Biomedical subjects

Irena Jekova

Publications and source records attributed to Irena Jekova.

7 recordsLinked to original sources

PubMed2006

Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification.

The prompt and adequate detection of abnormal cardiac conditions by computer-assisted long-term monitoring systems depends greatly on the reliability of the implemented ECG automatic analysis technique, which has to discriminate between different types of heartbeats. In this paper, we present a comparative study of the heartbeat classification abilities of two techniques for extraction of characteristic heartbeat features from the ECG: (i) QRS pattern recognition method for computation of a large collection of morphological QRS descriptors; (ii) Matching Pursuits algorithm for calculation of expansion coefficients, which represent the time-frequency correlation of the heartbeats with extracted learning basic waveforms. The Kth nearest neighbour classification rule has been applied for assessment of the performances of the two ECG feature sets with the MIT-BIH arrhythmia database for QRS classification in five heartbeat types (normal beats, left and right bundle branch blocks, premature ventricular contractions and paced beats), as well as with five learning datasets-one general learning set (GLS, containing 424 heartbeats) and four local sets (GLS+about 0.5, 3, 6, 12 min from the beginning of the ECG recording). The achieved accuracies by the two methods are sufficiently high and do not show significant differences. Although the GLS was selected to comprise almost all types of appearing heartbeat waveforms in each file, the guaranteed accuracy (sensitivity between 90.7% and 99%, specificity between 95.5% and 99.9%) was reasonably improved when including patient-specific local learning set (sensitivity between 94.8% and 99.9%, specificity between 98.6% and 99.9%), with optimal size found to be about 3 min. The repeating waveforms, like normal beats, blocks, paced beats are better classified by the Matching Pursuits time-frequency descriptors, while the wide variety of bizarre premature ventricular contractions are better recognized by the morphological descriptors.

Algorithms↗

PubMed2005

Subtraction of 16.67 Hz railroad net interference from the electrocardiogram: application for automatic external defibrillators.

The widespread application of automatic external defibrillators (AEDs) for treating out-of-hospital cardiac arrest incidents and their particular use at railway stations defines the task for 16.67 Hz power line interference elimination from the electrocardiogram (ECG). Although this problem exists only in five European countries, it has to be solved in all AEDs, which must comply with the European standard for medical equipment requirements for interchangeability and compatibility between rail systems. The elimination of the railroad interference requires a specific approach, since its frequency band overlaps with a significant part of the frequencies in the QRS spectra. We present a method based only on one channel ECG signal processing, which effectively subtracts the interference components. The computation procedure is based on simple signal processing tools, which include: (i) bi-directional band-pass filtering (13-23 Hz) of the analyzed ECG segment; (ii) estimation of adequate linearity thresholds; (iii) frequency measurement and calculation of dynamic interference buffer in linear segments and (iv) phase synchronization and subtraction technique in nonlinear segments. The developed method has proved advantageous in providing sufficient quality of the output interference free ECG signal for seven defined arrhythmia types (normal sinus rhythm, normal rhythm, supraventricular tachicardia, slow/rapid ventricular tachycardia, and coarse/fine ventricular fibrillation), and simulated interferences with constant or variable frequencies and amplitudes, which cover the entire amplitude range of the input channel. The procedure is suitable to be embedded in AEDs as a preprocessing branch, which proves reliable for analysis of ECG signals, thus guaranteeing the specified accuracy of the AED automatic rhythm analysis algorithms.

Algorithms↗

PubMed2005

Assessment of ECG frequency and morphology parameters for automatic classification of life-threatening cardiac arrhythmias.

The reliable recognition and adequate electrical shock therapy of life-threatening cardiac states depend on the electrocardiogram (ECG) descriptors which are used by the defibrillator-embedded automatic arrhythmia analysis algorithms. We propose a method for real-time ECG processing and parameter set extraction using band-pass digital filtration and ECG peak detection. Twelve parameters were derived: (i) seven parameters from the band-pass filter output-six threshold parameters and one peak counter; (ii) five parameters from the ECG peak detection branch, which assess the heart rate, the periodicity and the amplitude/slope symmetry of the ECG peaks. The statistical assessment for more than 36 h of cardiac arrhythmia episodes collected from the public AHA and MIT databases showed that some of the parameters achieved high specificity and sensitivity, but there was no parameter providing 100% separation between non-shockable and shockable rhythms. In order to estimate the influence of the wide variety of cardiac arrhythmias and the different artifacts in real recording conditions, we performed a more detailed study for eight non-shockable and four shockable rhythm categories. The combination of the six top-ranked parameters provided specificity: (i) more than 99% for rhythms with narrow supraventricular complexes, premature ventricular contractions, paced beats and bradycardias; (ii) almost 95% for supraventricular tachycardias; (iii) 91.5% for bundle branch blocks; (iv) 92.2% for slow ventricular tachycardias. The attained sensitivity was above 98% for coarse and fine ventricular fibrillations and 94% for the rapid ventricular tachycardias. The accuracy for the noise contaminated non-shockable and shockable signals exceeded 93%. The proposed parameter set guarantees an accuracy that meets the AHA performance goal for each rhythm category and could be a reliable facility for AED shock-advisory algorithms.

