Automated Electrocardiogram Analysis Research Articles

CCDD: AN ENHANCED STANDARD ECG DATABASE WITH ITS MANAGEMENT AND ANNOTATION TOOLS

Standard Electrocardiogram (ECG) database is created for validating and comparing different algorithms on feature detection and disease classification. At present, there are four frequently used standard databases: MIT-BIH arrhythmia database, QT database, CSE multi-lead database and AHA database. With the development in equipment and diagnosis approach, severe deficiencies are discovered and a new modern ECG database is needed for further research. So Chinese Cardiovascular Disease Database (CCDD or CCD database), which contains 12-Lead ECG data, detailed annotation features and beat diagnosis result is proposed. It is advanced for not only improving the raw ECG data's technical parameters, but also introducing valuable morphology features which are utilized by experienced cardiologists effectively. CCDD is employed by our group as well as aiming for supporting other research groups that work in automated ECG analysis.

Frequency Characteristics of Rhythm Non-Assimilation in Canine Ventricular Fibrillation

Ventricular fibrillation is the main cause of sudden cardiac death in many countries, including Russia; therefore to study ventricular fibrillation is an urgent problem. Objective: to study rhythm non-assimilation in canine ventricular fibrillation. Materials and methods. The author made a frequency analysis of electrocardiograms in 25 dogs with ventricular fibrillation, by applying the fast Fourier transform method. The frequency analysis was carried out in the frequency ranges corresponding to those of delta, theta, alpha, beta, and gamma rhythms of an electroencephalogram. Results. Ventricular fibrillation is characterized by rhythm non-assimilation with the oscillations being recorded on an electrocardiogram in the frequency ranges corresponding to those of delta, theta, alpha, beta, and gamma rhythms of an electroencephalogram. The proportion of oscillations in the frequency ranges of delta, theta, alpha, beta, and gamma rhythms is determined by cardiac functional mobility (lability). The latter is reduced by ischemia in ventricular fibrillation. Diminished cardiac lability in ventricular fibrillation reflects the stages of rhythm non-assimilation with a dominant and non-dominant frequency pattern naturally detectable by the frequency analysis of an electrocardiogram in the frequency ranges of delta, theta, alpha, beta, and gamma rhythms. The greater proportion of alpha, beta, and gamma rhythm frequencies in the non-dominant frequency pattern of rhythm non-assimilation in ventricular fibrillation is, the deeper cardiac lability fall is. Conclusion. The frequency analysis of an electrocardiogram in the frequency ranges of delta, theta, alpha, beta, and gamma rhythms allows the stages of rhythm non-assimilation to be objectively determined in the entire range — at all stages of ventricular fibrillation. The findings may be included into the algorithms of an automated electrocardiogram analysis, which permits objective determination of ventricular fibrillation stages in automated defibrillators. Key words: canine heart, ventricular fibrillation, rhythm non-assimilation.

EXTRACTING RESPIRATION RATE FROM RAW ECG SIGNALS

There exists several techniques for monitoring respiration (spirometers, nasal thermocouples, transthoracic inductance, impedance plethysmography, strain gauge etc.), but each of these techniques requires a special device. The method proposed in this paper calculates the intervals of respiration rates extracted from ordinary electrocardiogram (ECG) signals. The result obtained shows clearly that each respiration cycle can be represented by a maxima. The simple algorithm proposed in this study is used to count the respiration times. The method has been tested on 26 voluntary subjects in three different conditions (normal, controlled breath to simulate apnea, and during sleep). Moreover, the respiration times obtained have been compared with the respiration rate acquired by Philips Physiological Monitor (MP60). The method proposed in this paper for normal subjects have an accuracy that can reach over 96%. However, for apnea subject, the proposed methods can reach over 93% accuracy. In conclusion, this technique is applicable to any type of automated ECG analysis, in real time and without the need of additional hardware.

