Features estimated from multi-trend ECG wavelet coefficients will be useful for MI detection and localization. MEES features of multi-lead ECG and different classifiers are used for MI detection and localization. Multiscale PA characteristics were assessed from the phase of the complex coefficients of the multitrend ECG waves.
The proposed VMEE characteristics of ECG and the random bunch classification are used for the detection of shockable ventricular arrhythmias. VMEE features for detection and classification of shockable ventricular arrhythmias and non-shockable episodes of ECG. The multiscale analysis of multilead ECG for detection and localization of MI has not been used.
This motivates us to use the multiscale features of multilead ECG for detection and localization of MI. VMEE features for detection and classification of shockable ventricular arrhythmia and non-shockable episodes of ECG.
Electrical Activity of the Heart and the Electrocardiogram
The left atrium and right atrium are separated by a thin membranous wall called the interatrial septum. Similarly, the left ventricle and the right ventricle are separated by a thick muscular wall called the interventricular septum. First, the left atrium and right atrium are depolarized due to the firing of the SA node.
The depolarization of the septum and ventricles is due to the firing of the HIS bundle and Purkinje fibers [1]. The morphological features of the ECG are the amplitude, duration and shape of the local waves [8]. PR interval: The PR interval is the duration between the end of atrial depolarization and the beginning of ventricular depolarization [9].
Multilead Electrocardiogram
Different Views of Heart from Multilead ECG
The left lateral view of the heart is viewed using the ECG signals from lead I, lead aVL, lead V5, and lead V6, respectively. Similarly, Lead II, Lead III, and Lead aVF view the lower portion of the heart. Lead V1, Lead V2, Lead V3, and Lead V4 are used for anterior views of the heart.
The inferior MI can be diagnosed using the pathologic signatures of lead II, lead III, and lead aVF. Similarly, the pathological variations in the ECG signals of lead I, lead aVL, lead V5 and lead V6 are the evidences for the detection of lateral MI. The posterior MI is diagnosed using the reciprocal changes of the ECG signals from lead V1 and lead V2.
Clinical Components of ECG in Different Leads
Cardiac Ailments and Pathological Changes in ECG
QS level and abnormal Q wave appear in lead V2 and lead aVF, respectively [1]. Pathological signatures of ALMI on multi-lead ECG are ST-segment elevations in lead I, lead aVL, lead V3, lead V4, and lead V6, respectively. Due to this type of MI, abnormal Q waves appear in lead III and lead aVF, respectively.
ST junction elevation is also seen in lead II, lead III, and lead aVF. Pathological changes in the multichannel ECG due to this type of MI are tall R-waves and conical T-waves in lead V2 and lead V3, respectively. ST-segment elevation and T-wave inversion are seen in leads V5 and V6, respectively.
Automated Diagnostic System
Preprocessing
The isoelectric line and the ST segment of the ECG signal are affected by the baseline shift. The correlation between adjacent beats of ECG data is called beat-to-beat correlation. Heart diseases such as PVC beat, VB, VEB, localization of myocardial infarction and premature atrial beats are diagnosed based on segmentation of ECG data by beats [36], [37].
Cardiologists suggest serial recording of 12-lead ECG data to detect cardiac abnormalities [12]. To exploit intra-beat and inter-beat correlations, frame-based processing is needed. In this thesis, frame-based processing of ECG data is used for the detection of various cardiac diseases.
Diagnostic Feature Extraction and Feature Selection
For multiple ECG cases, the frame-based processing has the advantage of capturing the inter-lead, the intra-beat and the inter-beat correlations. The feature selection is a method to select the relevant features from the diagnostic feature vector of ECG signal.
Classification of Cardiac Ailments
Diagnostic Features for Automated Detection of Cardiac Ailments
Scope for the Present Work
Detecting and classifying shockable ventricular arrhythmia and non-shockable episodes from the ECG is a challenging problem. In the signal processing literature, VMD has recently been proposed for the analysis of nonlinear and non-stationary signals. This method has been used to reduce the noise of the ECG signal and detect various heart diseases from the ECG.
There are options to evaluate diagnostic characteristics of the ECG modes for shockable ventricular arrhythmia detection.
Organization of the Thesis
A key motivation for researchers is the use of different signal processing algorithms to evaluate the diagnostic features of the ECG. The performance of ECG diagnostic features is assessed using classifiers for the detection of cardiac abnormalities. Various signal processing techniques have been used to evaluate the diagnostic information of the ECG signal.
The classifiers like K-nearest neighbor (KNN) and fuzzy KNN, random forest (RF), support vector machine (SVM) and extreme learning machine (ELM) were used for classification of heart diseases from the diagnostic features of ECG. This chapter reviews the existing diagnostic feature extraction methods from ECG, the feature selection methods and the classifiers for heart disease detection. The ECG diagnostic features for the detection of myocardial infarction (MI), shockable ventricular arrhythmia and other groups of heart diseases are briefly discussed in Section 2.2.
