International journal of telemedicine and applications, 2012
Developments of new classes of efficient compression algorithms, software systems, and hardware f... more Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further en...
International journal of telemedicine and applications, 2011
A telemedicine system using communication and information technology to deliver medical signals s... more A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.
... [4] NB Amor, N. Essoukri, B. Amara, DICOM image compression by wavelet transform, in: Proceed... more ... [4] NB Amor, N. Essoukri, B. Amara, DICOM image compression by wavelet transform, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 2, October 2002, pp. ... 11 (10) (2004) 806–809. [14] Z. Wang, AC Bovik, HR Sheikh, EP Simoncelli ...
Recent advances in digital imaging technology have greatly enhanced the interpretation of critica... more Recent advances in digital imaging technology have greatly enhanced the interpretation of critical/pathology conditions from the 2-dimensional medical images. This has become realistic due to the existence of the computer aided diagnostic tool. A computer aided diagnostic (CAD) tool generally possesses components like preprocessing, identification/selection of region of interest, extraction of typical features and finally an efficient classification system. This paper enumerates on development of CAD tool for classification of chronic liver disease through the 2-D image acquired from ultrasonic device. Characterization of tissue through qualitative treatment leads to the detection of abnormality which is not viable through qualitative visual inspection by the radiologist. Common liver diseases are the indicators of changes in tissue elasticity. One can show the detection of normal, fatty or malignant condition based on the application of CAD tool thereby, further investigation required by radiologist can be avoided. The proposed work involves an optimal block analysis (64 × 64) of the liver image of actual size 256 × 256 by incorporating Gabor wavelet transform which does the texture classification through automated mode. Statistical features such as gray level mean as well as variance values are estimated after this preprocessing mode. A non-linear back propagation neural network (BPNN) is applied for classifying the normal (vs) fatty and normal (vs) malignant liver which yields a classification accuracy of 96.8%. Further multi classification is also performed and a classification accuracy of 94% is obtained. It can be concluded that the proposed CAD can be used as an expert system to aid the automated diagnosis of liver diseases.
Electroencephalograms (EEG) are the brain signals that provide us the valuable information about ... more Electroencephalograms (EEG) are the brain signals that provide us the valuable information about the normal or epileptic state of the brain. In this paper the EEG signals were characterized by wavelet, sample and spectral entropy approach and the recurrent neural network classifier is used for the automated detection of epileptic seizures.
Computer assisted automated detection is highly inevitable for recognizing neurological disorders... more Computer assisted automated detection is highly inevitable for recognizing neurological disorders, as it involves continuous monitoring of Electroencephalogram (EEG) signal. Being a non-stationary signal, suitable analysis is essential for EEG to differentiate the normal ...
The extensible markup language (XML), which has emerged as a standard for structuring documents, ... more The extensible markup language (XML), which has emerged as a standard for structuring documents, notably for World Wide Web is of great interest to the bioinformatics research community. Biological sequences that are stored in different databanks require standard software computational tools for sequence identifications, structural analysis and visualization. Recently bioinformatics applications make use of XML technology due to its flexibility, durability, data modeling capability, data interoperability and open framework possibility for defining standards. The traditional architectures available in literature are lagging in terms of standard formats for biological data integration. This work describes the features and limitations of those architectures and investigates the need for a remodeled architecture in terms of using technologies such as extensible style sheet languages (XSL), XSL translators (XSLT), document object model (DOM) and application program interface (API). The proposed architecture is intended to extract and handle the biological data from the different databanks.
