Localization of premature ventricular contraction foci in normal individuals based on multichannel electrocardiogram signals processing.

A premature ventricular contraction (PVC) is relatively a common event where the heartbeat is initiated by the other pathway rather than by the Sinoatrial node, the normal heartbeat initiator. Determining PVC foci is important for ablation procedure and it can help in pre-procedural planning and potentially may improve ablation outcome. In this study, 12-lead Electrocardiogram (ECG) of 87 patients without structural cardiac diseases, who had experienced PVC, were obtained.

Optimal delineation of ambulatory holter ECG events via false-alarm bounded segmentation of a wavelet-based principal components analyzed decision statistic.

The aim of this study is to develop and describe a new ambulatory holter electrocardiogram (ECG) events detection-delineation algorithm with the major focus on the bounded false-alarm probability (FAP) segmentation of an information-optimized decision statistic. After implementation of appropriate preprocessing methods to the discrete wavelet transform (DWT) of the original ECG data, a uniform length sliding window is applied to the obtained signal and in each slid, six feature vectors namely as summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second order differentiation, curve length, area and variance of the excerpted segment are calculated to construct a newly proposed principal components analyzed geometric index (PCAGI) by application of a linear orthonormal projection. In the next step, the α-level Neyman-Pearson classifier (which is a FAP controlled tester) is implemented to detect and delineate QRS complexes.

Segmentation of holter ECG waves via analysis of a discrete wavelet-derived multiple skewness-kurtosis based metric.

In this study, a simple mathematical-statistical based metric called Multiple Higher Order Moments (MHOM) is introduced enabling the electrocardiogram (ECG) detection-delineation algorithm to yield acceptable results in the cases of ambulatory holter ECG including strong noise, motion artifacts, and severe arrhythmia(s). In the MHOM measure, important geometric characteristics such as maximum value to minimum value ratio, area, extent of smoothness or being impulsive and distribution skewness degree (asymmetry), occult. In the proposed method, first three leads of high resolution 24-h holter data are extracted and preprocessed using Discrete Wavelet Transform (DWT).

A robust wavelet-based multi-lead Electrocardiogram delineation algorithm.

A robust multi-lead ECG wave detection-delineation algorithm is developed in this study on the basis of discrete wavelet transform (DWT). By applying a new simple approach to a selected scale obtained from DWT, this method is capable of detecting QRS complex, P-wave and T-wave as well as determining parameters such as start time, end time, and wave sign (upward or downward). First, a window with a specific length is slid sample to sample on the selected scale and the curve length in each window is multiplied by the area under the absolute value of the curve.