Name
Title | ||
|---|---|---|
A Real-Time Automated Point-Process Method for the Detection and Correction of Erroneous and Ectopic Heartbeats |
Abstract | ||
|---|---|---|
The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affects estimation of both time and frequency domain indices of heart rate variability (HRV). A reliable, real-time classification and correction of ECG-derived heartbeats is a necessary prerequisite for an accurate online monitoring of HRV and cardiovascular control. We have developed a novel point-process-based method for real-time R-R interval error detection and correction. Given an R-wave event, we assume that the length of the next R-R interval follows a physiologically motivated, time-varying inverse Gaussian probability distribution. We then devise an instantaneous automated detection and correction procedure for erroneous and arrhythmic beats by using the information on the probability of occurrence of the observed beat provided by the model. We test our algorithm over two datasets from the PhysioNet archive. The Fantasia normal rhythm database is artificially corrupted with known erroneous beats to test both the detection procedure and correction procedure. The benchmark MIT-BIH Arrhythmia database is further considered to test the detection procedure of real arrhythmic events and compare it with results from previously published algorithms. Our automated algorithm represents an improvement over previous procedures, with best specificity for the detection of correct beats, as well as highest sensitivity to missed and extra beats, artificially misplaced beats, and for real arrhythmic events. A near-optimal heartbeat classification and correction, together with the ability to adapt to time-varying changes of heartbeat dynamics in an online fashion, may provide a solid base for building a more reliable real-time HRV monitoring device. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TBME.2012.2211356 | Biomedical Engineering, IEEE Transactions |
Keywords | Field | DocType |
gaussian distribution,cardiovascular system,computerised monitoring,diseases,electrocardiography,error correction,error detection,medical signal detection,medical signal processing,real-time systems,signal classification,time-frequency analysis,ecg-derived heartbeats,fantasia normal rhythm database,physionet archive,artificially misplaced beats,benchmark mit-bih arrhythmia database,cardiac dysrhythmia,cardiovascular control,ectopic heartbeat correction,ectopic heartbeat detection,erroneous heartbeat correction,erroneous heartbeat detection,heart rate variability,heartbeat dynamics,instantaneous automated correction,instantaneous automated detection,irregular heartbeats,near-optimal heartbeat classification,near-optimal heartbeat correction,online monitoring,physiologically motivated time-varying inverse gaussian probability distribution,point-process-based method,probability,real-time r-r interval error correction,real-time r-r interval error detection,real-time automated point-process method,real-time classification,recurring arrhythmic events,reliable real-time hrv monitoring device,sensitivity,signal quality,time-frequency domain indices,arrhythmias,ectopic beats,erroneous r–r intervals,point processes | Data mining,Computer science,Point process,Ectopic Heartbeats | Journal |
Volume | Issue | ISSN |
59 | 10 | 0018-9294 |
Citations | PageRank | References |
13 | 1.36 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| luca citi | 1 | 168 | 27.88 |
| Emery N. Brown | 2 | 1019 | 151.59 |
| Riccardo Barbieri | 3 | 460 | 70.50 |