For a given DNA sequence, it is well known that pair wise alignment schemes are used to determine... more For a given DNA sequence, it is well known that pair wise alignment schemes are used to determine the similarity with the DNA sequences available in the databanks. The efficiency of the alignment decides the type of amino acids and its corresponding proteins. In order to evaluate the given DNA sequence for its proteomic identity, a pattern matching approach is proposed in this paper. A block based semi-global alignment scheme is introduced to determine the similarity between the DNA sequences (known and given). The two DNA sequences are divided into blocks of equal length and alignment is performed which minimizes the computational complexity. The efficiency of the alignment scheme is evaluated using the parameter, percentage of similarity (POS). Four essential DNA version of the amino acids that emphasize the importance of proteomic functionalities are chosen as patterns and matching is performed with the known and given DNA sequences to determine the similarity between them. The ratio of amino acid counts between the two sequences is estimated and the results are compared with that of the POS value. It is found from the experimental results that higher the POS value and the pattern matching higher are the similarity between the two DNA sequences. The optimal block is also identified based on the POS value and amino acids count.
Two-stage lossless data compression methods involving predictors and encoders are well known. Thi... more Two-stage lossless data compression methods involving predictors and encoders are well known. This paper discusses the application of context based error modeling techniques for neural network predictors used for the compression of EEG signals. Error modeling improves the performance of a compression algorithm by removing the statistical redundancy that exists among the error signals after the prediction stage. In this paper experiments are carried out by using human EEG signals recorded under various physiological conditions to evaluate the effect of context based error modeling in the EEG compression. It is found that the compression efficiency of the neural network based predictive techniques is significantly improved by using the error modeling schemes. It is shown that the bits per sample required for EEG compression with error modeling and entropy coding lie in the range of 2.92 to 6.62 which indicates a saving of 0.3 to 0.7 bits compared to the compression scheme without error modeling.
IEEE Transactions on Information Technology in Biomedicine, 2008
Lossless compression of EEG signal is of great importance for the neurological diagnosis as the s... more Lossless compression of EEG signal is of great importance for the neurological diagnosis as the specialists consider the exact reconstruction of the signal as a primary requirement. This paper discusses a lossless compression scheme for EEG signals that involves a predictor and an adaptive error modeling technique. The prediction residues are arranged based on the error count through an histogram computation. Two optimal regions are identified in the histogram plot through a heuristic search such that the bit requirement for encoding the two regions is minimum. Further improvement in the compression is achieved by removing the statistical redundancy that is present in the residue signal by using a context-based bias cancellation scheme. Three neural network predictors, namely, single-layer perceptron, multilayer perceptron, and Elman network and two linear predictors, namely, autoregressive model and finite impulse response filter are considered. Experiments are conducted using EEG signals recorded under different physiological conditions and the performances of the proposed methods are evaluated in terms of the compression ratio. It is shown that the proposed adaptive error modeling schemes yield better compression results compared to other known compression methods.
Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-... more Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.
In this paper, 3-D discrete Hartley transform is applied for the compression of two medical modal... more In this paper, 3-D discrete Hartley transform is applied for the compression of two medical modalities, namely, magnetic resonance images and X-ray angiograms and the performance results are compared with those of 3-D discrete cosine and Fourier transforms using the parameters such as PSNR and bit rate. It is shown that the 3-D discrete Hartley transform is better than the other two transforms for magnetic resonance brain images whereas for the X-ray angiograms, the 3-D discrete cosine transform is found to be superior.
In this paper, the performance of different types of perceptron predictors for EEG signal compres... more In this paper, the performance of different types of perceptron predictors for EEG signal compression is investigated. A two-stage lossless compression scheme which involves the decorrelation of EEG samples in the first stage and entropy coding in the second stage is considered. The second stage employs an arithmetic coding scheme. A comparison of the performance of the perceptron predictors with that of linear predictors such as FIR, NLMS is presented. It is found that the single-layer perceptron performs, in general, better than the multi-layer perceptrons as well as linear predictors.
IEEE Transactions on Information Technology in Biomedicine, 2007
The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The E... more The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time-consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system. Two different types of neural networks, namely, Elman and probabilistic neural networks, are considered in this paper. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system.
