Interactive Effects of HRV and P-QRS-T on the Power Density Spectra of ECG Signals

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...

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Bibliographic Details
Main Authors: Clifton, D.A (Author), Ji, N. (Author), Lu, L. (Author), Xiang, T. (Author), Zhang, Y.-T (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
ECG
Online Access:View Fulltext in Publisher
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020 |a 21682194 (ISSN) 
245 1 0 |a Interactive Effects of HRV and P-QRS-T on the Power Density Spectra of ECG Signals 
260 0 |b Institute of Electrical and Electronics Engineers Inc.  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1109/JBHI.2021.3100425 
520 3 |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. 
650 0 4 |a anterior myocardial infarction 
650 0 4 |a Article 
650 0 4 |a controlled study 
650 0 4 |a decision tree 
650 0 4 |a Diagnostic database 
650 0 4 |a ECG 
650 0 4 |a electrocardiogram 
650 0 4 |a electrocardiography 
650 0 4 |a Electrocardiography 
650 0 4 |a Electrocardiography 
650 0 4 |a female 
650 0 4 |a Frequency parameters 
650 0 4 |a glucose blood level 
650 0 4 |a Heart 
650 0 4 |a heart contraction 
650 0 4 |a heart infarction 
650 0 4 |a heart rate 
650 0 4 |a Heart Rate 
650 0 4 |a Heart rate variability 
650 0 4 |a human 
650 0 4 |a human experiment 
650 0 4 |a Humans 
650 0 4 |a Interactive effect 
650 0 4 |a male 
650 0 4 |a mathematical model 
650 0 4 |a mental stress 
650 0 4 |a Mobile health application 
650 0 4 |a myocardial infarction 
650 0 4 |a Myocardial Infarction 
650 0 4 |a Myocardial Infarction 
650 0 4 |a P wave 
650 0 4 |a parasympathetic tone 
650 0 4 |a power density spectrum 
650 0 4 |a Power density spectrum 
650 0 4 |a Q wave 
650 0 4 |a R wave 
650 0 4 |a sensitivity and specificity 
650 0 4 |a Sensitivity and Specificity 
650 0 4 |a signal processing 
650 0 4 |a simulation 
650 0 4 |a skin conductance 
650 0 4 |a spectroscopy 
650 0 4 |a ST segment elevation myocardial infarction 
650 0 4 |a STEMI 
650 0 4 |a St-segment elevations 
650 0 4 |a Systematic changes 
650 0 4 |a T wave 
650 0 4 |a thorax pain 
700 1 |a Clifton, D.A.  |e author 
700 1 |a Ji, N.  |e author 
700 1 |a Lu, L.  |e author 
700 1 |a Xiang, T.  |e author 
700 1 |a Zhang, Y.-T.  |e author 
773 |t IEEE Journal of Biomedical and Health Informatics