ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method

Abstract

This study proposes electrocardiogram (ECG) identification based on non-fiducial feature extraction using window removal method, nearest neighbor (NN), support vector machine (SVM), and linear discriminant analysis (LDA). In the pre-processing stage, Daubechies 4 is used to remove the baseline wander and noise of the original signal. In the feature extraction and selection stage, windows are set at a time interval of 5 s in the preprocessed signal, while autocorrelation, scaling, and discrete cosine transform (DCT) are applied to extract and select features. Thereafter, the window removal method is applied to all of the generated windows to remove those that are unrecognizable. Lastly, in the classification stage, the NN, SVM, and LDA classifiers are used to perform individual identification. As a result, when the NN is used in the Normal Sinus Rhythm (NSR), PTB diagnostic, and QT database, the results indicate that the subject identification rates are 100%, 99.40% and 100%, while the window identification rates are 99.02%, 97.13% and 98.91%. When the SVM is used, all of the subject identification rates are 100%, while the window identification rates are 96.92%, 95.82% and 98.32%. When the LDA is used, all of the subject identification rates are 100%, while the window identification rates are 98.67%, 98.65% and 99.23%. The proposed method demonstrates good results with regard to data that not only includes normal signals, but also abnormal signals. In addition, the window removal method improves the individual identification accuracy by removing windows that cannot be recognized.

ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

“…As already mentioned, Daubechies was our first approach since this is the common mother wavelet used for the analysis of the ECG signal [41,42,43]. Nevertheless, this paper explores how to extract randomness from ECG signals by multi-level wavelet decomposition, and to the best of our knowledge, this is the first time this approach has been studied.…”

Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG Morphology

“…In order to remove baseline wander, electromyographic (EMG) noise, and power line interference contained in the ECG signal, numerous methods have been proposed, such as bandpass filters [5], wavelet transform based methods [6], [7], adaptive filters [8], empirical mode decomposition [9], and independent component analysis [10]. For example, Rakshit and Das [9] have proposed a methodology using EMD which decomposes signals as a set of intrinsic mode function (IMF) based on the signal complexity for removing noise interference.…”

“…For example, Rakshit and Das [9] have proposed a methodology using EMD which decomposes signals as a set of intrinsic mode function (IMF) based on the signal complexity for removing noise interference. Jung and Lee [7] have employed Daubechies 4 as the wavelet basis function to remove the noise within the ECG signal in the preprocessing stage.…”

A new approach to the intracardiac inverse problem using Laplacian distance kernel

“…Cardiac arrhythmias are often first detected with electrocardiogram (ECG) monitoring, which consists of recording the electrical potential produced by the heart in the skin [ 1 ]. ECG interpretation is crucial for arrhythmia diagnosis, and in recent years, different techniques have been studied to improve the ECG signal quality, including the search for new materials for the electrodes [ 2 ], the design of cardiac monitoring systems [ 3 , 4 ] combining the analysis of ECG signals and other non-invasive signals such as the seismocardiogram [ 5 ], the estimation of fetal ECG [ 6 ], the impact of noise and improvement of signal preprocessing techniques [ 7 , 8 ], or the study of ECG recognition systems for arrhythmia classification [ 9 – 11 ]. However, the arrhythmia treatment often requires additional bioelectric sources, and invasive methods based on catheters have been used in cardiac electrophysiology to find the arrhythmia mechanisms and to suppress them with intracardiac catheter ablation.…”