Biomedical Signal Processing Thesis

This article presents you important current research ideas Biomedical Signal Processing Thesis Topics. Biomedical signal processing is the study of biomedical signals to extract accurate clinical information. Biomedical signals are collected from various medical utensils, which have the capabilities to sense/capture the human interior parts through several medical imaging techniques. In specific, it records every biological activity such as gene, organ, tissue, muscle, bone, neural functions, bone, heartbeats, protein and DNA structure, etc. 

Some of the medical imaging types are X-Ray, Ultrasound, PET, CT, MRI, and more. And few of the bio-signals are electromyogram (EMG), Electrocardiogram (ECG), electroencephalogram (EEG), Electroretinogram (ERG) and etc.

Once the signals are acquired, then these signals are trained through intelligent algorithms and techniques (machine learning and deep learning) to enhance the understandability of the signal. As a result, the physicians can know the actual clinical disorders with their severity level and take effective clinical measures. 

Though the biosignals are collected from dissimilar sources and techniques, they have some similar characteristics regardless of time and space scales. In addition, it also undergoes a few common artifacts and instabilities.

What is Biomedical Signal Analysis?

As mentioned earlier, biomedical signals are intended to capture the human physiological parts at different levels as cell, molecule, and organ. These signals are digitally processed to acquire the pathological information of the patient’s diseases, which assist physicians in further treatment. Basically, it is used in all medicinal and biomedical research for improving the biological and diagnostic systems in the healthcare sector.   

Characteristics of Biomedical signal processing

In the time of processing the raw biomedical signals, one should pay special attention to the following characteristics. Since these are the basic requirements for processing biosignals, and they are: 

  • Sample size
  • Operation mode as on-line or off-line
  • Sampling rate / frequency
  • Number of channels or variables are used in sample

In addition, it is necessary to have a clear purpose/motive of the signal interpretation or study, such as to set real-time health alarm, monitoring, diagnosis and etc. Further, we have given you the other significant data required for processing biomedical signal using matlab as follows,

  • Input and output functions
  • Computing devices
  • Preprocessing techniques
  • Databases / datasets
  • Deep Learning framework and architectures
  • Analyzing approaches
  • Number of MI tasks with its categories
  • Maximum performance parameters

Workflow of Biomedical Signal Processing

Now, we can see the working structure of biomedical signal processing through an example. In general, the brain-computer interfaces (BCI) system working in the following six processes. In short, we can say that it collects the brain signal and inspect them in all aspects. Next, it transfers output into a command for directing the machines. Let’s see detail in the below points, 

  • Get the input data (either private or public datasets)
  • Next, perform preprocess operation on the raw data for removing pointless data and noise
  • After, the extract the most important features from preprocessed data
  • Then, select the optimal features from the extracted features
  • Next, classify the selected features and transmit them to machine as commands
  • Finally, send feedback to the user based on the classification results
Recent Research Biomedical Signal Processing Thesis

Signal Processing Steps

  • Collect the raw EEG signals
  • Remove the noise over signal
  • Extract the most useful information in signal
  • Select the key features among extracted features
  • Classify the feature (by control commands)
  • Control the machine and send the feedback

Basically, when you are attempting to propose your handpicked Biomedical Signal Processing Thesis topics, you need to more careful in choosing an effecting research problem solution. Majorly, feature identification operation is found to be common for all biomedical signal processing. So, we have given you some significant and key algorithms used in feature extraction, selection, and classification phases.

Biomedical Signal Processing Techniques

Feature Identification

  • Extraction
    • Spatial Domain
      • Correlation based Common Spatial Pattern (C-CSP)
      • Sub band CSP (SBCSP)
      • Common Space Spatio-Spectral Patterns (CSSSP)
      • Common Spatio-Spectral Pattern (CSSP)
    • Frequency Domain
      • Local Characteristic-scale Decomposition (LCD)
      • Fast Fourier Transform (FFT)
      • Welch’s Method
    • Time Domain
      • Root Mean Square (RMS)
      • Autoregressive (AR)
      • Integrated EEG (IEEG)
      • Adaptive AR
    • Time Frequency Domain
      • Wavelet Transform (WT)
      • Discrete Wavelet Transform (DWT)
      • Short Time Fourier Transform (STFT)
  • Selection
    • Evolutionary Algorithms (EAs)
      • Artificial Bee Colony (ABC)
      • Genetic Algorithm (GA)
      • Differential Evolution (DE)
      • Ant Colony Optimisation (ACO)
      • Particle Swarm Optimisation (PSO)
    • Statistical Transformation
      • Independent Component Analysis (ICA)
      • Principal Component Analysis (PCA)
    • Filter Bank
      • Spare Filter Bank (SFB-CSP)
      • Filter Bank CSP (FB-CSP)
      • Discriminant Filter Bank CSP (DFB-CSP)
  • Classification
    • Nearest Neighbour
      • K-Nearest Neighbour Analysis (K-NN)
    • Artificial Neural Network (ANN)
      • Deep Neural Network (DNN)
      • Multi-Layer Perception (MLP)
      • Radial Basis Function (RBF)
    • Linear
      • Naïve Bayes
      • Support Vector Machines (SVM)
      • Logistic Regression (LR)
      • Linear Discriminant Analysis (LDA)
    • Non-linear
      • Hidden Markov Model (HMM)
      • Bayes Quadratic

