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Analysis and Visualization of Tissue-motion of Cranial Ultrasonogram Image Sequences for Newborn Babies
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh., 2011-05) Islam, Muhammad Muinul; Ahmad, Prof. Dr. Mohiuddin
Cranial ultrasound scans are very essential part of the routine investigation of neonatal intensive care. The scan ultrasound image sequence are not only used for real-time diagnosis but also recorded as moving images in video recorder as ultrasonographic movie for fitture diagnosis, offline analysis of ultrasonogram images and the study of tissue velocity in neonatal cranium. The tissue motion in neonatal cranium is an important physical parameter which is considered in discussing the pulsation strength of newborn baby for pediatric diagnosis. The artery pulsation has a strong correlation with the blood flow in the newborn baby head and tissue-motion has a relationship with artery pulsation. Optical flow technique finds an excellent application in determining the tissue motion A velocity quantitatively in cranial ultrasonograrn of newborn baby. Cranial ultrasonograrn of newborn babies for different coronal and sagittal sections are studied and the tissue motion velocity of neonatal cranium is analyzed using the optical flow techniques. In order to calculate tissue motion velocity, gradient-based approaches of optical flow techniques with different optical flow optimizations are used. Ultrasonogram image sequences of 32 frames, 640 x 480 pixels/frame, 8bits/pixel, 33 ms/frame for different coronal and sagival sections are used. Whole ultrasonogram is not useful for analysis and small portion of ultrasonogram images are selected during analysis in the region inside cranial bone. Tissue- motions are estimated in different coronal sections and their errors are also estimated. Further, the time variant tissue-motions are analyzed using discrete Fourier transform of optical flow velocity. The pulsation is observed in the time variant tissue motion images and strong pulsation is occurred in the harmonic frequencies of tissue-motion that has a relation to the heartbeat frequency of a newborn baby which is helpful for pediatric diagnosis. Pulsation amplitudes, caused by artery pulsation due to blood now are also analyzed and visualized from a video stream of ultrasound image sequence by using I D fast Fourier transform in brightness mode for future diagnosis using direct pixel value. A new imaging technique, named pulsation amplitude image is proposed. In the proposed method, the tissue motion of typical coronal and sagittal sections of normal and asphyxiated neonates are analyzed and visualized. Significant differences have been observed in cranial tissue motion between normal and abnormal neonate. The results obtained in the present study lead to determine pulsation amplitude that strongly contribute pediatricians in diagnosis of newborn baby’s ischemic diseases.
Clench Strength Prediction for Prosthesis Hand Using Surface Electromyogram
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh., 2012-09) Mostafa, Sheikh Shanawaz; Ahmad, Prof. Dr. Mohiuddin
Hand is the main environmental manipulator for humans. Therefore, any accidental lost of hand does a great harm in amputee life. Only choice left that he or she is using prosthesis, which is dated back to ancient time. Due to their usefulness prosthesis is also used in modern days. Now-a-day robotic rehabilitation opens a new era in the field of prosthesis. They are more capable of doing things compared to their ancestors. However, to duplicate the complex work of hand, the robotic hand is needed to complete two basic operations first one is predict the angular displacement and another one is the estimation of force. Estimation of force is essential for instance when drinking from a glass, one needs to apply sufficient grip force to prevent the object from slipping Out of the hand. In addition, one needs to control the total torque exerted on the glass such that the glass remains vertical. Usually, the requirements for grip force stabilization allow for some laxity, while the requirements for total torque production are highly specified. The grip force needs only to be larger than the slip threshold and smaller than the force that would break the object. Different researchers used different approach to solve this problem like body-powered prostheses, force-sensitive resistors. Nevertheless, when it comes to rehabilitation of amputee they are not up to the mark in practical field.Using of Electromyography (EMG) in the robotic rehabilitation shows some hope. Muscle tissue conducts electrical potentials similar to the way nerves do and the name given to these electrical signals is the muscle action potential. A muscle is composed of bundles of specialized cells capable of contraction and relaxation. The primary function of these specialized cells is to generate forces, movements and the ability to communicate such as speech or writing or other modes of expression. The skeletal muscle tissue is attached to the bone and its contraction is responsible for supporting and moving the skeleton. The contraction of skeletal muscle is initiated by impulses in the neurons to the muscle and is usually under voluntary control. Skeletal muscle fibers are well-supplied with neurons for its contraction. This depolarization, accompanied by a movement of ions, generates an electric field near each muscle fiber. An EMG signal is the train of Motor Unit Action Potential (MUAP) showing the muscle response to neural stimulation. There is a clear relationship between force and EMG. The higher the muscle force, the higher EMG level is developed. Clench force estimation is highly desirable in the field of prosthesis hand. It is one of the most used postures among different types of postures. In this thesis, the author proposes to estimate the clench force using two types of Surface Electromyography (SEMG): rectified SEMG and integrated SEMG. An Artificial Neural Network (ANN) is used to estimate the force from SEMG. For weight adjustment of the estimator Levenberg-Marquardt (L-M) back propagation algorithm is used. The proposed network is trained and tested using SEMG recorded from five subjects. The estimation result clearly shows that integrated SEMG performed 3.53 times better than rectified SEMG in the case of cross correlation coefficient. So integrated SEMG is recommended for clench force estimation. A neural number based experiment is also done to find the optimal number of neurons in hidden layer, which are five neurons as previously described neural network. In addition to that, these result also compared with Support Vector Machine (S\TM). Same result is found for integrated SEMG. In the case of rectified SEMG ANN is more suitable than SVM.
