Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier.

Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a significant measurement benchmark to assess patients suffering from epilepsy. This paper investigates the application of multi-features derived from different domains to recognize the focal and non focal epileptic seizures obtained from pharmacoresistant focal epilepsy patients from Bern Barcelona database. From the dataset, five different classification tasks were formed. Total 26 features were extracted from focal and non focal EEG. Significant features were selected using Wilcoxon rank sum test by setting p-value (p < 0.05) and z-score (-1.96 > z > 1.96) at 95% significance interval. Hypothesis was made that the effect of removing outliers improves the classification accuracy. Turkey's range test was adopted for pruning outliers from feature set. Finally, 21 features were classified using optimized support vector machine (SVM) classifier with 10-fold cross validation. Bayesian optimization technique was adopted to minimize the cross-validation loss. From the simulation results, it was inferred that the highest sensitivity, specificity, and classification accuracy of 94.56%, 89.74%, and 92.15% achieved respectively and found to be better than the state-of-the-art approaches. Further, it was observed that the classification accuracy improved from 80.2% with outliers to 92.15% without outliers. The classifier performance metrics ensures the suitability of the proposed multi-features with optimized SVM classifier. It can be concluded that the proposed approach can be applied for recognition of focal EEG signals to localize epileptogenic zones.

Automated region mask for four-chamber fetal heart biometry.

This letter proposes an automated region mask for the detection of cardiac chambers from ultrasonic fetal heart biometry. The fetal biometry consists of two dimensional ultrasonic cine-loop sequences of apical four chamber view of fetal heart, which are comparatively The clinical motion information of individual frame is extracted by keeping a constant frame rate of 25 frames per second (fps). The region mask is designed based on the superimposition of motion information from a set of consecutive frames that belong to one cardiac cycle followed by connected component labelling. The borders and edges of all four chambers are thus recognized leading to formation of binary region mask. Experimental study based on second trimester cine-loop sequences confirms the suitability of the proposed technique for detection of heart chambers.

Identification of apnea during respiratory monitoring using support vector machine classifier: a pilot study.

To determine the use of photoplethysmography (PPG) as a reliable marker for identifying respiratory apnea based on time-frequency features with support vector machine (SVM) classifier. The PPG signals were acquired from 40 healthy subjects with the help of a simple, non-invasive experimental setup under normal and induced apnea conditions. Artifact free segments were selected and baseline and amplitude variabilities were derived from each recording. Frequency spectrum analysis was then applied to study the power distribution in the low frequency (0.04-0.15 Hz) and high frequency (0.15-0.40 Hz) bands as a result of respiratory pattern changes. Support vector machine (SVM) learning algorithm was used to distinguish between the normal and apnea waveforms using different time-frequency features. The algorithm was trained and tested (780 and 500 samples respectively) and all the simulations were carried out using linear kernel function. Classification accuracy of 97.22 % was obtained for the combination of power ratio and reflection index features using SVM classifier. The pilot study indicates that PPG can be used as a cost effective diagnostic tool for detecting respiratory apnea using a simple, robust and non-invasive experimental setup. The ease of application and conclusive results has proved that such a system can be further developed for use in real-time monitoring under critical care conditions.

Wireless CardioS framework for continuous ECG acquisition.

