Anwar Shrouf

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Carlo Sansone

Università degli Studi di Napoli "Federico II"

lakshmi narayanan

Saeed Karimifard

Sandeep Raj

Vinitha Sree Subbhuraam

Roshan Joy Martis

IIT Kharagpur

Rosmina Jaafar

Universiti Kebangsaan Malaysia

Mustafa Alhamdi

Yakup Kutlu

Iskenderun Technical University

International Journal of Scientific Research in Science and Technology IJSRST

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Papers by Anwar Shrouf

Neural Network and Genetic Algorithm Based ECG Beat Classification

This paper proposes a potential cascaded neural network algorithm for ECG Holter system beat clas... more This paper proposes a potential cascaded neural network algorithm for ECG Holter system beat classification. The system works in real time and is capable of recognizing up to 20 artificial QRS templates. The parallelism of neural network increases the efficiency of computations. An error signal derived from differences between predictor and testing signal is used in the classification. A neural network is used to generate linear predictions for signals. Another neural network generates error signals measured between predictions taken from the first neural network and testing signal. A fifth neural network does the classifications utilizing the error signals instead of complex raw signal. Three Hermite functions are used in generating testing signals with and without noise. Proper thresholding for the error signals is essential for classifier immunity. The result is a compact, online, efficient, and hardware realizable signals classifier that uses minimal compressed error signal.

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QRS Detection and PVC Beat Recognition Using a Generalised Teager Energy Operator ANWAR

This paper presents a novel algorithm for automatic QRS complex detection and premature ventricul... more This paper presents a novel algorithm for automatic QRS complex detection and premature ventricular contraction (PVC) beat recognition based on a generalised Teager energy operator (GTEO). The algorithm is divided into two stages: QRS detection and PVC beat recognition. An optimal GTEO order is determined for each stage. A second order GTEO is used for QRS detection, and a seventh order GTEO is used for PVC beat recognition. The proposed algorithm was tested using ECG signals from two recognised arrhythmia databases, the MIT-BIH and the AHA database. The signals chosen contained PVC beats as well as normal beats. Sensitivity and specificity parameters were used to measure the accuracy of the proposed algorithm. The main advantages of using a GTEO are simplicity, robustness and speed. The sensitivities achieved using the proposed algorithm were 99.5% for QRS detection and 97.4% for PVC recognition. The specificities achieved were 99.8% for QRS detection and 99.1% for PVC beat recognition.

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ECG Beat Classification using a Sliding Window and Correlation of the Three-bit Linear Prediction Error Signal

WSEAS TRANSACTIONS ON SIGNAL PROCESSING, 2020

Sudden cardiac arrest (SCA) is responsible for half of all deaths due to heart disease. Most SCAs... more Sudden cardiac arrest (SCA) is responsible for half of all deaths due to heart disease. Most SCAs could be avoided by obtaining an early diagnosis from ECG recordings. The long-term monitoring systems record a large number of beats and require automatic detection and classification of the premature ventricular contraction (PVC) beats. Several ECG beat classification algorithms based on different methodologies have been developed and implemented. This paper presents a novel algorithm for automatic recognition of a premature ventricular contraction (PVC) beat based on a three-bit linear prediction error signal (LPES). The algorithm is composed of three main stages: signal denoising and QRS detection; nonlinear transformation of the linear prediction error signal e(n); and a sliding window. The proposed algorithm was tested using ECG signals from two recognized arrhythmia databases, MIT-BIH and AHA. The selected signals contained normal beats as well as abnormal beats. Sensitivity and ...

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Application of messy genetic algorithm in the design of FIR filters

ABSTRACT

Application of Cepstrum Algorithms for Speech Recognition

Lecture Notes in Computer Science, 2003

This paper proposes a novel method for speech recognition. This is method based on the calculatio... more This paper proposes a novel method for speech recognition. This is method based on the calculation of correlation coefficients of cepestrum by linear prediction (LP). The results of the method is compared with other methods. The real cepstrum is used to separate/estimate the spectral content of the speech from its pitch frequencies. Some notes regarding the efficiency of the processors

ECG Beat Classification Using Linear Prediction Error Signal

Lecture Notes in Medical Informatics, 1991

This paper proposes a linear prediction method for beat classification in ECG Holter system. We a... more This paper proposes a linear prediction method for beat classification in ECG Holter system. We assume that correlation method is used for recognition of up to 40 QRS templates. Since a classifier has to operate in real time mode a computationally efficient algorithm is used. A three state pulse-code train derived from a linear prediction error signal (LPES) is employed for classification instead of a raw signal.

