Impact of sampling rate reduction on automatic ECG delineation

Communication Technologies Group, I3A, Zaragoza University, 50018 Zaragoza, Spain.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2007; 2007:2587-90. DOI: 10.1109/IEMBS.2007.4352858
Source: PubMed


Electrogram (EGM) delineation is an increasingly important task to be performed in implantable cardiac devices such as pacemakers and defibrillators. Reliable detection and classification of EGM components might help to minimize the risk of false detections. Efforts are therefore undertaken to examine whether existing ECG delineators can be adapted for the delineation of EGMs. One issue to be solved is the low sampling rate at which EGMs are acquired. In this study we investigate performance degradation of an existing wavelet-based ECG delineator by a stepwise reduction of the sampling rate. It is shown that for signals sampled at 1 kHz, no significant performance degradation occurs in P or T wave delineation. The performance of QRS delineation is affected only at the lowest sampling rate of 62.5 Hz. For signals originally sampled at 250 Hz, no degradation in delineation performance is observed. It is concluded that the automatic delineation of ECGs can be performed at sampling rates as low as 62.5 Hz and that the low sampling rate does not significantly degrade the reliability of automatic delineation.

Download full-text


Available from: Bart van Grinsven, Mar 18, 2014
  • [Show abstract] [Hide abstract]
    ABSTRACT: Arrhythmia is one of the preventive cardiac problems frequently occurs all over the globe. In order to screen such disease at early stage, this work attempts to develop a system approach based on registration, feature extraction using discrete wavelet transform (DWT), feature validation and classification of electrocardiogram (ECG). This diagnostic issue is set as a two-class pattern classification problem (normal sinus rhythm versus arrhythmia) where MIT-BIH database is considered for training, testing and clinical validation. Here DWT is applied to extract multi-resolution coefficients followed by registration using Pan Tompkins algorithm based R point detection. Moreover, feature space is compressed using sub-band principal component analysis (PCA) and statistically validated using independent sample t-test. Thereafter, the machine learning algorithms viz., Gaussian mixture model (GMM), error back propagation neural network (EBPNN) and support vector machine (SVM) are employed for pattern classification. Results are studied and compared. It is observed that both supervised classifiers EBPNN and SVM lead to higher (93.41% and 95.60% respectively) accuracy in comparison with GMM (87.36%) for arrhythmia screening.
    Journal of Medical Systems 06/2010; 36(2):677-88. DOI:10.1007/s10916-010-9535-7 · 2.21 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, an electrocardiogram (ECG)-based pattern analysis methodology is presented for the detection of artrial flutter and atrial fibrillation using fractal dimension (FD) of continuous wavelet transform (CWT) coefficients of raw ECG signals, sample entropy of heart beat interval time series, and mean heart beat interval features. Accurate diagnosis of atrial tachyarrhythmias is important, as they have different therapeutic options and clinical decisions. In view of this, we have made an attempt to develop a discrimination mechanism between artrial flutter and atrial fibrillation. The methodology consists of mean heart beat interval detection using Pan Tompkins algorithm, calculation of sample entropy of heart beat interval time series, computation of box counting FD from CWT coefficients of raw ECG, statistical significance test, and subsequent pattern classification using different classifiers. Different wavelet basis functions like Daubechies-4, Daubechies-6, Symlet-2, Symlet-4, Symlet-6, Symlet-8, Coiflet-2, Coiflet-5, Biorthogonal-1.3, Biorthogonal-3.1, and Mayer wavelet have been used to compute CWT coefficients. Features are evaluated using statistical analysis and subsequently two-class pattern classification is done using unsupervised (k-means, fuzzy c-means, and Gaussian mixture model) and supervised (error back propagation neural network and support vector machine) techniques. In order to reduce the bias in choosing the training and testing set, k-fold cross validation is used. The obtained results are compared and discussed. It is found that the supervised classifiers provide higher accuracy in comparison to the set of unsupervised classifiers.
    Journal of Mechanics in Medicine and Biology 12/2012; 12(5-05):1240023. DOI:10.1142/S0219519412400234 · 0.73 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Low-power design has become a key technology for battery-power biomedical devices in Wireless Body Area Network. In order to meet the requirement of low-power dissipation for electrocardiogram related applications, a down-sampling QRS complex detection algorithm is proposed. Based on Wavelet Transform (WT), this letter characterizes the energy distribution of QRS complex corresponding to the frequency band of WT. Then this letter details for the first time the process of down-sampled filter design, and presents the time and frequency response of the filter. The algorithm is evaluated in fixed point on MIT-BIH and QT database. Compared with other existing results, our work reduces the power dissipation by 23%, 61%, and 72% for 1 ×, 2 ×, and 3 × down-sampling rate, respectively, while maintaining almost constant detection performance.
    IEEE Signal Processing Letters 05/2013; 20(5):515-518. DOI:10.1109/LSP.2013.2254475 · 1.75 Impact Factor
Show more