Department of Biomedical Engineering (BME)

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    Detection of Angina Pectoris Using ECG Signals
    (Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh, 2018-06) Islam, Md. Merajul; Hossain, Prof. Dr. A. B. M. Aowlad
    Angina pectoris due to ischemia is very crucial to detect because, if it can be detected earlier, doctor can provide the patient proper medication to cure. Sometimes from a long ECG data it is tiresome to calculate and differentiate the normal and angina pectoris affected ECG peak to decide about the condition of the patient. In addition, the remote areas of lower and midlower income countries often face lack of experienced doctors or highly cost devices like ecocardiogram, MRI to detect angina. In these regions, automatic identification of ECG features can be a fruitful solution. Therefore, only way is computerized and efficient ECG analyzing algorithm development that can be able to detect angina pectoris. In this work, an efficient algorithm is developed to detect angina pectoris from ECG signal. This algorithm consists of several steps to take decision on ECG signal. First of all this algorithm removes baseline wandering from ECG signal by baseline wandering path finding algorithm. After that it removes other noises from ECG signal by Gaussian weighted moving average window method. In this consequence, QRS complex was detected by very well-known method First and Second Derivative (FS2) algorithm and gradually other important points like S, J, K, and T were detected by possible range maxima-minima criterion. Besides, the isoelectric line of ECG signal is estimated and eventually the statistical features of J-K points of normal and abnormal ECG peaks are compared with that isoelectric line by the algorithm, Finally, this algorithm takes the decision whether the patient is suffering from angina or not. This algorithm is applied on MIT arrhythmia Database to detect angina pectoris. From the result provided by this algorithm, we have found 94% (average) accuracy which is noticeable. In addition with that the sensitivity and specificity of our proposed algorithm have also been calculated which are found 91% and 89%, respectively. Since the previous work is based on the single feature, it may prove inappropriate for all the time. Therefore with the help of multiple features machine learning based approach k-nearest neighbor (kNN) method has been deployed in this research work to make it more accurate and acceptable. Although kNN based prediction method also provide almost similar results found by the previous methodologies. Therefore our proposed approach for angina pectoris detection has been testified by both statistical and kNN based method. It is expected that the proposed algorithm will be helpful for computerized angina pectoris detection from ECG signals.
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    In Silico Characterization and Homology Modeling of Histamine Receptors
    (Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh, 2019-02) Zobayer, Md. Nayem; Hossain, Prof. Dr. A. B. M. Aowlad
    Histamine plays vital role in molecular mechanism of allergic reactions. Therefore, characterization and homology modeling of Histamine receptor is of great importance to design effective vaccines. In this thesis, different methods are applied to analyse biomolecular features of histamine receptors and design best models of these receptors. In addition to this, the study tried to identify potential B cell and T cell epitope based vaccine of an allergen and consequently, emphasized on to develop a B cell prediction tool. Identified four histamine receptors, such as Histamine H1, Histamine H2, Histamine H3 and Histamine H4 have been analysed through ProtParam to extract physiochemical properties and ClastalW algorithm has been applied to identify conserved regions. Motif and Transmembrane regions have been identified through MEME suit and TMHMM servers, respectively. For homology modeling, I-tasser has been used and generated models have been validated through RAMPAGE, ERRAT and PROCHECK. Targeted api m3 allergen then rendered through self-optimized prediction method with alignment for physiochemical feature extraction. NetCTL 1.2 has been applied to identify preliminary T cell epitope candidates and then scrutinized by Stabilized Matrix Base Method, relative to IC50 values. Predicted T cell epitopes have been further analysed for conservancy and population coverage via IEDB tools. B cell epitopes of api m3 allergen have been predicted through, BCPREDS, ABCpred, Bepipred and Bcepred. In addition, classifier based single interface B cell epitope prediction and/or validation tool has been developed through establishing efficient MATLAB algorithms to classify beta turn regions, hydrophilic regions, surface accessible regions and antigenic regions. Lastly, with superimposing graphical representation of these four criteria in a single interface graph plotted to identify B cell epitopes via this tool. Extracted results denotes that, Histamine receptors possess molecular weight around 55.7 KDa, theoretical pI 9.33-9.62, instability index 34.93-47.00, aliphatic index (AI) was above 90 and the receptors were hydrophobic except histamine H1 receptor. Moderately conserved region was found in 75-94 amino acid position. A profound motif has been identified from 84-149 amino acid position for four histamine receptors with significantly lower E-value. It has been identified that, these receptors are seven pass transmembrane protein and a gap between transmembrane helix number five and six was found in each histamine receptor except Histamine H2 receptor, which can be potential drug target candidate. Generated 3D models have been passed through every spheres of validation. Api m3 allergen has been found relative thermostable nature and only 10.46% of the overall secondary structure consisted of beta turn region. Five MHC class I T-cell epitopes were identified and scrutinized and YTEESVSAL found out as the best epitope. For MHC class II T-cell epitopes YPKDPYLYYDFYPLE and GGPLLRIFTKHMLDV have been found as most prominent T-cell epitopes of api m3 allergen. This study also revealed that, GDRIPDEKN and PHVPEYSSS, as the most effective B-cell epitopes of api m3. The proposed tool efficiently identified B cell epitopes and provided result in a single interface. The tool can aid in B cell research and vaccine development. Finally, the suggested potential drug targets can be applied in designing more sustainable antihistamines and relevant drugs in treating allergic diseases. Predicted T-cell and B-cell epitopes of api m3 allergen could help the researchers to test these vaccines further for immunoreactivity applying in vivo analysis. As still there is no report of T-cell and B-cell epitopes of Apis mellifera, this study can be the pioneer in finding effective vaccine against allergens of honeybee. This research also predicted potential B cell epitope regions from an antigenic protein. The most exciting feature of this part of the study is, it presents results of potential B cell epitopes on a single interface, so that, researchers don‘t need to search for every feature (e.g., hydrophilicity, antigenicity, beta turn, surface accessibility etc.) separately. Finally, the study can certainly aid in B cell epitope-based vaccine design research.