The wavelet transform and VMD-based processing of the ECG signal are described in Section 2.3 and Section 2.4, respectively. Diagnostic features based on PCA, HOS, and nonlinear ECG analysis are discussed in Section 2.5, Section 2.6, and Section 2.7, respectively. In Section 2.8, feature selection methods such as correlation-based technique, mutual information, and statistical uncertainty are described.
In this work, the algorithms developed for the evaluation of ECG diagnostic features and the detection of heart diseases are tested using four standard publicly available databases. The ECG signals in the CUDB database have the annotations such as ventricular fibrillation (VF), sustained ventricular tachycardia (VT) and non-VF rhythms. The annotations of ECG signals in VFDB database are normal sinus rhythm (NSR), ventricular flutter, VF, VT and other rhythms.
The sampling frequency of the ECG signal in the CUDB and VFDB database is 250 Hz.
Diagnostic Information from ECG
Diagnostic Features for Detection of MI
A number of methods have been proposed for detecting MI from ECG and VCG, using time-domain features, frequency-domain features, and time-frequency features. 70] have used ST-segment parameters such as ST-J amplitude, ST-amplitude and T-wave features of 12-lead ECG and artificial neural network (ANN) for the detection of acute MI. 72] have used morphological features such as Q-wave amplitude, Q-wave duration, R-wave amplitude, S-wave amplitude, and S-wave duration of 12-lead ECG and ANN for MI detection.
The decision tree classifier has been used for the detection of myocardial ischemia from the spatiotemporal features of the ECG. SVM and Gaussian mixture model (GMM) have been used for the detection of MI. The polynomial coefficients and other morphological features of multi-lead ECG and multi-instance learning (MIL) classifier have been used for the detection of MI.
Therefore, the frequency-domain features and the time-frequency-based features have been widely used for the detection of MI. In recent years, a number of methods have been proposed for the detection of MI based on the frequency domain and the wavelet-based properties of the ECG. Song et al.[81] have used QRS slope functions, spectral features of RR time series, and QRS area functions in ECG to detect acute myocardial ischemia.
Therefore, the time-frequency features were used for the detection of MI from ECG and VCG signals. Banerjee et al.[49] used discrete time continuous wavelet transform (CWT) for detection of MI from ECG signal. The DWT-based diagnostic features of ECG and the threshold-based classifier are used for the detection of MI.
Yang [14] has used multiscale repeatability quantitative analysis (RQA) of vector cardiogram (VCG) for MI detection.
Diagnostic Features for Detection of Various Cardiac Ailments
Feature selection technique based on bootstrap sampling and SVM classifier were used for shockable ventricular arrhythmia detection. The variational optimization problem for the ECG signal is formulated in three steps. i) The Hilbert transform is used to make the frequency spectrum of each mode unidirectional or unidirectional. ii) Then, each mode frequency spectrum is shifted to the baseband by multiplying the factor −jωtn, where ω is the center frequency of the tthmode. iii). Dl= [cDl1(k), cD2l(k), .., cDml (k)] (3.2) Multivariate sub-band matrices largely capture the clinical components (P wave, QRS complex and T wave) of multilead ECG .
The spectra of the main ECG signal I and the sub-band signals (the reconstructed signal using wavelet coefficients) are shown in Fig. In this block, MI and HC are classified using the MEES features of the multi-lead ECG framework. The 48 multi-scale energy features and 24-scale eigenvalue features are estimated from multi-scale ECG multi-scale matrices.
The proposed methods for evaluating the complex wavelet size and phase features of multi-lead ECG and heart disease detection are discussed in Section 4.2. The following subsections briefly describe the evaluation of the multiscale PA functions and the CWSB functions of multi-lead ECG. In this subsection, the feature selection and heart disease classification from the complex wavelet size and phase features of multi-lead ECG are discussed.
Then the magnitude and the phase characteristics are calculated from the CWSB of multi-lead ECG. The detection of hypertrophic cardiomyopathy using the temporal and the morphological features of multilead ECG was proposed in [39]. The center frequency and the phase difference characteristics of the variation modes of ECG were used for the detection of ventricular flutter, atrial flutter and VF [69].
The VMEE features of the ECG and the random forest classifiers are used for the detection and classification of shockable ventricular heart disease and non-shockable episodes. Mark, "The impact of the mit-bih arrhythmia database," Engineering in Medicine and Biology Magazine, IEEE, vol. Katz, “The weighted diagnostic distortion (wdd) measure for ECG signal compression,” IEEE Transactions on Biomedical Engineering, vol.
Clifford, "The Evaluation of an Open Source Online Training System for Teaching 12-Lead Electrocardiographic Interpretation," Journal of Electrocardiology, vol. Allegronet al., “The diagnostic and prognostic impact of the redefinition of acute myocardial infarction: lessons from the global registry of acute coronary events (grace),” American Heart Journal, vol.
