CCMAS Course Search

Introduction to biomedical informatics; components of biomedical informatics. Bioinformatics: What is bioinformatics; components of bioinformatics; biological data; information complexity; bioinformatics applications? Medical informatics: Management Information Systems in biology and medicine-data acquisition, data storage and retrieval, data processing; components of Health Information Systems; types of Health Information Systems. Computer networking in the hospitals: the concept of computer networking, telemetry; e-Health. Software development in biology and medicine. Computer applications in medical diagnosis and therapy. Computer-aided simulation and experimentation.

Introduction to biomedical instrumentation: basic biomedical instrumentation system. General considerations in the design of biomedical instrumentation systems. Biomedical measurements: errors in measurement. Biological signals: bioelectric signals. Biomedical sensors and transducers-types and forms. Physiological measurements: audiology; cardiac physiology; gastrointestinal tract physiology; neurophysiology; ophthalmic and vision science; respiratory physiology; urodynamic physiology; vascular technology; blood pressure measurement; blood flow measurements; measurement of the respiratory system; ECG measurement systems. Clinical laboratory instrumentation. Electrical safety in instrumentation. Introduction to Biomedical signal processing.

Signals: What is a signal? History; categories; application fields. Biomedical Signals: nature, sources, types and examples. Signal Processing: Definition, Stochastic and deterministic signals, Discrete signals, Linear time invariant systems, Duality of time and frequency domain, Hypotheses testing. Biomedical signal Processing: Brain signals-local field potentials (LFP), electrocorticogram, (ECG), electroencephalogram (EEG), and magnetoencephalogram (MEG); Heart signals - Electrocardiogram, Heart rate variability, Fetal ECG; Electromyogram; Gastro- intestinal signals; Acoustic signals. Modeling Biomedical Systems.

Advanced mathematical concepts: linear relations and functions; systems of equations and inequalities; polar coordinates and complex numbers; exponential and logarithmic functions; iteration; statistics and data Analysis; limits, derivatives and integrals: applications of differentiation and integration. Mathematical methods and Models: numerical methods; finite differences; solutions of differential equations; role and application of models in biology and medicine. Computer simulations: Development of computer simulation techniques to study physiological system.

Sample; sampling; sample size determinator; Data collection; analysis; test of hypotheses and other biostatistical parameters that are necessary for interpretation of results; Data Archiving

Introduction to biometrics and digital image processing. Matlab in biometric image/signal processing. Biometric algorithms and systems with emphasis on face, fingerprint, eyes (iris), speech (voice). Automated biometric identification multimodal biometrics. Biometric data: raw data, template data, and data methods. Biometric matching basics: biometric authentication, enrolment, correct user, and incorrect user. Match threshold and matching performance. Setting a threshold. Biometric authentication: matching data, ground truth, calculating errors rates and graphs. Biometric data: Storage of biometric data elements, transactions, errors and quality upgrades. Data security and integrity. Privacy issues and other aspects of biometrics. Applications of biometrics and future trends. Challenging issues: security strength and recognition rates. Alternatives of passwords and smart cards. Lab work: Practical exercise on biometric capture, image processing, matching threshold and performance. Learn the practical aspect of automated biometric identification of multimodal, authentication and calculation of error rates. Work on biometric algorithms, privacy and security of stored biometric data.

Aims, characteristics and application of biostatistics in clinical and preventive medicine. Statistical data in bio-medical science-samples, population, variables, frequency distribution, vital and descriptive statistics, measurement of central tendencies-mean, median, mode, dispersion and presentation of data probability distribution, Hypothetical tests of statistical significance. Analysis of variance. Regression and correlation. Experimental designs and clinical trials. 300 Level

Advanced Regression; Bio-assays; Probit and Logit models; Growth Curves; Logistic Regression; Potency/efficacy determination; Theory of clinical trials; Ethical Issues in Medical Data Collection

Biostatistics: Definition; scope and applications; Presentation of data; Overview of measures of central tendency; Chi square test; Scientific writing I; (a) Biostatistics; Population and sample size; Sampling distribution; Research design; Study of some classical papers for experimental design and presentation of data; Normal; Binomial and Poisson distributions; Tests of significance; Students t test; Analysis of variance (ANOVA); One way and two way ANOVA; Regression Analysis; Simple and multiple regression; Overview of non parametric tests; Statistical packages; Graphpad Instat; Minitab; SAS; Epi Info; and SPSS

Importance and central role of statistics in research. Review – types of variables, scales of measurement, concept of sampling, data collection. Reduction, summarisation and presentation of data. Probability. The Normal distribution. Sampling methods. Tests of hypothesis (significant difference, correlation, regression and many others.). Parametric and non-parametric tests. Factors for considerations in choosing a particular statistics such as Student-t test; Mann-Witney U, ANOA, MANOVA, Regression Analysis