I would like to thank and salute the Head of the Department of Electrical and Electronics Engineering, BUET, for his support and cooperation. The maximum value of the approximation coefficients of level 4 is chosen as an indication parameter used to distinguish between different abnormalities. The study of the ECG signal is therefore one of the main topics in biomedical engineering, more specifically in biomedical signal processing.
In 1856, Kollicker and Mueller discovered the electrical activity of the heart when a sciatic nerve from the frog/gastrocenemius preparation was dropped onto an isolated frog heart and both muscles contracted synchronously.(22) Alexander Muirhead attached wires to the wrist of a febrile patient to obtain a recording the patient's heartbeat while studying for his DSc (in Electricity) in 1872 at St Bartholomew's Hospital. The combined lead acts as a ground and is attached to the negative terminal of the EKG. In 1944, Young and Koenig reported deviation of the P-R segment in a series of patients with atrial infarction.
In 1963 Baule and McFee were the first to discover the magnetocardiogram, which is the electromagnetic field produced by the electrical activity of the heart. can detect the ECG without the use of skin electrodes.
Historical Background
Like the addition of the 6 standardized unipolar chest leads, these additional leads increase the sensitivity of the electrocardiogram in detecting myocardial infarction. The Oswalt Institute for Physics in Medicine, established in 1921 in Frankfurt, Germany, began offering formal training in this subject. The institute's founder, Friedrich Dessauer, was a pioneer in research into the biological effects of ionizing radiation. Five years later, the first engineering conference in medicine and biology in the United States convened under the auspices of the Institute of Radio Engineers, the American Institute for Electrical Engineering and the Instrument Society of.
The diversity of work in biomedical engineering and the variety of backgrounds of the people who contribute to the field have made it difficult for a single organization to represent them all. Gradually, biomedical engineering programs, departments and institutes were established in different parts of the world. Thus, the amount of data becomes huge, and the study becomes tedious and time-consuming.
Most of the time, the desired ECG signals are either corrupted or embedded in noise.
Motivation
Appropriate signal processing techniques such as wavelet analysis for ECG signals can be used to overcome these problems. The wavelet transform (WT) has emerged in recent years as a key time-frequency analysis and encoding tool for ECG [3]-[4]. The main advantage of DWT is its great time and frequency localization capability, which enables it to detect the local characteristics of the input signal.
Discrete wavelet techniques have been used for ECG peak detection, ECG characteristic point detection, heart rate variability analysis, etc. A direct and simplified method to detect cardiac abnormalities based on the numerical values of discrete wavelet coefficients is a somewhat new approach, which this study have aimed at. For each of these phenomena, some particular ECG signals have been selected as samples from the MIT-BIH Arrhythmia Database, an online source of ECG recordings used worldwide for ECG-related research.
We have chosen Discrete Wavelet Transform (DWT) as an effective signal processing tool that can be applied to these signals.
Organization of the Dissertation
DWT analysis of sample signals is performed using five different types of mother wavelets: haar, db10, coif2, bior6.8 and sym4. Compare the performance of different mother wavelets and choose one that can deliver the best performance.
WAVELET TRANSFORM
- Introduction
- Wavelet Characteristics
- Admissibility Condition
- Regularity Condition
- Wavelet Transformation Types
- Continuous Wavelet Transforms
- Discrete Wavelets Transforms
- Wavelet Decomposition
- Properties of Mother Wavelets
- Commonly Used Wavelet Families
- Wavelets Used in this Study
- Haar Wavelet
- Daubechies Wavelet
- Bi-orthogonal Wavelet
- Coiflet Wavelet
- Symlet Wavelet
Zero at zero frequency also means that the average value of the wavelet in the time domain must be zero, i.e. continuous wavelet (CWT) analyzes continuous time functions using a basis function which is the parent wavelet. Federated waltzes have an infinite number of waltzes in the waltz transform, which is not practical.
We usually choose s=2, so that the sampling of the frequency axis corresponds to dyadic sampling. Biorthogonal waves with FIR filters: These waves can be defined by the two scaling filters wr and wd, for reconstruction and decomposition respectively. Orthogonal waves without FIR filter, but with scaling function: These waves can be defined by defining the wavelet function and the scaling function.
Wavelets without FIR filter and without scale function: These wavelets can be defined by the definition of the wavelet function. Complex wavelets without FIR filter and without scaling function: these wavelets can be defined by the definition of the wavelet function. The regularity, which is useful for obtaining nice features such as the smoothness of the reconstructed signal or image, and for the estimated function in nonlinear regression analysis.
The Haar wave was created in 1909 by Alfred Haar. It is the first, the simplest and one of the most commonly used waltzes. Ingrid Daubechies, one of the pioneers in the field of waltz research, invented the Daubechies family, called compactly supported orthonormal waltzes, and thus enables discrete analysis of waltzes. The names of Daubechies waves are written dbN, where N is the order and db is the "surname" of the waves.