The volume of patient monitoring video acquired in hospitals is very huge and hence there is a ne... more The volume of patient monitoring video acquired in hospitals is very huge and hence there is a need for better compression of the same for effective storage and transmission. This paper presents a new motion segmentation technique, which improves the compression of patient monitoring video. The proposed motion segmentation technique makes use of a binary mask, which is obtained by thresholding the standard deviation values of the pixels along the temporal axis. Two compression methods, which make use of the proposed motion segmentation technique, are presented. The first method uses MPEG-4 coder and 9/7-biorthogonal wavelet for compressing the moving and stationary portions of the video respectively. The second method uses 5/3-biorthogonal wavelet for compressing both the moving and the stationary portions of the video. The performances of these compression algorithms are evaluated in terms of PSNR and bitrate. From the experimental results, it is found that the proposed motion technique improves the performance of the MPEG-4 coder. Among the two compression methods presented, the MPEG-4 based method performs better for bitrates less than 767 Kbps whereas for bitrates above 767 Kbps the performance of the wavelet based method is found superior.
IEEE Transactions on Information Technology in Biomedicine, 2008
This paper presents a comparison of the performances of neural network and linear predictors for ... more This paper presents a comparison of the performances of neural network and linear predictors for near-lossless compression of EEG signals. Three neural network predictors, namely, single-layer perceptron (SLP), multilayer perceptron (MLP), and Elman network (EN), and two linear predictors, namely, autoregressive model (AR) and finite-impulse response filter (FIR) are used. For all the predictors, uniform quantization is applied on the residue signals obtained as the difference between the original and the predicted values. The maximum allowable reconstruction error delta is varied to determine the theoretical bound delta 0 for near-lossless compression and the corresponding bit rate rp. It is shown that among all the predictors, the SLP yields the best results in achieving the lowest values for delta 0 and rp. The corresponding values of the fidelity parameters, namely, percent of root-mean-square difference, peak SNR and cross correlation are also determined. A compression efficiency of 82.8% is achieved using the SLP with a near-lossless bound delta 0 = 3, with the diagnostic quality of the reconstructed EEG signal preserved. Thus, the proposed near-lossless scheme facilitates transmission of real time as well as offline EEG signals over network to remote interpretation center economically with less bandwidth utilization compared to other known lossless and near-lossless schemes.
For the diagnosis of epiiepsy, Electroencephalogram (EEG) signal plays an important role. EEG rec... more For the diagnosis of epiiepsy, Electroencephalogram (EEG) signal plays an important role. EEG recordings of an epileptic patient obtained from ‘ambulatory recording systems’ contain a large volume of EEG data. A time consuming analysis of the entire length of EEG data by an expert is required to detect the epileptic activity. This paper discusses an automated diagnostic method using artificial neural networks for the detection of epilepsy. Experimental resuIts obtained with four different types of neural networks, namely, multi-Iayer perceptron, Elman network, probabilistic neural network and learning vector quantization are presented. It is found that the Elman network performs better than the other three neural networks. It is also shown that the performance of the Elman network with a single attribute as the input is almost identical to that of the recently reported LAMSTAR network which uses two attributes as inputs.
Lossless compression of electroencephalograph (EEG) data is of great interest to the biomedical r... more Lossless compression of electroencephalograph (EEG) data is of great interest to the biomedical research community. Lossless compression through neural network is achieved by using the net as a predictor and coding the prediction error in a lossless manner. The predictive neural network uses a certain number of past samples to predict the present one and in most cases, the differences between the actual and predicted values are zero or close to zero. Entropy coding techniques such as Huffman and arithmetic coding are used in the second stage to achieve a high degree of compression. Predictive coding schemes based on single- layer and multi-layer perceptron networks and recurrent network models are investigated in this paper. Compression results are reported for EEG's recorded under various clinical conditions. These results are compared with those obtained by using linear predictors such as FIR and lattice filters.
Aeu-international Journal of Electronics and Communications, 2009
Compression and transmission of electroencephalography (EEG) signal for telemedicine applications... more Compression and transmission of electroencephalography (EEG) signal for telemedicine applications have made the stringent demand on the quality of the reconstructed signal. Although lossless compression scheme involving predictor–encoder satisfies the perfect reconstruction quality, the amount of compression it yields is very less thereby utilizing more bandwidth. Facing this reality, more and more users in medical community accept near-lossless methods to
International journal of telemedicine and applications, 2012
Developments of new classes of efficient compression algorithms, software systems, and hardware f... more Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further en...