A first-level textile-based electrocardiogram (ECG) monitoring system referred to as "CardioS" (cardiac sensor) for continuous health monitoring applications is proposed in this study to address the demand for resource-constrained environments. and the signal quality assessment of a wireless CardioS was studied. The CardioS consists of a Lead-I ECG signal recorded wirelessly using silver-plated nylon woven (Ag-NyW) dry textile electrodes to compare the results of wired wearable Ag-NyW textile electrode-based ECG acquisition system and CardioS. The effect of prolonged usage of Ag-NyW dry electrodes on electrode impedance was tested in the current work. In addition, electrode half-cell potential was measured to validate the range of Ag-NyW dry electrodes for ECG signal acquisition. Further, the quality of signals recorded by the proposed wireless CardioS framework was evaluated and compared with clinical disposable (Ag-AgCl Gel) electrodes. The signal quality was assessed in terms of mean magnitude coherence spectra, signal cross-correlation, signal-to-noise-band ratio (Sband/Nband), crest factor, low and high band powers and power spectral density. The experimental results showed that the impedance was increased by 2.5-54.6% after six weeks of continuous usage. This increased impedance was less than 1 MΩ/cm2, as reported in the literature. The half-cell potential of the Ag-NyW textile electrode obtained was 80 mV, sufficient to acquire the ECG signal from the human body. All the fidelity parameters measured by Ag-NyW textile electrodes were correlated with standard disposable electrodes. The cardiologists validated all the measurements and confirmed that the proposed framework exhibited good performance for ECG signal acquisition from the five healthy subjects. As a result of its low-cost architecture, the proposed CardioS framework can be used in resource-constrained environments for ECG monitoring.

A Signal Processing Framework for the Detection of Abnormal Cardiac Episodes.

MOTIVATION: Cardiologists rely on the long duration Holter electrocardiogram (ECG) recordings in general for assessment of abnormal episodes and such process found to be tedious and time consuming. An automatic abnormal cardiac episode detection algorithm is the need of the hour that needs to be optimized to reduce the manual burden. OBJECTIVE: The current study presents a signal processing framework with a cross-database to detect abnormal episodes in long-term ECG signals. METHODOLOGY: The data was pre-processed to remove power line interference and baseline drift using basis pursuit sparsely decomposed tunable-Q wavelet transform (BPSD-TQWT). A total of 44 features of time domain, frequency domain, and time-frequency domain characteristics were extracted from the ECG signal. This proposed work tested classification performance with support vector machine (SVM), K-nearest neighbour (KNN), decision tree, naïve Bayes, the nearest mean classifier, and the nearest root mean square classifiers. The trained models with open-source data were used to predict the abnormal episodes from the proprietary database and vice versa. Finally, the performance was analysed via recall rate, specificity, precision, F1-score, and accuracy. RESULTS: Among six classification models, SVM performed best. With an open-source database, the SVM model achieved 95.01% accuracy, and detected the abnormal episodes from proprietary database with an accuracy of 99.31%. In addition, with the proprietary database SVM model classified the normal-abnormal cardiac episodes with an accuracy of 99.89% and detected the abnormal episodes from proprietary database with an accuracy of 92.51%. CONCLUSION: When the performance results were compared with the literature, it was observed that the proposed framework performed well. As a result, the proposed framework could be used in an autonomous diagnosis system.

Performance evaluation of computer-aided automated master frame selection techniques for fetal echocardiography.

PURPOSE: Fetal echocardiography is widely used for the assessment of fetal heart development and detection of congenital heart disease (CHD). Preliminary examination of the fetal heart involves the four-chamber view which indicates the presence of all the four chambers and its structural symmetry. Examination of various cardiac parameters is generally done using the clinically selected diastole frame. This largely depends on the expertise of the sonographer and is prone to intra- and interobservational errors. To overcome this, automated frame selection technique is proposed for the recognition of fetal cardiac chamber from fetal echocardiography. METHODS: Three techniques have been proposed in this research study to automate the process of determining the frame referred as "Master Frame" that can be used for the measurement of the cardiac parameters. The first method uses frame similarity measures (FSM) for the determination of the master frame from the given cine loop ultrasonic sequences. FSM makes use of similarity measures such as correlation, structural similarity index (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE) to identify the cardiac cycle, and all the frames in one cardiac cycle are superimposed to form the master frame. The final master frame is obtained by considering the average of the master frame obtained using each similarity measure. The second method uses averaging of ± 20% from the midframes (AMF). The third method uses averaging of all the frames (AAF) of the cine loop sequence. Both diastole and master frames have been annotated by the clinical experts, and their ground truths are compared for validation. No segmentation techniques have been used to avoid the variability of the performance of various segmentation techniques. All the proposed schemes were evaluated using six fidelity metrics such as Dice coefficient, Jaccard ratio, Hausdorff distance, structural similarity index, mean absolute error, and Pratt figure of merit. RESULTS: The three proposed techniques were tested on the frames extracted from 95 ultrasound cine loop sequences between 19 and 32 weeks of gestation. The feasibility of the techniques was determined by the computation of fidelity metrics between the master frame derived and the diastole frame chosen by the clinical experts. The FSM-based identified master frame found to closely match with manually chosen diastole frame and also ensures statistically significant. The method also detects automatically the cardiac cycle. The resultant master frame obtained through AMF though found to be identical to that of the diastole frame, the size of the chambers found to be reduced that can lead to inaccurate chamber measurement. The master frame obtained through AAF was not found to be identical to that of clinical diastole frame. CONCLUSION: It can be concluded that the frame similarity measure (FSM)-based master frame can be introduced in the clinical routine for segmentation followed by cardiac chamber measurements. Such automated master frame selection also overcomes the manual intervention of earlier reported techniques in the literature. The fidelity metrics assessment further confirms the suitability of proposed master frame for automated fetal chamber recognition.