QRS COMPLEX DETECTION USING DOUBLE DENSITY DISCRETE WAVELET TRANSFORM

In this paper, an offline double density discrete wavelet transform based QRS complex detection of the electrocardiogram signal is discussed. Baseline wandering present in the signal is removed by using the double density discrete wavelet transformed approximation coefficients of the signal. The results are more accurate than other methods with less effort. This is an unsupervised method allowing the process to be used in offline automatic analysis of electrocardiogram. The measurement of timing intervals of ECG signal by automated system is highly superior to its subjective analysis. The heart rate signals are essentially non-stationary and contain indicators of current disease or warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. Double density discrete wavelet transform is easier to implement, provides multiresolution and also reduces the computational time. In the pre-processing step, the baseline wandering is removed from the ECG signal. Then the R peaks/QRS complexes are detected. From the location of the R peaks, the successive RR intervals and heart rate are calculated. Fifty-two records from the MIT-BIH arrhythmia database are used to evaluate the proposed method. Sensitivity and positive prediction are used as performance measures. This method detects the R peaks with 100% sensitivity and 99.95% positive prediction. The performance of the proposed method is better than other methods existing in the literature.

DE-NOISING AND BASELINE WANDERING REMOVAL OF ELECTROCARDIOGRAM USING DOUBLE DENSITY DISCRETE WAVELET

In this paper, an off-line double density discrete wavelet transform based de-noising and baseline wandering removal methods are proposed. Different levels decomposition is used depending upon the noise level, so as to give a better result. When the noise level is low, three levels decomposition is used. When the noise level is medium, four levels decomposition is used. When the noise level is high, five levels decomposition is used. Soft threshold technique is applied to each set of wavelet detail coefficients with different noise level. Donoho's estimator is used as a threshold for each set of wavelet detail coefficients. The results are compared with other classical filters and improvement of signal to noise ratio is discussed. Using the proposed method the output signal to noise ratio is 19.7628 dB for an input signal to noise ratio of -7.11 dB. This is much higher than other methods available in the literature. Baseline wandering removal is done by using double density discrete wavelet approximation coefficients of the whole signal. This is an unsupervised method allowing the process to be used in off-line automatic analysis of electrocardiogram. The results are more accurate than other methods with less effort.

Combining Algorithms in Automatic Detection of QRS Complexes in ECG Signals

QRS complex and specifically R-Peak detection is the crucial first step in every automatic electrocardiogram analysis. Much work has been carried out in this field, using various methods ranging from filtering and threshold methods, through wavelet methods, to neural networks and others. Performance is generally good, but each method has situations where it fails. In this paper, we suggest an approach to automatically combine different QRS complex detection algorithms, here the Pan-Tompkins and wavelet algorithms, to benefit from the strengths of both methods. In particular, we introduce parameters allowing to balance the contribution of the individual algorithms; these parameters are estimated in a data-driven way. Experimental results and analysis are provided on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database. We show that our combination approach outperforms both individual algorithms.

High-Resolution Analysis of Ambulatory Electrocardiograms to Detect Possible Mechanisms of Premature Ventricular Beats

For generations of electrocardiogram (ECG) analysis, the presence of premature ventricular beats (PVBs) has been characterized as a common event in the ECG without regard to the mechanism which has caused the PVB in the first place. At best, the coupling interval with the preceding sinus beat may be noted. This viewpoint persisted throughout the era of automated ECG analysis, as well as influencing the treatment of more life threatening events by PVB suppression strategies alone. This study proposed three hypotheses which would link the PVB to a specific mechanism or milieu. Each of these hypotheses requires significant signal processing of the continuously recorded high resolution ECG. Data are presented which demonstrate that abnormal intra-QRS potentials may be linked to a reentrant mechanism for the PVBs and that many patients have significant changes in these potentials in the sinus beats preceding the PVB. Changes in the characteristics of the repolarization as measured in the T/U wave period were also observed and could be linked to triggered activity mechanism for some PVBs. Finally, the role of subclinical ST segment changes also indicates that low grade ischemia may play a role in modulating either PVB mechanism. The data generated by this study suggest that a new view toward PVB mechanism as measured by ECG characteristics may warrant a more rational approach to renewed interest identifying the malignant PVBs and their eventual clinical management.

