Carlos R. Vázquez Seisdedos

An innovative approach of QRS segmentation based on first-derivative, Hilbert and Wavelet Transforms

The QRS detection and segmentation processes constitute the first stages of a greater process, e.g., electrocardiogram (ECG) feature extraction. Their accuracy is a prerequisite to a satisfactory performance of the P and T wave segmentation, and also to the reliability of the heart rate variability analysis. This work presents an innovative approach of QRS detection and segmentation and the detailed results of the proposed algorithm based on First-Derivative, Hilbert and Wavelet Transforms, adaptive threshold and an approach of surface indicator. The method combines the adaptive threshold, Hilbert and Wavelet Transforms techniques, avoiding the whole ECG signal preprocessing. After each QRS detection, the computation of an indicator related to the area covered by the QRS complex envelope provides the detection of the QRS onset and offset. The QRS detection proposed technique is evaluated based on the well-known MIT-BIH Arrhythmia and QT databases, obtaining the average sensitivity of 99.15% and the positive predictability of 99.18% for the first database, and 99.75% and 99.65%, respectively, for the second one. The QRS segmentation approach is evaluated on the annotated QT database with the average segmentation errors of 2.85±9.90ms and 2.83±12.26ms for QRS onset and offset, respectively. Those results demonstrate the accuracy of the developed algorithm for a wide variety of QRS morphology and the adaptation of the algorithm parameters to the existing QRS morphological variations within a single record.

Neural network approach for T-wave end detection: A comparison of architectures

In this paper, a new approach to the problem of detecting the end of the T wave (Te) on the electrocardiogram (ECG) using Multilayer Perceptron (MLP) neural networks is proposed and evaluated. The approach consists of a neural network acting as a regression function that estimates the Te location using the samples between two consecutive R peaks. The input vectors were taken using three dimensional reduction methods (Discrete Cosine Transform, DCT, Principal Component Analysis, PCA and resampling, RES) over a window of 100 samples. For training, Bayesian regularization has been used. A total of 1536 neural networks were trained. The results show that PCA and DCT are more feasible than RES as dimension reduction methods. Finally, a brief comparison with other algorithms proposed in the literature is included.

Análise de Desempenho da Entropia Aproximada (ApEn) na Análise da Variabilidade da Frequência Cardíaca (VFC)

This work evaluates the performance of a nonli-near indicator in front of noise in the analysis of Heart Rate Variability (HRV) due to the presence of artifacts. For this purpose, we select approximate entropy (ApEn). Firstly, we use artificial RR time series and compare the error differences between the computed values before and after inserting ran-dom false-positive and false-negative beats. Afterwards, we use ambulatory recordings of 18 healthy subjects from the MIT-BIH database and compare the error difference between the computed values mantaining and excluding artifacts. We have found that the average errors, both in artificial RR time series and ambulatory recordings, vary inversely proportional to the metric value of ApEn for each time series without artifacts.

Two solutions for the processing of ambulatory electrocardiogram

The Electrocardiogram (ECG) ambulatory monitoring has three stages: acquisition, processing and analysis, in each one, the information on ECG signal itself or the time series extracted from it are affected by noises, interferences and artifacts. The analysis of the duration and variability of heart intervals establishes rules for the selection of valid beats and to determine with accuracy the fiducially points, especially the T wave end. This paper describes two problems and their solutions involved in the processing stage. First, a method is described to identify the valid beats for heart rate variability analysis, and then another one is presented for the determination of T wave end point, based on the calculation of trapezes areas. The validation of both methods exhibits excellent performance indexes in comparison with other approaches.

Método estadístico-geométrico para medir la variabilidad de la frecuencia cardíaca

This paper presents a new statistical-geometric method to quantify the heart rate variability (HRV). The proposed method is intended to be robust in front to artefacts embedded in RR time series. It is performed in two steps: (a) automatic elimination of artefacts using a geometric method, based on the triangles areas, and (b) computation of the RR-series standard deviation. We compare the behaviour of two new indexes, derived from the proposed method, with other two robust indexes in front to artefacts, described in the literature. The obtained results show that the computations of statistical-geometric indexes preserve more diagnostic information than the others. The statistical-geometric method is very easy to implement and it is very familiar for the medical professionals.