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This paper presents for the first time Electrocardiograph (ECG) QRS detection algorithm implemented in Application Specific Integrated Circuit (ASIC). The algorithm based on the dyadic wavelet transform (DYWT) multiscale-product scheme is designed especially for real-time biomedical signal processing applications. The algorithm is evaluated based on the MIT-BIH database and achieves a sensitivity...
Hilbert-Huang transform (HHT) is composed of the empirical mode decomposition (EMD) as the first step of the procedure and Hilbert spectral analysis (HSA) as the second step. It is a recent tool in the analysis of signals originating from nonlinear processes as well as nonstationary signals. Empirical mode decomposition produces a set of intrinsic mode functions and the core idea is based on the assumption...
Electrocardiogram (ECG) signal involves significant information about heart state and is one of the common tools for cardiologist in diagnosis of heart failures. Using adaptive filters for filtering this signal, which inherently has nonstationary features, is used as one of the known methods. In this paper, the wavelet transform and also a neural network (NN) based on adaptive filters are used for...
Presented paper describes a system of biomedical signal classifiers with preliminary feature extraction stage based on matched wavelets analysis, where two structures of classifier using Neural Networks (NN) and Support Vector Machine (SVM) are applied. As a pilot study the rules extraction algorithm applied for two of mentioned machine learning approaches (NN & SVM) was used. This was made to...
The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. The selection of the appropriate wavelet basis is crucial due to different wavelet bases may cause different de-noising results. This paper concludes the generally used wavelet bases according to the last twenty-year literatures which used wavelet transform to de-noise ECG signals, analyzes the best wavelet basis selection...
In this paper we present a bimodal biometric system for cryptographic key generation that works with speech and electrocardiogram (ECG) signals using wavelet transforms. This work is based on the uniqueness and quasi-stationary behavior of ECG and speech signals with respect to an individual. The architecture of the proposed system considers three security factors, namely, user password, biometric...
In this paper, we present a wavelet-based 128-bit key generator using electrocardiogram (ECG) signals. The key generator comprises two independent stages, namely, enrollment and verification-generation. In the latter, an algorithm for determining the keys is also proposed. This work is based on the uniqueness and quasi-stationary behavior of ECG signals with respect to an individual. This lets to...
In ECG de-noising, the goal is to recover the valid ECG from the undesired artifacts with the minimum distortion for presenting a signal, which allows easy visual interpretation and auto diagnosis. We adopt three de-noising algorithms based on wavelet packet (WPT), lifting wavelet (LWT) and stationary wavelet transform (SWT) for de-noising ECG signals, respectively. Through using simulated and real...
To solve the problem of lacking adaptability and difficulties in selecting wavelet base when using the Wavelet-ICA to cancellation the noise in ECG, a new method called E-ICA is proposed which is combined with the Empirical Mode Decomposition and Independent Component Analysis. The advantages of EMD and ICA are exploited, and the two layers reconstruction, that is inverse ICA and inverse EMD was introduced...
In this paper, we describe the ECG PQRST key features detector based on dyadic wavelet transform (DyWT) which is robust to time varying & noise. This method analyses ECG waveform. It includes noise purification, sample design of digital ECG. This method can implement ECG report in real time and provide exact explanation for diagnostic decision obtained. We exemplify the performance of the DyWT...
A wavelet-based electrocardiogram (ECG) compression algorithm is proposed in this paper. The proposed algorithm reduces the bit rate of ECG and preserves its main clinically diagnostic features intact by minimizing reconstructed signal distortion. The original signal is divided into blocks and each block goes through a discrete wavelet transform. A threshold based on energy packing efficiency of the...
This paper describes electrocardiogram (ECG) pattern classification using QRS morphological features and the artificial neural network. Four types of ECG patterns were chosen from the MIT-BIH database to be classified, including normal sinus rhythm, premature ventricular contraction, atrial premature beat and left bundle branch block beat. Authors propose a set of six ECG morphological features to...
In this paper, we describe the ECG PQRST key features detector based on dyadic wavelet transform (DyWT) which is robust to time varying & noise. This method analyses ECG waveform. It includes noise purification, sample design of digital ECG. This method can implement ECG report in real time and provide exact explanation for diagnostic decision obtained. We exemplify the performance of the DyWT...
We design a portable measurement device which can monitor electrocardiograph (ECG) and analyze arrhythmia. It is small, light, lower power, and consists of two parts: the main system and the sub. The device gets ECG signals by the electrode sticking to the chest skin. This make the device is suitable for monitoring in long time. 16-bits MCU-MSP430 is the most important part. Besides, there are amplifier...
A novel approach for the nonlinear characterization of electrocardiogram (ECG) signals has been developed. The new developed methodology is based on a numerical algorithm that extracts the value of dinfin (d-infinite) characterizing the asymptotic chaotic behavior of a system. This algorithm also extracts a measure of the maximum Lyapunov exponent and it is applicable to time series where the knowledge...
In this work we present a comparative study, testing selected methods for clustering and classification of Holter electrocardiogram (ECG). More specifically we focus on the task of discriminating between normal 'N' beats and premature ventricular 'V' beats. Some of the tested methods represent the state of the art in pattern analysis, while others are novel algorithms developed by us. All the algorithms...
The paper presents an application of a clustering technique inspired by ant colony metaheuristics. The paper addresses the problem of long-term (Holter) electrocardiogram data processing. Long-term recording produces a huge amount of biomedical data, which must be preprocessed prior to its presentation to the specialist. The paper also discusses relevant aspects improving the robustness, stability...
An integrated framework for ventricular arrhythmias (VA) assessment, composed of two levels, is proposed in this work. The first level consists of four independent neural networks (NN), designed for specific detection tasks: signal quality, premature ventricular contractions (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF). Time and frequency domain features, obtained from the...
We have presented a new method for ECG baseline correction using the adaptive bionic wavelet transform (BWT). In fact by the means of BWT, the resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential. Besides by optimizing the BWT parameters parallel to modifying our previous...
In this paper, the artificial neural network method was used for electrocardiogram (ECG) pattern recognition. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, premature ventricular contraction, atrial premature beat and left bundle branch block beat. ECG morphology and R-R interval features were performed as the characteristic representation...
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