Data analysis is crucial to the future of medical practice. Specialize in this exciting field and be part of the change towards excellence in clinical decision making, promoting more personalized medicine"

In the last few decades, data storage, analysis and management has become a fundamental task for many disciplines. The medical field is no exception, and the analysis of so-called Biomedical Signals has undoubtedly marked a milestone that made possible the beginning of a new era in medical diagnostic techniques, fostering the greater inclusion of technology in healthcare. Since then, more and more electronic equipment is capable of revolutionizing the techniques used in routine clinical practice, improving diagnosis, treatment and, ultimately, patient care.

As such, Biomedical Signals, as well as their acquisition, processing and analysis, make up one of the most important branches of Biomedical Engineering, where numerous branches of knowledge converge: Medicine, Biology, Physics, Electronics or Computer Science, in addition to many others.

Therefore, this Postgraduate diploma will address the physical and mathematical principles that govern Biomedical Signals. It will develop in the student specific knowledge on how the different signals that the body can emit are acquired, and what they are used for at a clinical level.  Thanks to this, the student will learn to interpret these signals and even process them, acquiring extensive skills in this field of Biomedical Engineering.

Following this same line, and once the data have been stored, this program will provide the latest developments in methodology and educational resources for the use of bioinformatics tools for scientific computing. All this, in order to obtain, analyze, organize and interpret biological information for medicine, encouraging the student to incorporate bioinformatics to their research tasks and, potentially, to their professional life.

And finally, this program will address a growing field: the storage, analysis and study of data. Data processing is essential for the development of Telemedicine Systems that can be integrated into the day to day running of hospitals, as well as for developing Artificial Intelligence tools to aid clinical decision making. Building databases that protect patient privacy and contain information that can be analyzed effectively is one of the cornerstones of personalized medicine. For all these reasons, this program will address the design of databases according to technical criteria and patient needs, as well as the tools for their construction. 

Thanks to this program, you will learn about the latest software and equipment that are revolutionizing medical practice thanks to their ability to analyze and store clinical data" 

The Postgraduate diploma in Health Data Management and Analysis in Biomedical Engineering contains the most complete and up-to-date educational program on the market. The most important features include:

• The development of case studies presented by experts in Biomedical Engineering
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where the self assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

The analysis and management of Biomedical Signals requires highly specialized professionals who are up to date with the latest developments in the profession. If you want to be one of them, then do not hesitate and start this Postgraduate diploma today"   

The program’s teaching staff includes professionals from sector who contribute their work experience to this training program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive training programmed to train in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts.    

Delve into biomedical signals and their applications, and position yourself as an engineer in high demand by numerous health services"

In just 6 months of intensive online study you will learn everything you need to process and compute medical data efficiently and effectively"

TECH teaching experience in the creation of highly efficient and specialized university programs has made it possible to organize the contents in a way that is highly efficient in terms of reinforcing learning. As a result, the student will have 3 theoretical modules with an eminently practical approach in which they will learn the most effective way to analyze, manage and store biomedical data. All this, in addition, through innovative learning tools such as videos of real cases, interactive summaries and action guides that will enhance and enrich the study process. 

This Postgraduate diploma is designed to be the most comprehensive and specialized option on the current educational scene, and has the most effective syllabus to help you get to the top, making it a simple task"

Module 1. Biomedical Signals

1.1. Biomedical Signals

1.1.1. Origin of Biomedical Signals
1.1.2. Biomedical Signals

1.1.2.1. Amplitude
1.1.2.2. Period
1.1.2.3. Frequency (F)
1.1.2.4. Wave Length
1.1.2.5. Phase

1.2. Classification and Examples of Biomedical Signals

1.2.1. Types of Biomedical Signals Electrocardiography, Electroencephalography and Magnetoencephalography

1.2.1.1. Electrocardiography (ECG)
1.2.1.2. Electroencephalography (EEG)
1.2.1.3. Magnetoencephalography (MEG)

1.3. Types of Biomedical Signals Electroneurography and Electromyography

1.3.1. Electroneurography (ENG)
1.3.2. Electromyography (EMG)
1.3.3. Event-Related Potentials (ERPs)
1.3.4. Other Types

1.4. Signals and Systems

1.4.1. Signals and Systems
1.4.2. Continuous and Discrete Signals: Analog vs. Digital
1.4.3. Systems in the Time Domain
1.4.4. Systems in the Frequency Domain Spectral Method

1.5. Fundamentals of Signals and Systems

1.5.1. Sampling: Nyquist
1.5.2. The Fourier Transform DFT
1.5.3. Stochastic Processes

1.5.3.1. Deterministic vs. Random Signals
1.5.3.2. Types of Stochastic Processes
1.5.3.3. Stationarity
1.5.3.4. Ergodicity
1.5.3.5. Relationships Between Signals

1.5.4. Power Spectral Density

1.6. Processing of Biomedical Signals

1.6.1. Processing of Signals
1.6.2. Objectives and Processing Steps
1.6.3. Key Elements of a Digital Processing System
1.6.4. Applications. Trends

1.7. Filtering: Artifact Removal

1.7.1. Motivation. Types of Filtering
1.7.2. Time Domain Filtering
1.7.3. Frequency Domain Filtering
1.7.4. Applications and Examples