Effective Electrodes Position and Features Selection for EEG Based Epilepsy Detection
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh., 2017-08) Hasan, Md. Kamrul; Ahmad, Prof. Dr. Mohiuddin
Electroencephalogram (EEG) signal is a representative signal that contains information about the brain activity, which is the key identifier and used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure from the EEG signal usually requires a detailed and experienced analysis of EEG data as well as proper collections of epileptic EEG signal from the effective positions of the scalp. In this thesis, we have introduced a statistical analysis of EEG signal with the optimized electrodes and features that are capable of recognizing epileptic seizure with a high degree of accuracy (96.1 %) and helps to provide automatic detection of epileptic seizure for different ages of epileptic persons. To accomplish the target research, we extract various epileptic features namely Approximate Entropy (ApEn), Kolmogorov–Sinai Entropy (KSE), Spectral Entropy (SE), Standard Deviation (SD), Standard Error (SE), Modified Mean Absolute Value (MMAV), Roll-off (R), and Zero Crossing (ZC) from the epileptic EEG signal. The k-nearest neighbor (k-NN) algorithm is used for the classification of epilepsy then regression analysis is used for the prediction of the epilepsy level at different ages of the patients. Using the statistical parameters and regression analysis, a prototype mathematical model is proposed which helps to find the epileptic randomness with respect to age of different subjects. The accuracy of this prototype equation depends on proper analysis of the dynamic information from the epileptic EEG signal.
Formation of Channel Correlation Based Significant Virtual Images from EEG Sub-bands Data to Recognize Human Emotion using Convolutional Neural Network
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh, 2019-07) Islam, Md. Rabiul; Ahmad, Prof. Dr. Mohiuddin
Emotions are the most fundamental feature for non-verbal communication between human and machine. But the improvement of different human interacted media depends largely on the appropriate recognition of the human feeling that means what he or she wants just right at that moment. To communicate computer with human, to detect present feelings of the patient, to understand customer interest for shopping, to realize interest of physically handicapped people or to detect the lie of a convict; emotion recognition is a fundamental prerequisite. But due to some complexities, the proper recognition of human emotion from Electroencephalogram (EEG) has become too much challenging. Normally, feature-based emotion recognition requires a strong effort to design the perfect feature or feature set related to the classification of emotion. To curtail the manual human effort of feature extraction, we designed a model with Convolutional Neural Network (CNN). As Electroencephalogram (EEG) is 1D data, to use CNN the 1D EEG data have to be converted into 2D significant image data. That is a challenging task. To meet up this challenge, initially, we calculated Pearson’s correlation coefficients form different sub-bands of EEG to formulate a virtual image. Later, this virtual image was fed into a CNN architecture to classify emotion. We made two distinct protocols; between these, protocol-1 was to classify positive and negative emotion and protocol-2 was to classify three distinct emotions. Overall maximum accuracy of 76.52% on valence and 76.82% on arousal was obtained by using internationally authorized DEAP dataset. We observed that the Convolutional Neural Network (CNN) based method showed state-of-the-art performance for emotion classification.