International journal of telemedicine and applications, 2011
A telemedicine system using communication and information technology to deliver medical signals s... more A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.
... [4] NB Amor, N. Essoukri, B. Amara, DICOM image compression by wavelet transform, in: Proceed... more ... [4] NB Amor, N. Essoukri, B. Amara, DICOM image compression by wavelet transform, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 2, October 2002, pp. ... 11 (10) (2004) 806–809. [14] Z. Wang, AC Bovik, HR Sheikh, EP Simoncelli ...
Recent advances in digital imaging technology have greatly enhanced the interpretation of critica... more Recent advances in digital imaging technology have greatly enhanced the interpretation of critical/pathology conditions from the 2-dimensional medical images. This has become realistic due to the existence of the computer aided diagnostic tool. A computer aided diagnostic (CAD) tool generally possesses components like preprocessing, identification/selection of region of interest, extraction of typical features and finally an efficient classification system. This paper enumerates on development of CAD tool for classification of chronic liver disease through the 2-D image acquired from ultrasonic device. Characterization of tissue through qualitative treatment leads to the detection of abnormality which is not viable through qualitative visual inspection by the radiologist. Common liver diseases are the indicators of changes in tissue elasticity. One can show the detection of normal, fatty or malignant condition based on the application of CAD tool thereby, further investigation required by radiologist can be avoided. The proposed work involves an optimal block analysis (64 × 64) of the liver image of actual size 256 × 256 by incorporating Gabor wavelet transform which does the texture classification through automated mode. Statistical features such as gray level mean as well as variance values are estimated after this preprocessing mode. A non-linear back propagation neural network (BPNN) is applied for classifying the normal (vs) fatty and normal (vs) malignant liver which yields a classification accuracy of 96.8%. Further multi classification is also performed and a classification accuracy of 94% is obtained. It can be concluded that the proposed CAD can be used as an expert system to aid the automated diagnosis of liver diseases.
Electroencephalograms (EEG) are the brain signals that provide us the valuable information about ... more Electroencephalograms (EEG) are the brain signals that provide us the valuable information about the normal or epileptic state of the brain. In this paper the EEG signals were characterized by wavelet, sample and spectral entropy approach and the recurrent neural network classifier is used for the automated detection of epileptic seizures.
Computer assisted automated detection is highly inevitable for recognizing neurological disorders... more Computer assisted automated detection is highly inevitable for recognizing neurological disorders, as it involves continuous monitoring of Electroencephalogram (EEG) signal. Being a non-stationary signal, suitable analysis is essential for EEG to differentiate the normal ...
The extensible markup language (XML), which has emerged as a standard for structuring documents, ... more The extensible markup language (XML), which has emerged as a standard for structuring documents, notably for World Wide Web is of great interest to the bioinformatics research community. Biological sequences that are stored in different databanks require standard software computational tools for sequence identifications, structural analysis and visualization. Recently bioinformatics applications make use of XML technology due to its flexibility, durability, data modeling capability, data interoperability and open framework possibility for defining standards. The traditional architectures available in literature are lagging in terms of standard formats for biological data integration. This work describes the features and limitations of those architectures and investigates the need for a remodeled architecture in terms of using technologies such as extensible style sheet languages (XSL), XSL translators (XSLT), document object model (DOM) and application program interface (API). The proposed architecture is intended to extract and handle the biological data from the different databanks.