Basis pursuit sparse decomposition using tunable-Q wavelet transform (BPSD-TQWT) for denoising of electrocardiograms.

The electrocardiogram (ECG) is an essential diagnostic tool to identify cardiac abnormalities. So, the primary issue in an ECG acquisition unit is noise interference. Essentially, the prominent ECG noise sources are power line interference (PLI) and Baseline drift (BD). Therefore, in the study, a new technique called the basis pursuit sparse decomposition (BPSD) using tunable-Q wavelet transform (TQWT) is proposed to remove the PLI and BD present in the ECG recordings. Chiefly, the TQWT method is a wavelet transform with distinct Quality factors (Q) which can adjust the signal to the natural non-stationary behaviour in time and space. Further, the method decomposes the signal into high-Quality factor and low-Quality factor components of wavelet coefficients to eliminate PLI and BD by choosing appropriate redundancy (r) and decomposition levels (J2). The 'r' and 'J' values are chosen based on the trial-and-error method concerning signal-to-noise ratio (SNR). It has been found that the PLI noise has been suppressed significantly with the redundancy of 3 and decomposition levels of 10; more so, the BD has been removed with the redundancy of 4 and decomposition levels of 19. The proposed method BPSD-TQWT was evaluated using the open-source MIT-BIH Arrhythmia database and the real-time ECG recordings collected through a wearable Silver Plated Nylon Woven (Ag-NyW) textile-based ECG monitoring system. The performance was then evaluated using fidelity metrics such as SNR, maximum absolute error (MAX), and normalized cross-correlation coefficient (NCC). The results were compared with IIR filter, stationary wavelet transform (SWT), non-local means (NLM) and local means (LM) methods. Using the proposed method on MIT-BIH Arrhythmia Database, performance evaluation parameters such as SNR, MAX, and NCC were improved by 4.3 dB and 6.8 dB, 0.37 and 0.78, 0.2 and 0.46 compared to IIR and SWT methods respectively. On the other hand, using the proposed method on the real-time datasets, values of SNR, MAX, and NCC were improved by 0.3 dB and 0.6 dB, 0.009 and 0.74 and 0.3 and 0.35 compared to IIR and SWT methods respectively. Finally, it can be concluded that the proposed method shows improved performance over IIR, SWT, NLM and LM methods for PLI and BD removal.

Performance evaluation of neural network and linear predictors for near-lossless compression of EEG signals.

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.

Using 3-D Printers and Microvolunteering to Improve Healthcare Access [EMBS News].

With fewer than 800,000 doctors to meet the needs of a population of 1.3 billion people, India is confronting an immense healthcare challenge. Simply put, how can more people get access to quality, affordable medical care in whatever state or territory they happen to live? This question holds relevance not just in India, but in all regions of the world where doctors are in short supply.

Multichannel EEG based inter-ictal seizures detection using Teager energy with backpropagation neural network classifier.

