Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohsen Naji; Seyed Mohammad Firouzabadi; Sedighe Kahrizi
Volume 7, Issue 1 , June 2013, , Pages 13-20
Abstract
The collected electromyogram (EMG) signals from trunk musculature (e.g., rectus abdominis and external oblique muscle) are often contaminated with the heart muscle electrical activity (ECG). This paper introduces a novel method, the Empirical Mode Decomposition, for elimination of ECG contamination from ...
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The collected electromyogram (EMG) signals from trunk musculature (e.g., rectus abdominis and external oblique muscle) are often contaminated with the heart muscle electrical activity (ECG). This paper introduces a novel method, the Empirical Mode Decomposition, for elimination of ECG contamination from EMG signals. The method is compared to a Butterworth high pass filtering. Results obtained from the analysis of generated and experimental EMG signals show that our method outperforms the high pass filtering for elimination of ECG contamination from trunk EMG signals.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohsen Mohammadvali’ee; Ali Mahloojifar
Volume 7, Issue 3 , June 2013, , Pages 265-276
Abstract
One of the most important goals for increasing the recognition and treatment revenue is transmitting the vital data to medical care team, more quickly. Nowadays, use of new technologies for transmission of data is extending every day. In this research, for transmitting electrocardiogram, first we code ...
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One of the most important goals for increasing the recognition and treatment revenue is transmitting the vital data to medical care team, more quickly. Nowadays, use of new technologies for transmission of data is extending every day. In this research, for transmitting electrocardiogram, first we code the signal into a matrix of codes, then we will use bluetooth technology to transmit data from offset device to target device. Signal coding will affect in sending and storing data. This suite of codes that form for the first time in this method, include number and type of extermumes, time of occurring them, samples of signal and etc. We complete the coding, using arithmetic coding. The input of arithmetic coding is the extracted suite of coefficients and the output is arithmetic codes. We use SD-200 serial bluetooth modules produced by SENA™ in transmission of coding coefficients. The transmitter sends extracted coefficients and receptor receives them and reconstructs the primary signal. For testing and evaluating the method, we use MIT–BIH arrhythmia database. In our method, when average Percentage of Root Mean Square Differential (PRD) is equal to 5.93%, Compression Ratio (CR) and Cross Correlation (CC) is equal to 8.69 and 99.8%, respectively. Beside, when PRD is about 10.21%, CR and CC is 13.03 and 99.47%, respectively. The maximum standard deviation of compression ratio in two states is 4.17.
Biomedical Image Processing / Medical Image Processing
Jamal Esmaeilpour; Sattar Mirzakouchaki; Jalil Seyfali Harsini; Abdorrahim Kadkhoda Mohammadi
Volume 1, Issue 3 , June 2007, , Pages 167-176
Abstract
In this paper, the role of Vector Quantizer Neural Network in classification of six types of ECG signals has been investigated using the features that extracted from Daubechies6 Wavelet transformation. The six types of signals are: normal beat, left bundle branch block beat, right bundle branch block ...
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In this paper, the role of Vector Quantizer Neural Network in classification of six types of ECG signals has been investigated using the features that extracted from Daubechies6 Wavelet transformation. The six types of signals are: normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction paced beat and fusion of paced and normal beats. The required data were obtained from the MIT/BIH arrhythmia databases. By using the annotation files of the databases, the patterns of these six types of ECG signals were separated. Then, for better feature extraction, filtering and scaling on the patterns were applied. We used the energies of the last five detailed signals obtained from the exerting the Wavelet transformation in six levels, as the pattern features for Vector Quantizer Network training and testing. From each class, five hundred patterns were used for network training and one hundred patterns for testing. The results indicated %93.1 accuracy for six classes and above %94.3 for lesser than six classes. Then the rate of similarity and dissimilarity of the classes were considered. Finally, the results of this method were compared with some other methods in terms of accuracy.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Reza Nourouzi; Mohammad Javad Yazdanpanah
Volume 1, Issue 1 , June 2007, , Pages 53-62
Abstract
Ventricular Fibrillation (VF) is a dangerous abnormality in the heart activity. During the VF, well known shape of electrocardiogram (ECG) signal changes to a pseudo-noise waveform. Recent researches have depicted that VF is not a noisy signal. The characteristics of VF and chaotic signals are the same. ...
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Ventricular Fibrillation (VF) is a dangerous abnormality in the heart activity. During the VF, well known shape of electrocardiogram (ECG) signal changes to a pseudo-noise waveform. Recent researches have depicted that VF is not a noisy signal. The characteristics of VF and chaotic signals are the same. In this research, these characteristics were studied and used for discriminating the VF signal from the other electrocardiogram signals. Three types of electrocardiogram signals including VF, Tachycardia and Normal ECG were used for training and testing a back propagation neural network. We used these signals in three stages. At the first stage, the power spectrum of signals was used for training and testing the neural network. Time Series signals were used in the second stage. The result of the first experience was better than the second. At the third stage, we used surrogate technique to enrich the training signals in the time domain. The surrogate technique is a method which has been used in the chaotic systems. By using these new generated signals for training the neural network, the results of classification were extremely improved. Furthermore, the results of simulations showed that the chaotic dynamic of VF signal is a time dependant one.