For a given DNA sequence, it is well known that pair wise alignment schemes are used to determine... more For a given DNA sequence, it is well known that pair wise alignment schemes are used to determine the similarity with the DNA sequences available in the databanks. The efficiency of the alignment decides the type of amino acids and its corresponding proteins. In order to evaluate the given DNA sequence for its proteomic identity, a pattern matching approach is proposed in this paper. A block based semi-global alignment scheme is introduced to determine the similarity between the DNA sequences (known and given). The two DNA sequences are divided into blocks of equal length and alignment is performed which minimizes the computational complexity. The efficiency of the alignment scheme is evaluated using the parameter, percentage of similarity (POS). Four essential DNA version of the amino acids that emphasize the importance of proteomic functionalities are chosen as patterns and matching is performed with the known and given DNA sequences to determine the similarity between them. The ratio of amino acid counts between the two sequences is estimated and the results are compared with that of the POS value. It is found from the experimental results that higher the POS value and the pattern matching higher are the similarity between the two DNA sequences. The optimal block is also identified based on the POS value and amino acids count.
Two-stage lossless data compression methods involving predictors and encoders are well known. Thi... more Two-stage lossless data compression methods involving predictors and encoders are well known. This paper discusses the application of context based error modeling techniques for neural network predictors used for the compression of EEG signals. Error modeling improves the performance of a compression algorithm by removing the statistical redundancy that exists among the error signals after the prediction stage. In this paper experiments are carried out by using human EEG signals recorded under various physiological conditions to evaluate the effect of context based error modeling in the EEG compression. It is found that the compression efficiency of the neural network based predictive techniques is significantly improved by using the error modeling schemes. It is shown that the bits per sample required for EEG compression with error modeling and entropy coding lie in the range of 2.92 to 6.62 which indicates a saving of 0.3 to 0.7 bits compared to the compression scheme without error modeling.
IEEE Transactions on Information Technology in Biomedicine, 2008
Lossless compression of EEG signal is of great importance for the neurological diagnosis as the s... more Lossless compression of EEG signal is of great importance for the neurological diagnosis as the specialists consider the exact reconstruction of the signal as a primary requirement. This paper discusses a lossless compression scheme for EEG signals that involves a predictor and an adaptive error modeling technique. The prediction residues are arranged based on the error count through an histogram computation. Two optimal regions are identified in the histogram plot through a heuristic search such that the bit requirement for encoding the two regions is minimum. Further improvement in the compression is achieved by removing the statistical redundancy that is present in the residue signal by using a context-based bias cancellation scheme. Three neural network predictors, namely, single-layer perceptron, multilayer perceptron, and Elman network and two linear predictors, namely, autoregressive model and finite impulse response filter are considered. Experiments are conducted using EEG signals recorded under different physiological conditions and the performances of the proposed methods are evaluated in terms of the compression ratio. It is shown that the proposed adaptive error modeling schemes yield better compression results compared to other known compression methods.
Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-... more Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.
In this paper, 3-D discrete Hartley transform is applied for the compression of two medical modal... more In this paper, 3-D discrete Hartley transform is applied for the compression of two medical modalities, namely, magnetic resonance images and X-ray angiograms and the performance results are compared with those of 3-D discrete cosine and Fourier transforms using the parameters such as PSNR and bit rate. It is shown that the 3-D discrete Hartley transform is better than the other two transforms for magnetic resonance brain images whereas for the X-ray angiograms, the 3-D discrete cosine transform is found to be superior.
In this paper, the performance of different types of perceptron predictors for EEG signal compres... more In this paper, the performance of different types of perceptron predictors for EEG signal compression is investigated. A two-stage lossless compression scheme which involves the decorrelation of EEG samples in the first stage and entropy coding in the second stage is considered. The second stage employs an arithmetic coding scheme. A comparison of the performance of the perceptron predictors with that of linear predictors such as FIR, NLMS is presented. It is found that the single-layer perceptron performs, in general, better than the multi-layer perceptrons as well as linear predictors.
IEEE Transactions on Information Technology in Biomedicine, 2007
The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The E... more The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time-consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system. Two different types of neural networks, namely, Elman and probabilistic neural networks, are considered in this paper. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system.