A long-term multichannel electroencephalogram recording plays a crucial role in recognizing the epileptic seizure activities from the brain lobes. This research study investigates the automated detection of epileptic seizures from multichannel electroencephalogram recordings using Teager energy feature. A supervised back-propagation neural network model was implemented to classify the inter-ictal seizures. The study was conducted on multichannel electroencephalogram data that was obtained from Institute of Neuroscience, Ramaiah Memorial Hospital, Bengaluru, India, after ethical clearance from the from the Institutional Ethics Board. Initially, notch filter was applied to remove the 50 Hz power line noise from raw electroencephalogram followed by independent component analysis to remove eye blinks and muscular activities. A time domain feature called Teager energy was estimated which detects the rapid changes in the given electroencephalogram time series. A 1 s windowing was introduced to ensure stationarity for estimation of Teager energy. The descriptive and box plot analysis ensures the suitability of the Teager energy for the seizure detection. The performance of the multilayer perceptron neural network classifier was evaluated using sensitivity, specificity, and false detection rate. Simulation results showed the highest sensitivity, specificity and false detection rate of 96.66%, 99.15%, and 0.30 per hour respectively. It can be concluded that procedure can be applied for real-time seizure detection.

Automated epileptic seizures detection using multi-features and multilayer perceptron neural network.

Detection of epileptic seizure activities from long-term multi-channel electroencephalogram (EEG) signals plays a significant role in the timely treatment of the patients with epilepsy. Visual identification of epileptic seizure in long-term EEG is cumbersome and tedious for neurologists, which might also lead to human error. Therefore, an automated tool for accurate detection of seizures in a long-term multi-channel EEG is essential for the clinical diagnosis. This study proposes an algorithm using multi-features and multilayer perceptron neural network (MLPNN) classifier. After appropriate approval from the ethical committee, recordings of EEG data were collected from the Institute of Neurosciences, Ramaiah Memorial College and Hospital, Bengaluru. Initially, preprocessing was performed to remove the power-line noise and motion artifacts. Four features, namely power spectral density (Yule-Walker), entropy (Shannon and Renyi), and Teager energy, were extracted. The Wilcoxon rank-sum test and descriptive analysis ensure the suitability of the proposed features for pattern classification. Single and multi-features were fed to the MLPNN classifier to evaluate the performance of the study. The simulation results showed sensitivity, specificity, and false detection rate of 97.1%, 97.8%, and 1 h-1, respectively, using multi-features. Further, the results indicate the proposed study is suitable for real-time seizure recognition from multi-channel EEG recording. The graphical user interface was developed in MATLAB to provide an automated biomarker for normal and epileptic EEG signals.

Pattern recognition of spectral entropy features for detection of alcoholic and control visual ERP's in multichannel EEGs.

This paper presents a novel ranking method to select spectral entropy (SE) features that discriminate alcoholic and control visual event-related potentials (ERP'S) in gamma sub-band (30-55 Hz) derived from a 64-channel electroencephalogram (EEG) recording. The ranking is based on a t test statistic that rejects the null hypothesis that the group means of SE values in alcoholics and controls are identical. The SE features with high ranks are indicative of maximal separation between their group means. Various sizes of top ranked feature subsets are evaluated by applying principal component analysis (PCA) and k-nearest neighbor (k-NN) classification. Even though ranking does not influence the performance of classifier significantly with the selection of all 61 active channels, the classification efficiency is directly proportional to the number of principal components (pc). The effect of ranking and PCA on classification is predominantly observed with reduced feature subsets of (N = 25, 15) top ranked features. Results indicate that for N = 25, proposed ranking method improves the k-NN classification accuracy from 91 to 93.87% as the number of pcs increases from 5 to 25. With same number of pcs, the k-NN classifier responds with accuracies of 84.42-91.54% with non-ranked features. Similarly for N = 15 and number of pcs varying from 5 to 15, ranking enhances k-NN detection accuracies from 88.9 to 93.08% as compared to 86.75-91.96% without ranking. This shows that the detection accuracy is increased by 6.5 and 2.8%, respectively, for N = 25, whereas it enhances by 2.2 and 1%, respectively, for N = 15 in comparison with non-ranked features. In the proposed t test ranking method for feature selection, the pcs of only top ranked feature candidates take part in classification process and hence provide better generalization.