Quality Assurance in Cardiac Disease Diagnostic using Computerised Feature Extraction of ECG Signal

The bio-potentials generated by the muscles of the heart result in an electrical signal called electrocardiogram (ECG). It is one of the most important physiological parameter, which is being extensively used for knowing the state of cardiac patients. Electrocardiograph enables a rapid observation of the behavior of the heart activity in terms of heart rhythm, conduction intervals, morphological aspects of the waves (P, Q, R, S, T) associated to depolarization and repolarization of the auricles and ventricles. Feature extraction of ECG is most essential task in the manual and automated ECG analysis for use in instruments like ECG monitors, Holter tape recorders and scanners, ambulatory ECG recorders and analysers. Reliable detection of the QRS complex in either a normal or an abnormal ECG and its analysis is the first and foremost task in almost every ECG signal analysis system aimed at the diagnostic interpretation of ECG. A good amount of research work has been carried out during the last five decades for the accurate and reliable detection of QRS segment in the ECG signal. In the present paper, a bibliographic survey of different feature extraction methods has been presented. A comparative study of the work carried out in the department of Electrical Engineering of Indian Institute of Technology Roorkee has also been discussed. The element of subjectivity is totally eliminated in cardiac disease diagnostic by using computerised feature extraction of the ECG signal. All modern state-of-art hospitals use computer based ECG machines for feature extraction and disease diagnostic with a view to assure quality of their health care services.

Panoramic display of the orderly sequenced 12-lead ECG

The standard 12-lead electrocardiogram (ECG) has been developed over many years. The ECG has had a long and successful history of providing diagnostic information in clinical medicine. Cardiac arrhythmias have been elucidated by deductive reasoning from continuous ECG recordings with confirmation from electrophysiologic studies. Recently, there has been renewed interest in the morphology of the QRS complex, ST-segment, and T wave, which raises the important question of considering whether the usual method of display provides maximal diagnostic capabilities. The conventional display provides a logical visualization of precordial lead recordings representing the horizontal plane, but does not provide a logical visualization of the limb lead recordings representing the frontal plane. Many clinical problems require the consideration of serial ECGs necessitating the comparison of separate pages. An alternate format presenting serial recordings on a single page would be advantageous. Some automated ECG analysis systems already include the capability for multiple display formats, but these have not yet been widely used in clinical practice. This point of view paper introduces a new display format for the standard 12-lead ECG that includes: (1) a presentation of an orderly sequence of leads to facilitate scanning through different points in space and (2) a presentation of recordings of 12-lead sequences to facilitate scanning through different points in time. This display format could either replace or supplement the conventional ECG format.

Influence of recognition errors of computerised analysis of 24-hour electrocardiograms on the measurement of spectral components of heart rate variability

Spectral methods for the assessment of heart rate variability (HRV) in 24-h electrocardiogram (ECG) are believed to require visual verification and manual editing of the computerised recognition of the ECG. This study investigated the effect of the recognition errors of computerised ECG recognition on two methods providing spectral HRV indices: (a) Fast Fourier Transformation (FFT); and (b) peak-to-trough analysis (PTA). Both methods were used to measure HRV spectra in 24-h ECGs recorded in 557 survivors of acute myocardial infarction. Each ECG was analysed using the Marquette 8000 Holter system and spectral HRV analyses were performed both prior to and after manual verification of the automatic ECG analysis. The FFT and PTA methods were used to calculate the low (0.04-0.15 Hz), medium (0.15-0.40 Hz) and high (0.40-1.00 Hz) HRV spectral components. For each method and for each spectral component, the rank correlations between the results obtained from unedited and edited ECG recognition were calculated. The correlations between the corresponding spectral components provided by the FFT and PTA methods applied to the edited recognitions were also calculated. Both methods were substantially affected by recognition errors. The FFT method was more sensitive to the misrecognition than the PTA method. The inter-method correlations were higher for the high and medium spectral components than for the low spectral component. The study suggests that spectral HRV analysis should be performed only on carefully verified and manually corrected recognitions of long-term electrocardiograms.

Automated electrocardiogram analysis: The state of the art

The overwhelming number of electrocardiograms (ECGs) now recorded routinely has prompted the development of computer analysis which in turn has benefited electrocardiography with important technological advances. All automated ECG analysis systems adopt a similar approach: a measurement program and a program that interprets the clinical significance of these measurements along with a rhythm analysis algorithm. Measurement, selection and classification of parameters vary according to the program used. Data compression is applied to the signal to reduce processing time and allow long-term storage. Diagnostic accuracy, however, is not greatly improved over that of experienced cardiologists. Programs studied using a validated data bank provided by an international group of cardiologists show a variability not only in parameter measurement but also in diagnostic statement and in the way in which such statements are expressed. Recommendations for measurement standards have been made to fulfil the need for exchange of diagnostic criteria. No recommendations concerning the selection of parameters have been proposed, and so new parameters or combinations of parameters can be introduced with the ultimate aim of increased diagnostic performance.