KUET Institutional Repository
Browse
Item Cardiac Function Evaluation of Healthy Young Adults by Consuming Energy Drinks(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh., 2014-03) Uddin, Md. Bashir; Ahmad, Prof. Dr. Mohiuddin.Energy drink (ED) is a beverage that mainly contains some form of legal stimulants which are supposed to give consumers a short term boost in energy. It is a type of beverage containing caffeine as main stimulant, which is marketed as providing both mental and physical stimulation. With ED becoming a worldwide phenomenon, the short- and longterm effects of these beverages must be evaluated more closely in order to fully comprehend the psychological impact of these products. The market and degree of consumption of ED is increasing every year, but only few have global knowledge of their ingredients and actual physiological and psychological effects. Although ED have been sold worldwide for more than a decade, only a few published studies have examined their effects on health and well-being. The effects of ED consumption on physiological and psychological are more or less investigated but the effects of ED consumption on heart activity or cardiac function aren't well studied. The main aim of this study is to evaluate cardiac function of healthy young adults by consuming ED analyzing different cardiac signals. The modeling of cardiac signals with consuming ED is the partial objective of this study.The electrical activity of the heart over a period of time can be represented by the properties of Electrocardiogram (ECG) wave. Photo Plethysmogram (PPG) is the tactilearterial palpation of the heartbeat which is also known as pulse. ECG and PPG signals are used in this study as cardiac signals which are recorded by biopac accessories from healthy human subjects. ECG is recorded using electrode lead set connected to MP36 data acquisition unit. PPG (pulse) is also recorded using pulse transducer connected to the same MP36 data acquisition unit. ECG and PPG are recorded at both before and after the consumption of ED. The Laser Doppler Flowmetry (LDF) recording is also done for results verification for certain subjects. The consumption of ED affects heart activity that is determined in this study using electrocardiographic and photo plethysmographic parameters. The ECG parameters analysis show significant reduction in their corresponding amplitude as well as heart rate due to having ED. The amplitude of R wave of ECG increases little bit that may give short-term boost of energy. A notable decrement in peak to peak amplitude of PPG as well as pulse rate is observed due to having ED. The spectrum or frequency components for ECG as well as PPG signal decreases with a significant rate from the instant of having ED. That is, the spectrum parameters of cardiac activity decrease due to the consumption of energy drinks. The spectrum analysis of LDF signal also results similar type of decrement in their spectrum parameters for same type of energy drinks consumption. This LDF signal analysis validates our main experimental results. These results reflect adverse impacts of energy drinks consumption on cardiac activity. ECG is the main cardiac signal which represents complete cardiac function which is the measurement of bio-electric potential produced by rhythmical cardiac activities (contraction and relaxation) of the cardiac muscle. Different cardiac functions can be easily identified by ECG that's why ECG modeling is the most important with the consumption of energy drinks. Different techniques have been developed in the past for modeling of ECG. An ECG model is proposed in this study using peak amplitude based Gaussian function with some modifications in both before and after having ED. This model is best suited with practical ECG parameters. Using this ECG model it is possible to find out cardiac parameters which are needed for the evaluation of cardiac function with having ED. In this research, we have find out cardiac parameters using our ECG model and compared with real ECG parameters. The comparison results less error between real ECG and model ECG in the evaluation of cardiac function with consuming ED. Thus this ECG model is effective to evaluate cardiac function with the consumption of EDItem Determination of Muscle Activity and Work-Done Through Emg Analysis During Human Movements in Salat(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh., 2016-05) Khanam, Farzana; Ahmad, Prof. Dr. Mohiuddin.In this thesis, human movements during Salat (the ritual prayer of Muslim) are taken in account to observe this muscle movement patterns as exercise. Since surface electromyography (sEMG) is a proven method to observe the muscle activity based on the generated action potential by the muscles, sEMG method is applied to measure the physical activities from different muscles of the different steps of Salat. From these measurements, it has been shown that muscle activity during Salat can perform better exercise than walking in treadmill based on muscle fatigue index. The results specify that during Salat, Biceps Brachii (BB) and Erector Spine (ES) produce improved EMG level for both male and female subjects in opposition to Treadmill exercise. Therefore, the aftermath of this work gives us a message that the person who performs Salat five times in a day is doing exercise of upper limb muscles especially the BB and ES muscles. Furthermore, a modified power spectrum analyzing method is developed. By this proposed method it is proved that mean frequency (MNF) based EMG power spectrum analyzing method is comparatively efficient method than the previously proposed to determine Salat associated muscle fatigue and indices. For finding the relation between work done and corresponding sEMG voltage a number of subjects (male and female) are studied with a known work done and their corresponding sEMG voltages are measured. As a result, some approximate mathematical functions are developed by appropriate curve fitting to relate the work done and corresponding EMG voltage. These mathematical functions are proposed for upper limb (Bicep Bracii) muscle and lower limb (Biceps Femoris and Medial Gastrocnemius) muscle. The estimated relationship between work done and sEMG voltage show minimum error when it actually follows second degree function. Based on the work, it is observed that the functions vary from muscle to muscle which means for upper limb and lower limb the relationships follow different mathematical functions. It is also found that the sEMG voltage generated from muscle undergoes saturation after increasing to a certain work done. At last it can be said so far that this work focuses on human movement in Salat to prove it as a fresh exercise (no muscle fati(Iue) mathematically.In addition with that, analytical techniques are developed to determine its muscle indices and fatigue, as well as mathematical relations are established between work done and corresponding sEMG voltage to observe the work done during Salat.Item Model Based ECG Denoising Using Discrete Bionic WaveletTransform(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh., 2011-12) Awal, Md. Abdul; Ahmad, Prof. Dr. Mohiuddin.Electrocardiogram (ECG) is a measurement of bio-electric potential produced by