By using two wavelets, one for decomposition (shown on the left in the figure) and the other for reconstruction. shown on the right) instead of the same individual, interesting properties are derived. Wavelets of the Symlet family are denoted as symN, where N is the order of the wavelet.
ELECTROCARDIOGRAM
- Anatomy and Physiology of Human Heart
- Electrical Activity of the Heart
- Arrhythmia
- Electrocardiogram
- Basic Components of ECG
- ECG Monitoring Method
- Normal ECG Signal
- Cardiac Phenomena in Consideration
- Normal Beat
- Left Bundle Branch Block (LBBB)
- Premature Ventricular Contraction (PVC)
- Atrial Premature Beat (APB)
The right and left sides of the heart are separated by an internal tissue wall called the septum. The main function of the heart's electrical conduction system is to transmit small electrical impulses from the SA node (where they are normally generated) to the atria and ventricles, causing them to contract. The SA node lies in the wall of the right atrium near the inlet of the superior vena cava.
The interatrial conduction tract (bundle of Bachmann), a branch of one of the internodal atrial conduction channels, extends across the atria and conducts electrical impulses from the SA node to the left atrium. The bundle of His lies in the upper part of the interventricular septum and connects to the AV node with two bundle branches. Correctly diagnosing arrhythmias requires an electrocardiogram, which is used to assess the electrical activity of the heart.
Electrocardiography (ECG or EKG) is a graphic interpretation of the electrical activity of the heart over time captured and recorded externally by skin electrodes. When electrical activity of the heart is not detected, the EKG is a straight, flat line - the isoelectric line or baseline. QRS complex The QRS complex is a recording of a single heartbeat on the EKG that corresponds to the depolarization of the right and left ventricles.
PR Interval The PR interval is measured from the beginning of the P wave to the beginning of the QRS complex. The interval from the beginning of the QRS complex to the peak of the T wave is called the absolute refractory period. The last half of the T wave is called the relative refractory period (or vulnerable period).
QT interval The QT interval is measured from the beginning of the QRS complex to the end of the T wave. Left bundle branch block occurs due to failure of the left bundle branch to transmit excitation.
RESULTS
Selection Criteria
Analog Recording and Digitization
The sampling frequency was chosen to facilitate implementations of 60 Hz (mains frequency) digital notch filters in arrhythmia detectors. Since recorders were battery operated, most of the 60 Hz noise in the database occurred during playback. In the recordings digitized at twice real time, this noise appears at 30 Hz (and multiples of 30 Hz) relative to real time.
Each signal was sampled almost simultaneously (the inter-signal skew was on the order of a few microseconds). Given the sampling rate and resolution of the ADC, the difference coding implies a maximum recordable slew rate of ±225 mV/s.
Contents of the Database
Used Symbols
Symbol Meaning
- Test Data
- Normal Beat
- Left Bundle Branch Block
- Right Bundle Branch Block
- Premature Ventricular Contraction
- Atrial Premature Beat
- Methodology
- Analysis Using haar
- Normal Beat
- Left Bundle Branch Block
- Right Bundle Branch Block
- Premature Ventricular Contraction
- Atrial Premature Beat
- Complete Result Obtained from the Analysis with haar
- Analysis Using coif2
- Analysis Using sym4
- Analysis Using db10
- Analysis Using bior6.8
- Discussion
Total number of samples to be used in the analysis, level of decomposition and the mother wave function are defined by the user. ECG signals as obtained from the sample records are imported into Matlab and some initial processing of the raw data is performed. Discrete Wavelet Transform (DWT) is performed on each of the records based on the input parameters.
The results include the maximum, minimum, standard deviation, and mean values of the approximation coefficients of the final level and the detail coefficients of each level. For each of the cardiac events, a graph of the range of A4 (max) values is displayed. Orthogonal with compact support Yes Yes Yes Yes No Biothogonal with compact support Yes No No No Yes.
One of the objectives of our analysis was to select a mother wavelet function that shows the best performance in differentiating five particular types of cardiac phenomena. The characteristics of the signal vary greatly from person to person, even for the same type of cardiac phenomenon. Based on the maximum values of approximation coefficients up to level 4 for discrete wavelet transform (DWT) of the sample signals, we can choose db10 as the most suitable mother wavelet function to perform this task.
However, observing the results obtained for db10 from table 5.9 and figure 5.4, we can conclude that the goal of differentiating all five ECG beats is partially, not completely, achieved.
CONCLUSION
Conclusion
Future Perspective
By overcoming the limitations of our proposed algorithm, this method can be further improved and can be made more effective for the diagnosis of heart diseases. Further analysis with more number of symmetric and asymmetric wave functions should be performed to generalize the findings of this study and to detect other types of cardiac abnormalities.