Electrocardiogram data compression algorithm based on the linear prediction of the wavelet coefficients

ICECS 2000. 7th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.00EX445), 2000

... Abstract - This paper describes a new algorithm, based on the compression of the linearly pre... more

ECG data compression using optimal non-orthogonal wavelet transform

Medical Engineering & Physics, 2000

This paper introduces an effective technique for the compression of electrocardiogram (ECG) signa... more This paper introduces an effective technique for the compression of electrocardiogram (ECG) signals. The technique is based on a new class of non-orthogonal discrete wavelet transform (DWT). The performance of ECG compression algorithm is measured by its ability to minimize distortion while retaining all clinically significant features of the signal. The percent root-mean square difference (PRD) is used as an accepted standard for measuring the signal distortion. However, there is no standard for measuring the clinically significant features retained after signal reconstruction. The coefficients of the DWT are calculated such that the square of the difference between the original signal and the reconstructed one is minimum in least mean square sense. The resulting transforms deal with signals of arbitrary lengths; that means the signal length is not restricted to be a multiple of power of 2. Numerical results comparing the performance of the constructed non-orthogonal transform with that of W-transform and Daubechies D(4) orthogonal transform are given. These results show that, independent of signal length, the decomposition of the signal up to the fourth level is sufficient for getting minimum PRD. In addition, the proposed technique yields the lowest PRD compared to the other two algorithms and for a compression ratio less than 10 the optimal transform can be obtained for only one ECG period. However, for a higher compression ratio the PRD is smaller for long signals.

A novel compression algorithm for electrocardiogram signals based on the linear prediction of the wavelet coefficients

Digital Signal Processing, 2003

This paper describes a new algorithm for electrocardiogram (ECG) compression. The main goal of th... more This paper describes a new algorithm for electrocardiogram (ECG) compression. The main goal of the algorithm is to reduce the bit rate while keeping the reconstructed signal distortion at a clinically acceptable level. It is based on the compression of the linearly predicted residuals of the wavelet coefficients of the signal. In this algorithm, the input signal is divided into blocks and each block goes through a discrete wavelet transform; then the resulting wavelet coefficients are linearly predicted. In this way, a set of uncorrelated transform domain signals is obtained. These signals are compressed using various coding methods, including modified run-length and Huffman coding techniques. The error corresponding to the difference between the wavelet coefficients and the predicted coefficients is minimized in order to get the best predictor. The method is assessed through the use of percent root-mean square difference (PRD) and visual inspection measures. By this compression method, small PRD and high compression ratio with low implementation complexity are achieved. Finally, we have compared the performance of the ECG compression algorithm on data from the MIT-BIH database.

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Noise-Immune ECG Classifier Using Wavelet Transform and Neural Networks

This paper proposes a novel algorithm for automatic classification of electrocardiogram (ECG) bea... more This paper proposes a novel algorithm for automatic classification of electrocardiogram (ECG) beats recorded by Holter systems. The algorithm is based on a combination of neural network and discrete wavelet transform. Discrete wavelet transform coefficients are used as an input of the neural network to perform the classification task. The proposed classifier wastested by both real ECG signals andartificially generated signals. Five Hermite functionswereused in generating the ECG artificial testing signals. Different levels of noise were added to the signals to examine the noise immunity of the classifier. The main advantage of the proposed classifier is that it is noise immune and accurate. The testing results on the proposed classier show that it is capable of recognising 40 beats, and it works properly in the classification of the ECG signal with a classification ratio of 100% for an SNR of more than 6 dB. KeywordsWavelet transform, neural networks, ECG beat classification, arrhyt...

Neural Network and Genetic Algorithm Based ECG Beat Classification

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QRS Detection and PVC Beat Recognition Using a Generalised Teager Energy Operator ANWAR

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ECG Beat Classification using a Sliding Window and Correlation of the Three-bit Linear Prediction Error Signal

WSEAS TRANSACTIONS ON SIGNAL PROCESSING, 2020

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Application of messy genetic algorithm in the design of FIR filters

ABSTRACT

Application of Cepstrum Algorithms for Speech Recognition

Lecture Notes in Computer Science, 2003