rhythmical cardiac activities, contraction and relaxation of the cardiac muscle produced at sinoatrial (SA) node. This electric potential associated with the cardiac cycle can be detected at the surface of the body, amplified and recorded as a time record of each cardiac cycle. Different cardiac function such as heart rate, abnormality of rhythm can easily be identified by ECG and it is a low cost tools in the medical diagnostic system. Therefore, ECG signal modeling and processing is one of the most significant topic in biomedical signal analysis. Most of the ECG models are complex and their computational time is high. In this research, a Gaussian wave-based model is proposed which can simulate ECG wave as well as its P, Q, R, S and T components individually. In addition, dynamically shifting baseline of the model reduces the preprocessing of ECG signal. The coefficient of the model is calculated by nonlinear least square technique using Gauss-Newton algorithm.The model fits well with real ECG by Normalized Root Mean Square error (NRMSE) of 0.0034 at the normal condition. Further analyses have been performed to evaluate the models ability of representing the different cardiac Dysrhythmias like atrial fibrillation,brachycardia and tachycardia successfully. For better model fitting denoised ECG plays a significant role. Bionic wavelet Transform (BWT) is based on auditory model but it is not efficient for ECG signal processing since ECG is generated from the heart. So for denoising ECG, a new adaptive wavelet transform is developed based on heart- arterial interaction model. Adaptability is adjusted instantaneous amplitude of the signal and its first-order difference. The automatically adjusted resolution is achieved by introducing the active control mechanism of the cardiac system into the wavelet transform. It is very hard to know what entropy function used in the bio-system. This is the problems of other transforms. But, the discrete BWT uses active control mechanism in the cardiac system to adjust the wavelet function rather than entropy function as criterion. Moreover, due to various oscillating behavior of different types of ECG signal constant Quality factor (Q) of wavelet is not as effective as variable Q. is changed with the instantaneous value of a signal and it will make BWT more adaptive compared to Tunable The variable Q Q wavelet transform (TQWT). As in variable Q-wavelet transform like DBWT which is he discrete version of BWT, is changed with the instantaneous value of a signal and its first order difference instead of Q-factor is tuned to a fixed value in TQWT. In addition, our proposed modified S-median thresholding technique has an adjustable factor and introduced in the system for better performance. In order to compare DBWT with other wavelet transform, experiments on traditional WT, multi- Q adaptive BWT, TQWT were conducted on both constructed signals and real ECG signals. The results show that novel DBWT performs better than these three wavelet transforms, and is appropriate for cardiac signal processing, especially over noisy environment.Item Motor Imagery Movement and Neurological Disorder Classification using Salient Features of EEG Signal(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh., 2017-07) Rasshid, Md. Mamun Or; Ahmad, Prof. Dr. Mohiuddin.Electroencephalography (EEG) measurement plays a significant role in the clinical and scientific research of brain studies. EEG signals are very important, particularly for classification and treatment of neurological diseases and brain computer interface (BCI) applications. The aim of this dissertation is to develop methods for the analysis and classification of different categories of motor imagery (MI) movements, epileptic EEG signals, and human alertness states. A novel method is also developed for continuous alertness monitoring. EEG signals of MI movements are classified for right hand and left hand (two class) and right hand, left hand, and feet (three class) movements. Nowadays, MI is a highly prescribed method for the disabled patients to give them hope to control machine or computer by interfacing with brain or mind. This dissertation proposes a classification method between imagery left and right hands movement using Daubechies wavelet of discrete wavelet transform (DWT) and Levenberg-Marquardt back propagation training algorithm of artificial neural network (ANN). DWT decomposes the raw EEG data to extract significant features that provide feature vectors precisely. ANN classifies the two class and three class trials data. Classification accuracy varies with respect to the subject. This method can be used to design a well-organized BCI system with better accuracy. Results from classifier can be used to design brain machine interface (BMI) for better performance that requires high precision and accuracy scheme. Neurological disorder i.e. epilepsy detection is enough time consuming and requires thorough observation to determine epilepsy type and locate the responsible area of the cerebral cortex. The dissertation proposes an effortless epilepsy classification method for epilepsy detection and investigates the classification accuracy of multiclass EEG signal during epilepsy. For accomplishing the proposed research work we use DWT to obtain responsible features to accumulate feature vectors. Afterward feature vectors are given in the input layer of the ANN classifiers to differentiate normal, interictal, and ictal EEG periods. Accuracy rate is calculated based on the confusion matrix. Proposed method can be utilized to monitor and detect epilepsy type incorporating with an alarm system. It is tiresome for human to concentrate constantly, though several works require continuous alertness like efficient driving, learning, etc. A practical method is applied to investigate the concentration state of human brain by EEG acquisition. This research work proposes continuous alertness state classification method based on two different types of mental tasks with respect to the resting state (resting with eyes open and eyes close). To conduct this research work, some participants were involved and they performed several tasks such as alphabet counting, virtual motor driving, resting with eyes open and eyes close. During the performances of the tasks, 9 channel EEG data has been acquired from their scalps. The data acquisition is performed by B-Alert (BIOPAC) system. The acquired data are filtered by IIR filter and responsible channels are selected by the statistical method. The features of the signals were extracted by using principal component analysis (PCA) and DWT algorithms. The alert states of our brain are classified by ANN. In addition, the specific relative power (RP) of the responsible frequency band of EEG signals is calculated for alertness monitoring. Within the RP range of resting and active state, a threshold value is proposed for monitoring the alertness state of the participants. This work will be helpful to classify the epileptic states with more accuracy as well as this works is also a well guide to classify the motor imagery movements. In addition, the proposed method based on continuous alertness monitoring will be remarkable approach to design machines for monitoring driving or learning.