Different Techniques and Algorithms for Biomedical Signal Processing

This paper is intended to give a broad overview of the complex area of biomedical and their use in signal processing. It contains sufficient theoretical materials to provide some understanding of the techniques involved for the researcher in the field. This paper consists of two parts: feature extraction and pattern recognition. The first part provides a basic understanding as to how the time domain signal of patient are converted to the frequency domain for analysis. The second part provides basic for understanding the theoretical and practical approaches to the development of neural network models and their implementation in modeling biological syste

Short-segment heart sound classification using an ensemble of deep convolutional neural networks

This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional time-frequency feature maps based on Mel-frequency cepstral coefficients (MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities. On the large PhysioNet CinC challenge 2016 database, the proposed CNN models outperformed traditional classifiers based on support vector machine and hidden Markov models with various hand-crafted time- and frequency-domain features. Best classification scores with 89.22% accuracy and 89.94% sensitivity were achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by the 2D-CNN alone on the test set.Comment: 8 pages, 1 figure, conferenc

Analysis and classification of myocardial infarction tissue from echocardiography images based on texture analysis

Texture analysis is an important characteristic for automatic visual inspection for surface and object identification from medical images and other type of images. This paper presents an application of wavelet extension and Gray level cooccurrence matrix (GLCM) for diagnosis of myocardial infarction tissue from echocardiography images. Many of applications approach have provided good result in different fields of application, but could not implemented at all when texture samples are small dimensions caused by low quality of images. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposition images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. The gray level co-occurrence matrices are computed for each sub-band. The feature vector of testing image and other feature vector as normal image classified by Mahalanobis distance to decide whether the test image is infarction or not

Discriminative Tandem Features for HMM-based EEG Classification

Abstract—We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system. Two sets of tandem features are derived from linear discriminant analysis (LDA) projection output and multilayer perceptron (MLP) class-posterior probability, before appended to the standard autoregressive (AR) features. Evaluation on a two-class motor-imagery classification task shows that both the proposed tandem features yield consistent gains over the AR baseline, resulting in significant relative improvement of 6.2% and 11.2 % for the LDA and MLP features respectively. We also explore portability of these features across different subjects. Index Terms- Artificial neural network-hidden Markov models, EEG classification, brain-computer-interface (BCI)

Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models

Recent studies on analyzing dynamic brain connectivity rely on sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes simultaneously. Emerging evidence suggests state-related changes in brain connectivity where dependence structure alternates between a finite number of latent states or regimes. Another challenge is inference of full-brain networks with large number of nodes. We employ a Markov-switching dynamic factor model in which the state-driven time-varying connectivity regimes of high-dimensional fMRI data are characterized by lower-dimensional common latent factors, following a regime-switching process. It enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We consider the switching VAR to quantity the dynamic effective connectivity. We propose a three-step estimation procedure: (1) extracting the factors using principal component analysis (PCA) and (2) identifying dynamic connectivity states using the factor-based switching vector autoregressive (VAR) models in a state-space formulation using Kalman filter and expectation-maximization (EM) algorithm, and (3) constructing the high-dimensional connectivity metrics for each state based on subspace estimates. Simulation results show that our proposed estimator outperforms the K-means clustering of time-windowed coefficients, providing more accurate estimation of regime dynamics and connectivity metrics in high-dimensional settings. Applications to analyzing resting-state fMRI data identify dynamic changes in brain states during rest, and reveal distinct directed connectivity patterns and modular organization in resting-state networks across different states.Comment: 21 page

Year-Long Monthly Rainfall Forecasting for a Coastal Environment of Bangladesh

Forecasting rainfall plays an important role to develop, planning and management a sustainable water resource system. In this study stochastic Seasonal Auto Regressive Integrated Moving Average (SARIMA) were used to forecast monthly rainfall of Teknaf for 12 month lead time. The best SARIMA (0, 0, 0) (1, 1, 1) model was selected based on Normalized BIC (Bayesian Information Criteria) and R-squared. Diagnostic check was then conducted for the best fitted model to check if the residuals are white noise. The predicted rainfall amount from the best fitted model was compared with the observed data. The predicted values shows reasonably good result. Thus the model can be used for future rainfall prediction. Keywords: Bangladesh, Teknaf, Rainfall, ARIMA, Forecas

Simulation and Assessment of Groundwater for Domestic and Irrigation Uses

The alluvial fan of Mandali located between latitude 30˚45’00” N longitude 45˚30’00” E in east of Diyala Governorate, Iraq. Thirty-five wells were identified in the study area with average depth of 84 m and estimated area of 21550 ha. A three-dimensional conceptual model was prepared by using GMS program. From wells cross sections, four geological layers have been identified. The hydraulic conductivity of these layers was calculated for steady state condition, where the water levels for nine wells distributed over the study area were observed at same time. Afterward, PEST facility in the GMS was used to estimate the aquifer hydraulic characteristics. Other characteristics such as storage coefficient and specific yield have been determined from one year field observations that were collected by General Authority of Groundwater, Diyala Governorate. Also, the observations were used for calibration of unsteady state model. Then wells were hypothetically redistributed and increased to 103 wells, assuming a distance of 1500 m between the wells, a well productivity rate of were 7 l/s, annual rainfall rate was used for recharging. Three different wells operating times were suggested and these 6, 12, and 18 hr/day with total discharge of 150, 300, 450 m3/day and maximum drawdown of 7, 11, and 20 m respectively. For water quality assessment, the collected groundwater samples were analysed at the laboratory.  Results showed that the TDS in all wells was ranged from 1000-3000 mg/l but TDS in well number 18 was exceeded 3000 mg/l which indicate that the groundwater in this well is not recommended to be used for irrigation. According to Iraqi standard for drink (IQS 2009), it can be used for drinking if saline treatment units were provided