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10.1109-JBHI.2021.3100425 |
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|a 21682194 (ISSN)
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|a Interactive Effects of HRV and P-QRS-T on the Power Density Spectra of ECG Signals
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|b Institute of Electrical and Electronics Engineers Inc.
|c 2021
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|z View Fulltext in Publisher
|u https://doi.org/10.1109/JBHI.2021.3100425
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|a Different from the traditional methods of assessing the cardiac activities through heart rhythm statistics or P-QRS-T complexes separately, this study demonstrates their interactive effects on the power density spectrum (PDS) of ECG signal with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases. Firstly, a mathematical model of the PDS of ECG signal with a random pacing pulse train (PPT) mimicking S-A node firings was derived. Secondly, an experimental PDS analysis was performed on clinical ECG signals from 49 STEMI patients and 42 healthy subjects in PTB Diagnostic Database. It was found that besides the interactive effects which are consistent between theoretical and experimental results, the ECG PDSs of STEMI patients exhibited consistently significant power shift towards lower frequency range in ST-elevated leads in comparison with those of reference leads and leads of health subjects with the highest median frequency shift ratios at 51.39 ± 12.94% found in anterior MI. Thirdly, the results of ECG simulation with systematic changes in PPT firing statistics over various lengths of ECG data ranging from 10 s to 60 mins revealed that the mean and median frequency parameters were less affected by the heart rhythm statistics and the data length but more depended on the alterations of P-QRS-T complexes, which were further confirmed on 33 more STEMI patients in European ST-T Database, demonstrating that the frequency indexes could be potentially used as alternative indicators for STEMI diagnosis even with ultra-short-term ECG recordings suitable for wearable and mobile health applications in living-free environments. © 2013 IEEE.
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|a anterior myocardial infarction
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|a Article
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|a controlled study
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|a decision tree
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|a Diagnostic database
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|a ECG
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|a electrocardiogram
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|a electrocardiography
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|a Electrocardiography
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|a Electrocardiography
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|a female
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|a Frequency parameters
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|a glucose blood level
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|a Heart
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|a heart contraction
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|a heart infarction
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|a heart rate
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|a Heart Rate
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|a Heart rate variability
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|a human
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|a human experiment
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|a Humans
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|a Interactive effect
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|a male
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|a mathematical model
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|a mental stress
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|a Mobile health application
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|a myocardial infarction
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|a Myocardial Infarction
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|a Myocardial Infarction
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|a P wave
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|a parasympathetic tone
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|a power density spectrum
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|a Power density spectrum
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|a Q wave
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|a R wave
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|a sensitivity and specificity
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|a Sensitivity and Specificity
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|a signal processing
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|a simulation
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|a skin conductance
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|a spectroscopy
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|a ST segment elevation myocardial infarction
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|a STEMI
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|a St-segment elevations
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|a Systematic changes
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|a T wave
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|a thorax pain
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|a Clifton, D.A.
|e author
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|a Ji, N.
|e author
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|a Lu, L.
|e author
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|a Xiang, T.
|e author
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|a Zhang, Y.-T.
|e author
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|t IEEE Journal of Biomedical and Health Informatics
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