Epileptic Seizure Detection And Prediction From Electroencephalogram Using Neuro-Fuzzy Algorithms

This dissertation presents innovative approaches based on fuzzy logic in epileptic seizure detection and prediction from Electroencephalogram (EEG). The fuzzy rule-based algorithms were developed with the aim to improve quality of life of epilepsy patients by utilizing intelligent methods. An adaptive fuzzy logic system was developed to detect seizure onset in a patient specific way. Fuzzy if-then rules were developed to mimic the human reasoning and taking advantage of the combination in spatial-temporal domain. Fuzzy c-means clustering technique was utilized for optimizing the membership functions for varying patterns in the feature domain. In addition, application of the adaptive neuro-fuzzy inference system (ANFIS) is presented for efficient classification of several commonly arising artifacts from EEG. Finally, we present a neuro-fuzzy approach of seizure prediction by applying the ANFIS. Patient specific ANFIS classifier was constructed to forecast a seizure followed by postprocessing methods. Three nonlinear seizure predictive features were used to characterize changes prior to seizure. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. The ANFIS classifier was constructed based on these features as inputs. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. In this dissertation, the application of the neuro-fuzzy algorithms in epilepsy diagnosis and treatment was demonstrated by applying the methods on different datasets. Several performance measures such as detection delay, sensitivity and specificity were calculated and compared with results reported in literature. The proposed algorithms have potentials to be used in diagnostics and therapeutic applications as they can be implemented in an implantable medical device to detect a seizure, forecast a seizure, and initiate neurostimulation therapy for the purpose of seizure prevention or abortion

A Fuzzy Logic System for Seizure Onset Detection in Intracranial EEG

We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved

IDENTIFICATION OF CAUSES OF DEMAND VARIATION AND ITS IMPACT ON SALES VOLUME - AN EXPLORATORY STUDY IN PROCESSED FOOD INDUSTRY IN BANGLADESH

Demand variation is a key issue in processed food industry, because demands for processed food products vary daily. The organizations in this situation face challenges to meet customer demand. Their products have a definite shelf life and prone to be obsolete. Obsolete products are totally wastes. So, there exists a producer risk. This study has been conducted with the aim of identifying the root causes of demand variation and its impact on sales volume. For this purpose an exploratory study involving two food processing organizations and their forty eight points of sales had been performed. Each food item has different causes and consequences for demand variation. In this regard, three food items having limited shelf life had been selected to find out the causes of their demand variation. The study identified eleven causes and twelve consequences such as special occasion, duration of shelf life, wrong forecasting and so on. Then some root causes are figured out that dominates over others. The impacts of these causes on sales volume are also shown with six months demand data. The research concludes with the level of impact of the significant causes like price, occasion. Lastly some recommendations are mentioned to minimize those root causes of demand variation

Biomedical Signal Transceivers

With the growing costs of healthcare, the need for mobile health monitoring devices is critical. A wireless transceiver provides a cost effective way to transmit biomedical signals to the various personal electronic devices, such as computers, cellular devices, and other mobile devices. Different kinds of biomedical signals can be processed and transmitted by these devices, including electroencephalograph (EEG), electrocardiograph (ECG), and electromyography (EMG). By utilizing wireless transmission, the user gains freedom to connect with fewer constraints to their personal devices to view and monitor their health condition. In this chapter, in the first few sections, we will introduce the reader with the basic design of the biomedical transceivers and some of the design issues. In the subsequent sections, we will be presenting design challenges for wireless transceivers, specially using a common wireless protocol Bluetooth. Furthermore, we will share our experience of implementing a biomedical transceiver for ECG signals and processing them. We conclude the discussion with current trends and future work

12-segment display for the Bengali numerical characters

For representing the Bengali numerical characters the researchers have been working for a long time. In this paper, the idea of 12-segment display is introduced which ensures better outlook than the existing or proposed display systems. A 12-segment display for Bengali Numerical Characters needs 4-bit inputs for representing each digit. Appropriate logic circuits are also designed for that purpose

Internet of Things (IoT) based ECG System for Rural Health Care

Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, pre-processed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System. Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future

A Review on Embryonic Development of Inland Fishes of Bangladesh

The early developmental pattern of inland fishes of Bangladesh are not well studied though it has a great importance in fisheries and aquaculture sector. The embryonic study provides interesting information on further growth and health of the fish and considered as an essential component for optimization of fish seed production by natural and induced breeding. Therefore, the current review work has been undertaken to provide a detail information on embryonic development of important inland fishes of Bangladesh. Information was collected from published scientific papers, un-published Masters and PhD dissertations from universities, popular articles and other published and grey literature. Diameters of unfertilized egg of the reviewed fish species were found to be 0.5 to 1.3 mm and fertilized egg were 0.49 to 1.6 mm. Shapes of the egg were also variable from species to species. There is little information available on egg activation and egg micropyle of fish species of Bangladesh. The fertilization rate of different fishes ranged from 40.1% to 93.9%. There are different stages of early development in different species and time needs to complete the stages also vary. The timing of post hatching development by metamorphosis was found to vary based on the fish species from several days to weeks. Different factors like temperature, photoperiod, DO, seasonality and presence of chemicals in water were found to affect the early development of fish. The review included eighteen inland fishes and unearthed useful insights of their embryonic development and influence of different factors. As we expect, the outcome of the study would provide a baseline and would be very useful in conducting further research on the embryology of indigenous fishes of Bangladesh.&nbsp

Growth and yield of short duration Aman rice (Oryza sativa) cultivars as influenced by age of seedlings

An experiment was conducted at the Agronomy Field Laboratory, Bangladesh Agricultural University (BAU), Mymensingh during the period from July to December 2019 to study the effect of cultivar and seedling age on the performance of short duration transplant Aman rice. The experiment comprised four Aman rice cultivars, viz., BRRI dhan49, BRRI dhan56, BRRI dhan66 and BRRI dhan71, and four seedling ages viz. 20, 25, 30 and 35-day old seedlings. The experiment was laid out in a randomized complete block design with three replications. Results of the study showed that growth, yield and yield contributing characters were significantly influenced by cultivars, seedlings age and their interactions. At growth stage, BRRI dhan49 with 20-day old seedlings produced the tallest plant (57.67 cm and 67.33 cm, respectively), the highest number of total tillers hill-1 (15.00 and 13.67, respectively) and total dry matter (8.03 g m-2 and 11.50 g m-2, respectively) at 30 and 50 DAT. At harvest, the highest number of total and effective tillers hill-1 (12.82 and 12.00), longest panicle (24.50 cm), highest number of grains panicle-1 (128.80), heaviest 1000-grain weight (23.17 g), highest grain yield (5.35 t ha-1) and highest harvest index (51.69 %) were obtained from the cultivar BRRI dhan66. While, thirty-day old seedlings produced the highest number of total and effective tillers hill-1 (13.46 and 12.70), longest panicle (24.67 cm), highest number of grains panicle-1 (136.90), highest grain (5.62 t ha-1) and straw yields (5.81 t ha-1) and harvest index (51.67 %). In case of interactions, BRRI dhan66 with 30-day old seedlings produced the highest number of total and effective tillers hill-1 (14.67 and 13.97), longest panicle (26.00 cm), highest number of grains panicle-1 (146.7), highest grain yield (6.31 t ha-1) and highest harvest index (52.72 %). So, result of the present study reveals that BRRI dhan66 with 30-days old seedlings was found to be the best for obtaining maximum grain yield

Improving spatial agreement in machine learning-based landslide susceptibility mapping

Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions