak interval is used as the quality index 4 of the ECG record.
[0044] c-5) The ratio pNN50 of the difference between adjacent R peaks greater than 50 ms is used as the quality index 5 of the ECG record.
[0045] c-6) The R peak density (that is, the number of R peaks / record length) is used as the quality index 6 of the ECG record.
[0046] c-7) The root mean square RMSSD of the difference between adjacent R peaks is used as the quality index 7 of the ECG record.
[0047] c-8) The RR interval sampling entropy, which measures the confusion of R peak interval changes, will be used a...
Embodiment 3
[0050] The S-transform in step d) is the inheritance and development of the wavelet transform and the short-time Fourier transform, which can not only maintain a high high-frequency time resolution, but also maintain a high low-frequency frequency resolution. The formula for the S-transform is: Through this calculation, the time-spectrum matrix S(τ,f) is obtained, where x(t) is the ECG signal to be analyzed, τ is the time shift factor, p is a rational number, i is an imaginary number unit, t is time, and e is an irrational number , f is the frequency.
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Abstract
A method for quality assessment of ECG signals. The time-frequency map obtained by S-transform is combined with the deep features obtained by the integrated neural network of deep residual shrinkage network and convolutional neural network, combined with the extracted statistical features, using the proposed dual Proportional loss function extracts deep features and combines them with statistical features to evaluate the quality of ECG signals to solve the problem of poor quality of ECG signals in practical applications, which can greatly improve the quality evaluation of 12-lead ECG signals the accuracy rate. The residual shrinkage network can delete a lot of original information that is irrelevant to the current task in the data through soft threshold processing to obtain the most relevant information to the current task. The convolutional neural network has the advantages of parameter sharing and sparse connection, and can further refine and optimize the deep features extracted by the residual shrinkage network to obtain the most suitable deep features for the current task.
Description
technical field [0001] The invention relates to the technical field of electrocardiographic signal processing, in particular to a method for evaluating the quality of electrocardiographic signals. Background technique [0002] Electrocardiogram (ECG) signal is of great significance as a comprehensive reflection of heart activity. The traditional ECG requires doctors to analyze the characterization information of the patient's ECG signal waveform, but due to the influence of various noises and interferences, a large number of poor-quality ECG records will be generated, which will greatly increase the doctor's workload and even cause errors. Analysis, therefore, requires an automated assessment of the quality of the ECG signal. Contents of the invention [0003] In order to overcome the deficiencies of the above technologies, the present invention provides a method that combines a deep residual shrinkage network and a convolutional neural network to greatly improve the accu...
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