The volume of patient monitoring video acquired in hospitals is very huge and hence there is a ne... more The volume of patient monitoring video acquired in hospitals is very huge and hence there is a need for better compression of the same for effective storage and transmission. This paper presents a new motion segmentation technique, which improves the compression of patient monitoring video. The proposed motion segmentation technique makes use of a binary mask, which is obtained by thresholding the standard deviation values of the pixels along the temporal axis. Two compression methods, which make use of the proposed motion segmentation technique, are presented. The first method uses MPEG-4 coder and 9/7-biorthogonal wavelet for compressing the moving and stationary portions of the video respectively. The second method uses 5/3-biorthogonal wavelet for compressing both the moving and the stationary portions of the video. The performances of these compression algorithms are evaluated in terms of PSNR and bitrate. From the experimental results, it is found that the proposed motion technique improves the performance of the MPEG-4 coder. Among the two compression methods presented, the MPEG-4 based method performs better for bitrates less than 767 Kbps whereas for bitrates above 767 Kbps the performance of the wavelet based method is found superior.
IEEE Transactions on Information Technology in Biomedicine, 2008
This paper presents a comparison of the performances of neural network and linear predictors for ... more This paper presents a comparison of the performances of neural network and linear predictors for near-lossless compression of EEG signals. Three neural network predictors, namely, single-layer perceptron (SLP), multilayer perceptron (MLP), and Elman network (EN), and two linear predictors, namely, autoregressive model (AR) and finite-impulse response filter (FIR) are used. For all the predictors, uniform quantization is applied on the residue signals obtained as the difference between the original and the predicted values. The maximum allowable reconstruction error delta is varied to determine the theoretical bound delta 0 for near-lossless compression and the corresponding bit rate rp. It is shown that among all the predictors, the SLP yields the best results in achieving the lowest values for delta 0 and rp. The corresponding values of the fidelity parameters, namely, percent of root-mean-square difference, peak SNR and cross correlation are also determined. A compression efficiency of 82.8% is achieved using the SLP with a near-lossless bound delta 0 = 3, with the diagnostic quality of the reconstructed EEG signal preserved. Thus, the proposed near-lossless scheme facilitates transmission of real time as well as offline EEG signals over network to remote interpretation center economically with less bandwidth utilization compared to other known lossless and near-lossless schemes.
For the diagnosis of epiiepsy, Electroencephalogram (EEG) signal plays an important role. EEG rec... more For the diagnosis of epiiepsy, Electroencephalogram (EEG) signal plays an important role. EEG recordings of an epileptic patient obtained from ‘ambulatory recording systems’ contain a large volume of EEG data. A time consuming analysis of the entire length of EEG data by an expert is required to detect the epileptic activity. This paper discusses an automated diagnostic method using artificial neural networks for the detection of epilepsy. Experimental resuIts obtained with four different types of neural networks, namely, multi-Iayer perceptron, Elman network, probabilistic neural network and learning vector quantization are presented. It is found that the Elman network performs better than the other three neural networks. It is also shown that the performance of the Elman network with a single attribute as the input is almost identical to that of the recently reported LAMSTAR network which uses two attributes as inputs.
Lossless compression of electroencephalograph (EEG) data is of great interest to the biomedical r... more Lossless compression of electroencephalograph (EEG) data is of great interest to the biomedical research community. Lossless compression through neural network is achieved by using the net as a predictor and coding the prediction error in a lossless manner. The predictive neural network uses a certain number of past samples to predict the present one and in most cases, the differences between the actual and predicted values are zero or close to zero. Entropy coding techniques such as Huffman and arithmetic coding are used in the second stage to achieve a high degree of compression. Predictive coding schemes based on single- layer and multi-layer perceptron networks and recurrent network models are investigated in this paper. Compression results are reported for EEG's recorded under various clinical conditions. These results are compared with those obtained by using linear predictors such as FIR and lattice filters.
Aeu-international Journal of Electronics and Communications, 2009
Compression and transmission of electroencephalography (EEG) signal for telemedicine applications... more Compression and transmission of electroencephalography (EEG) signal for telemedicine applications have made the stringent demand on the quality of the reconstructed signal. Although lossless compression scheme involving predictor–encoder satisfies the perfect reconstruction quality, the amount of compression it yields is very less thereby utilizing more bandwidth. Facing this reality, more and more users in medical community accept near-lossless methods to
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