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier.

Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.

Medical image compression using 3-D Hartley transform.

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.

An Engineering Perspective of External Cardiac Loop Recorder: A Systematic Review.

External cardiac loop recorder (ELR) is a kind of ECG monitoring system that records cardiac activities of a subject continuously for a long time. When the heart palpitations are not the frequent and nonspecific character, it is difficult to diagnose the disease. In such a case, ELR is used for long-term monitoring of heart signal of the patient. But the cost of ELR is very high. Therefore, it is not prominently available in developing countries like India. Since the design of ELR includes the ECG electrodes, instrumentation amplifier, analog to digital converter, and signal processing unit, a comparative review of each part of the ELR is presented in this paper in order to design a cost effective, low power, and compact kind of ELR. This review will also give different choices available for selecting and designing each part of the ELR system. Finally, the review will suggest the better choice for designing a cost effective external cardiac loop recorder that helps to make it available even for rural people in India.

Recognition of wake-sleep stage 1 multichannel eeg patterns using spectral entropy features for drowsiness detection.

Electroencephalographic (EEG) activity recorded during the entire sleep cycle reflects various complex processes associated with brain and exhibits a high degree of irregularity through various stages of sleep. The identification of transition from wakefulness to stage1 sleep is a challenging area of research for the biomedical community. In this paper, spectral entropy (SE) is used as a complexity measure to quantify irregularities in awake and stage1 sleep of 8-channel sleep EEG data from the polysomnographic recordings of ten healthy subjects. The SE measures of awake and stage1 sleep EEG data are estimated for each second and applied to a multilayer perceptron feed forward neural network (MLP-FF). The network is trained using back propagation algorithm for recognizing these two patterns. Initially, the MLP network is trained and tested for randomly chosen subject-wise combined datasets I and II and then for the combined large dataset III. In all cases, 60 % of the entire dataset is used for training while 20 % is used for testing and 20 % for validation. Results indicate that the MLP neural network learns with maximum testing accuracy of 95.9 % for dataset II. In the case of combined large dataset, the network performs with a maximum accuracy of 99.2 % with 100 hidden neurons. Results show that in channels O1, O2, F3 and F4 (A1, A2 as reference), the mean of the spectral entropy value is higher in awake state than in stage1 sleep indicating that the EEG becomes more regular and rhythmic as the subject attains stage1 sleep from wakefulness. However, in C3 and C4 the mean values of SE values are not very much discriminative of both groups. This may prove to be a very effective indicator for scoring the first two stages of sleep EEG and may be used to detect the transition from wakefulness to stage1 sleep.

A high-performance lossless compression scheme for EEG signals using wavelet transform and neural network predictors.

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 encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.

Development of a secure body area network for a wearable physiological monitoring system using a PSoC processor.

Wearable physiological monitoring systems have gained popularity in the recent years due to their ability to continuously monitor physiological signals, thereby making them suitable for home-healthcare applications. The electrocardiogram (ECG), phonocardiogram (PCG) and photoplethysmogram (PPG) signals have been studied and it has been observed that there is a correlation between the three signals. This paper proposes the development of a secure body area network (BAN), for a wearable physiological monitoring system. The BAN is composed of three nodes, for ECG, PPG and PCG signals. The peak-peak distances of these signals are calculated first, in the coordinator of BAN. The coordinator is designed in such a manner that signals from it are transmitted to a monitoring station, only if the difference between the peak-peak distances of both ECG-PPG signals and ECG-PCG signals fall below a threshold. The entire operation of the coordinator is implemented using a real-time processor, Cypress(™) Programmable System on Chip (PSoC).

Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors.

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.

Performance evaluation of computer aided diagnostic tool (CAD) for detection of ultrasonic based liver disease.

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 x 64) of the liver image of actual size 256 x 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.