An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database
Computers and Biomedical Research
Adaptive wavelet network for multiple cardiac arrhythmias recognition
Expert Systems with Applications: An International Journal
Extension of a Kernel-Based Classifier for Discriminative Spoken Keyword Spotting
Neural Processing Letters
| Hi-index | 12.05 |
The presented study describes a false-alarm probability-FAP bounded solution for detecting and quantifying Heart Rate Turbulence (HRT) major parameters including heart rate (HR) acceleration/deceleration, turbulence jump, compensatory pause value and HR recovery rate. To this end, first, high resolution multi-lead holter electrocardiogram (ECG) signal is appropriately pre-processed via Discrete Wavelet Transform (DWT) and then, a fixed sample size sliding window is moved on the pre-processed trend. In each slid, the area under the excerpted segment is multiplied by its curve-length to generate the Area Curve Length (ACL) metric to be used as the ECG events detection-delineation decision statistic (DS). The ECG events detection-delineation algorithm was applied to various existing databases and as a result, the average values of sensitivity and positive predictivity Se=99.95% and P+=99.92% were obtained for the detection of QRS complexes, with the average maximum delineation error of 7.4 msec, 4.2 msec and 8.3 msec for P-wave, QRS complex and T-wave, respectively. Because the heart-rate time series might include fast fluctuations which don't follow a premature ventricular contraction (PVC) causing high-level false alarm probability (false positive detections) of HRT detection, based on the binary two-dimensional Neyman-Pearson radius test (which is a FAP-bounded classifier), a new method for discrimination of PVCs from other beats using the geometrical-based features is proposed. The statistical performance of the proposed HRT detection-quantification algorithm was obtained as Se=99.94% and P+=99.85% showing marginal improvement for the detection-quantification of this phenomenon. In summary, marginal performance improvement of ECG events detection-delineation process, high performance PVC detection and isolation from noisy holter data and reliable robustness against holter strong noise and artifacts can be mentioned as important merits and capabilities of the proposed HRT detection algorithm.