Improvement of Medical Imaging Equipment Management System of Bangladesh
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh, 2012-06) Hossain, Md. Anwar; Ahmad, Prof. Dr. Mohiuddin
This thesis deals with the development of a standard Medical Imaging Equipment Management System (MIEMS) in the public hospitals of Bangladesh. Medical imaging equipment management has become an important component of health services delivery in Bangladesh. One of the most serious issues of Hospitals, Medical Center, Clinics and the health Systems is the threat in diagnosing diseases due to improper medical imaging equipment management system in Bangladesh. Presently a proper equipment management set-up does not exist in public hospitals and the overall diagnostic equipment condition in various departments is very poor. To find the facts and reasons the author visited fifteen medical imaging equipment departments in public hospitals of Bangladesh. From the data it is seen that a big percentage of the required management steps were found to be lacking. Based on the findings of the studies and with a view to improving the existing MIEMS, it is proposed to improve the medical imaging equipment management system for public hospitals in Bangladesh. The major findings are insufficient Biomedical Engineering Team, negligible in-house and central MIEMS, weak bridge between users and biomedical engineering team. The Ministry of Health and Family welfare of Bangladesh Govt., maintains a separate Medical Imaging Equipment Management System, which is not consistent with this plan. The MIEMS is designed to assure selection of appropriate medical imaging equipment to support the medical care processes of the nation and its ambulatory care facilities. The program is designed to assure effective preparation of staff responsible for the use or maintenance of the equipment. Finally, the program is designed to assure continuous availability of safe, effective equipment through a planned maintenance program, timely repair and evaluation of all events that could have an adverse impact on safety of patients or staff. The Clinical Biomedical Engineering Department staff has primary responsibility to maintain and repair all medical equipment including equipment under contract maintenance agreement by Vendors. All staff who uses medical equipment is required to learn and implement various general and specific procedures to ensure safe and reliable use of medical equipment. In addition, it will provide a proper guideline for all patient care staff, failures and user errors associated with the use of medical equipment. Regular maintenance and evaluation are necessary to assure that particular equipment delivers the expected performance within specified para
Modeling and Classification of Voluntary and Imagery Movements through fNIR & EEG Signals for Brain-Computer Interface
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh, 2020-01) Rahman, Md. Asadur; Ahmad, Prof. Dr. Mohiuddin
Neural activation measurement regarding voluntary and imagery movement is a crucial argument for brain-computer interface. Generally, movement-related hemodynamics is measured from the central lobe of the brain which may be erroneous for the paralyzed people due to their inactiveness of this brain area. To overcome this limitation, this thesis work proposes an approach to measure the movement-related hemodynamics from the prefrontal cortex. In the proposed research work, the changes of the oxidized hemoglobin (HbO2) and deoxidized hemoglobin (dHb) concentration regarding different voluntary and imagery movement stimuli are captured by functional near-infrared spectroscopy (fNIR) from several subjects. With necessary preprocessing, the fNIR signals are statistically analyzed by ANOVA and effect size method to localize the most significantly activated regions of the prefrontal cortex regarding the voluntary and imagery stimuli. The experimental results show that voluntary and imagery movements have a strong correlation with the prefrontal cortex. The temporal pattern of HbO2 and dHb signals regarding the most activated regions are modeled by polynomial regression. Consequently, the model activation patterns are used to classify the voluntary and imagery tasks based on the maximum similarity approach. In addition, conventional classification methods are used to classify the signals. In this work, we consider two, four, and six class fNIR data of movement-related tasks for classification. The classification accuracies of the proposed method are convincing and found almost similar to the conventional procedure. The outcomes of this proposed work suggest that the prefrontal hemodynamics can be used for the modeling and classification of the voluntary and imagery movement-related tasks which will be helpful for the brain-computer interface applications. The combination of fNIR and electroencephalography (EEG) signals has become the best choice of accurate brain-computer interface (BCI) because of their finer spatiotemporal resolution. The purpose of this work is to develop an effective BCI model to classify the brain signals (fNIR and EEG) regarding the voluntary and imagery movements. For achieving the high classification accuracy from the developed BCI system, Convolutional neural network (CNN) has been used to extract the features automatically from the multiple channel fNIR and EEG signals instead of the manual feature selection. In this work, eight different movement-related stimuli (four voluntary and four imagery movements of hands and feet) have been considered. The multiple channel fNIR and EEG signals are used to prepare functional neuroimages to train and test the performance of the proposed BCI system. In addition, the proposed procedure is applied to prepare neuroimages from the individual modality (fNIR and EEG) to train and test the performance of the CNN based BCI system. The results reveal that the combined-modality approach of fNIR and EEG provides improved classification accuracy than the individual one. From the results, we found that the proposed CNN-based BCI system of bimodal (fNIR+EEG) approach outperforms the unimodal (only fNIR) methods in terms of the classification accuracy. Therefore, the outcomes of the proposed research work will be very helpful to implement the finer BCI system, in future.
Study of Stimulation Effects on Different Bands of EEG Signal
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh., 2016-09) Ghosh, Tarun Kanti; Ahmad, Prof. Dr. Mohiuddin
Brain–computer interfaces (BCIs) are the communication bridge between the human brain and a computer which may be implemented on the basis of steady-state evoked potentials (SSEPs). A brain–computer interface is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions. The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. The objectives of the research is to check the different stimulation effects of human brain, to improve the accuracy, higher information transfer rate (ITR), desired bandwidth (BW), and signal to noise ratio (SNR) of BCIs and to identify Power and Energy of different Stimulation Effects on Alpha and Beta bands of EEG signal. In this research the power and energy of Alpha and Beta bands of EEG signal for different stimulation were determined and signal analysis, signal processing, Fast Fourier Transform (FFT), Statistical parameter methods were used. The performance of stimulator depends on many factors such as size and shape of stimulator, frequency of stimulation, luminance, color, and subject attention. Information Transfer Rate (ITR) varies with the change of frequency and size of the visual stimuli. In this research, a circular repetitive visual stimulator (CRVS) of different diameter (2", 2.5"and 3"), color (RGB), frequency (10, 15 and 20 Hz) was used. When the size of the stimulator changes from 2" to 2.5" a greater increase of Alpha wave (58.18%) is observed than Beta wave (13.68%). But a further increase in size from 2" to 2.5" a greater decrease of Alpha wave (45%) is observed than Beta wave (36.59%).