Arrhythmias, Cardiac↗

PubMed2004

Real time detection of ventricular fibrillation and tachycardia.

The automatic external defibrillator (AED) is a lifesaving device, which processes and analyses the electrocardiogram (ECG) and delivers a defibrillation shock to terminate ventricular fibrillation or tachycardia above 180 bpm. The built-in algorithm for ECG analysis has to discriminate between shockable and non-shockable rhythms and its accuracy, represented by sensitivity and specificity, is aimed at approaching the maximum values of 100%. An algorithm for VF/VT detection is proposed using a band-pass digital filter with integer coefficients, which is very simple to implement in real-time operation. A branch for wave detection is activated for heart rate measurement and an auxiliary parameter calculation. The method was tested with ECG records from the widely recognized databases of the American Heart Association (AHA) and the Massachusetts Institute of Technology (MIT). A sensitivity of 95.93% and a specificity of 94.38% were obtained.

Algorithms↗

PubMed2004

Defibrillation shock success estimation by a set of six parameters derived from the electrocardiogram.

It is well known that in some cases defibrillator shocks cannot terminate ventricular fibrillation (VF). Repeated failed shocks often may worsen subsequent response to therapy. This study assesses the ability of six parameters derived from the surface electrocardiogram (ECG) to predict defibrillation shock outcome. Using stepwise discriminant analysis, we obtained several discriminant functions, yielding different combinations of sensitivity and specificity for detection of pre-shock ECG segments corresponding to successful versus unsuccessful shocks. The study was performed consecutively for 3, 4 and 5 s ECG time intervals. The prediction accuracy of 72.3% (61.8% sensitivity and 79.6% specificity) with five parameters and 3 s VF segment analysis prior to defibrillation shock could be considered acceptable for possible practical application in automatic external defibrillators.

Databases, Factual↗

PubMed2002

Method for ventricular fibrillation detection in the external electrocardiogram using nonlinear prediction.

The automatic external defibrillator is a lifesaving device which processes and analyses the electrocardiogram (ECG) and delivers defibrillation shock when necessary. The accuracy of the built-in algorithm for ECG analysis must be very high, with sensitivity and specificity aimed to approach the maximum values of 100%. An algorithm based on nonlinear prediction of the external ECG signal is proposed. It extracts seven parameters characterizing the prediction possibility of the assessed ECG signal. By means of the K-nearest neighbours rule the diagnostic accuracy of different combinations of these parameters was evaluated. Thus the accuracy obtained was higher than 95% with sensitivity and specificity values depending on the combination of parameters. The method was tested with ECG records from the widely recognized databases of the American Heart Association (AHA) and Massachusetts Institute of Technology (MIT).

Electrocardiography↗

PubMed2002

Detection of ventricular fibrillation and tachycardia from the surface ECG by a set of parameters acquired from four methods.

The recent development and increased application of automatic external defibrillators have prescribed very strong requirements towards ventricular fibrillation (VF) and fast ventricular tachycardia (VT > 180 bpm) detection from the surface electrocardiogram (ECG). We attempted to use informative parameters from several existing analysis methods and from a method developed in-house. A set of nine parameters was derived initially, with four of them being selected after statistical assessment. Detection of VF against non-shockable rhythms was obtained using the K-nearest neighbours classification method, with 98.6% specificity and 96.7% sensitivity. The detection accuracy remained high after inclusion of VT episodes above and below 180 bpm to shockable and non-shockable rhythms respectively and after the addition of noise. Test signals were taken from the well-known ECG signal databases of the American Heart Association and the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH-'cudb' and 'vfdb' files).

Electrocardiography↗