1.8. Time-Frequency Analysis

1.8.1. Motivation
1.8.2. Time-Frequency Plane
1.8.3. Short-Time Fourier Transform (STFT)
1.8.4. Wavelet Transform
1.8.5. Applications and Examples

1.9. Event Detection

1.9.1. Case Study I: ECG
1.9.2. Case Study II: EEG
1.9.3. Evaluation of Detection

1.10. Software for Biomedical Signal Processing

1.10.1. Applications, Environments and Programming Languages
1.10.2. Libraries and Tools
1.10.3. Practical Applications: Basic Biomedical Signal Processing System

Module 2. Medical Bioinformatics

2.1. Medical Bioinformatics

2.1.1. Computing in Medical Biology
2.1.2. Medical Bioinformatics

2.1.2.1. Bioinformatic Applications
2.1.2.2. Computer Systems, Networks and Medical Databases
2.1.2.3. Applications of Medical Bioinformatics in Human Health

2.2. Computer Equipment and Software Required in Bioinformatics

2.2.1. Scientific Computing in Biological Sciences
2.2.3. The Computer
2.2.4. Hardware, Software and Operating Systems
2.2.5. Workstations and Personal Computers
2.2.6. High-Performance Computing Platforms and Virtual Environments
2.2.7. Linux Operating System

2.2.7.1. Linux Installation
2.2.7.2. Using the Linux Command Line Interface

2.3. Data Analysis Using R Programming Language

2.3.1. Language R Statistical Programming
2.3.2. Installation and Uses of R
2.3.3. Data Analysis Methods With R
2.3.4. R Applications in Medical Bioinformatics

2.4. Data Analysis Using R Programming Language

2.4.1. Multipurpose Programming Language Python
2.4.2. Installation and Uses of Python
2.4.3. Data Analysis Methods with Python
2.4.4. Python Applications in Medical Bioinformatics

2.5. Methods of Human Genetic Sequence Analysis

2.5.1. Human Genetics
2.5.2. Techniques and Methods for Sequencing Analysis of Genomic Data
2.5.3. Sequence Alignments
2.5.4. Tools for Detection, Comparison and Modeling of Genomes

2.6. Data Mining in Bioinformatics

2.6.1. Phases of Knowledge Discovery in Databases, KDD
2.6.2. Processing Techniques
2.6.3. Knowledge Discovery in Biomedical Databases
2.6.4. Human Genomics Data Analysis

2.7. Artificial Intelligence and Big Data Techniques in Medical Bioinformatics

2.7.1. Machine Learning for Medical Bioinformatics

2.7.1.1. Supervised Learning Regression and Classification
2.7.1.2. Unsupervised Learning Clustering and Association Rules

2.7.2. Big Data
2.7.3. Computing Platforms and Development Environments

2.8. Applications of Bioinformatics for Prevention, Diagnosis and Clinical Therapies

2.8.1. Disease-Causing Gene Identification Procedures
2.8.2. Procedure to Analyze and Interpret the Genome for Medical Therapies
2.8.3. Procedures to Assess Genetic Predispositions of Patients for Prevention and Early Diagnosis

2.9. Medical Bioinformatics Workflow and Methodology

2.9.1. Creation of Workflows to Analyze Data
2.9.2. Application Programming Interfaces, APIs

2.9.2.1. R and Python Libraries for Bioinformatics Analysis
2.9.2.2. Bioconductor: Installation and Uses

2.9.3. Uses of Bioinformatics Workflows in Cloud Services

2.10. Factors Associated with Sustainable Bioinformatics Applications and Future Trends

2.10.1. Legal and Regulatory Framework
2.10.2. Best Practices in the Development of Medical Bioinformatics Projects
2.10.3. Future Trends in Bioinformatics Applications

Module 3. Biomedical and Healthcare Databases

3.1. Hospital Databases

3.1.1. Data Bases
3.1.2. The Importance of Data
3.1.3. Data in a Clinical Context

3.2. Conceptual Modeling

3.2.1. Data Structure
3.2.2. Systematic Data Model
3.2.3. Data Standardization

3.3. Relational Data Model

3.3.1. Advantages and Disadvantages
3.3.2. Formal Languages

3.4. Designing from Relational Databases

3.4.1. Functional Dependence
3.4.2. Relational Forms
3.4.3. Standardization

3.5. SQL Language

3.5.1. Relational Model
3.5.2. Object-Relationship Model
3.5.3. XML-Object-Relationship Model

3.6. NoSQL

3.6.1. JSON
3.6.2. NoSQL
3.6.3. Differential Amplifiers
3.6.4. Integrators and Differentiators

3.7. MongoDB

3.7.1. ODMS Architecture
3.7.2. NodeJS
3.7.3. Mongoose
3.7.4. Aggregation

3.8. Data Analysis

3.8.1. Data Analysis
3.8.2. Qualitative Analysis
3.8.3. Quantitative Analysis

3.9. Legal Foundations and Regulatory Standards

3.9.1. General Data Protection Regulation
3.9.2. Cybersecurity Considerations
3.9.3. Regulations Applied to Health Data

3.10. Integration of Databases in Medical Records

3.10.1. Medical History
3.10.2. HIS Systems
3.10.3. HIS Data

You can only find the best syllabus at the best university: enroll at TECH today and start to see your dreams and goals materialize"