Next, we can see the recent research encounters that currently exist in biomedical signal processing thesis. When you choose the research question, make sure that you have answers to the following questions. So, we assure you that our resource team helps you to find the best fitting answers for these questions in spite of research complications.

Research Challenges and Issues in Biomedical Signal Processing

  • Which frequency range should take into account in the analysis?
  • What input data structure has most beneficial effect on deep learning?
  • For classification process, what is most effective machine learning / deep neural network architecture?

As a matter of fact, we have a sophisticated environment with below listed technical features to support you in all aspects while implementing your Biomedical Signal Processing projects.

Biomedical Signal Processing Research Ideas
Our Technical Features
  • Comprises recent research ideas and case studies on modern healthcare systems
  • Acquire and model the various bio signals to undergo thorough study on them
  • For obtaining useful information on diagnosis, use CAD-based analysis that help healthcare
  • Contains different medical imaging and bio signal modalities (ECG, EEG, PCG, EMG, MEG and more)

Then for your benefit, our research team has shared a few mind-blowing Biomedical Signal Processing Thesis research ideas. These ideas are very important for active scholars because all these topics are gathered from current research areas of biomedical signal processing.

Recent Research Ideas in Biomedical Signals

  • Advancement in Wireless Communication Technology
  • Smart sensors: Real-time Low Power Signal Computation in IoT Applications
  • Cooperative Bio-inspired Artificial Intelligence Algorithms for Wearable and Implantable Technologies
  • Grid and Pervasive Distributed Computing for Sensor Networks
  • Channel Coding, Modeling and Equalization in Biomedical Signals
  • Trends in Wireless Body Sensor Networks Applications
  • Intelligent Techniques for processing biosignals from different Sensors and Systems
  • Trends in Healthcare based Smart Sensor Applications in Clinic Environment
  • Biomedical Nanotechnology for Smart IoT and Healthcare Applications
  • QoS aware Energy Harvesting in Biomedical Signal Processing
  • Robust Cooperative Transmission in Decentralized Sensor Networks
  • New developments in Signal Processing for Smart Sensor Technologies
  • Advance Artificial Intelligence Techniques on Smart Embedded and Portable Sensors for Healthcare Applications
  • Issues and Impact of Sensor Technology in Biomedical and Industrial Applications
  • Recent Biosignal Processing Algorithms in Wearable Sensors

For illustration purposes, here we have selected “Mental Fatigue Assessment” as the sample one. Also, it is a newly emerging research area that uses multi-sensors data fusion mechanisms.

Mental Fatigue Assessment

Step 1: Data Collection and Acquisition

Here, the biomedical data are collected from multiple sensors and grouped together as single data through a microcontroller. Then, apply preprocessing techniques on comprehensively collected data for reducing data size by removing unwanted data/signals which affect the further processes.

  • Multi-Data Collection and Grouping (Eye tracker, EEG, Temperature,  ECG and EMG)
  • Data Preprocessing

Step 2 – Assess Mental Fatigue

Here, implement a multi-sensors data fusion algorithm on preprocessed data. This algorithm will fuse the information of different channels and generate output too registered MF indicators. This indicator helps to assess the complexity level in the processing.

  • Sensor Fusion
  • Mental Fatigue Scale Calculation (using CNN)
  • Classification of Mental Fatigue Stages – Low Fatigue and High Fatigue

What is Mental Fatigue Scale?

It is a variable that is used to assess mental fatigue from biological signals. By the by, mental fatigue is based on the stress symptoms like Sensory, Cognitive, Sleep Duration Variance, and Affective

How can the main stress of fatigue be evaluated?

In the view of mental tiredness effect and limit with respect to the mean compressive stress, the MFS is computed by using the Tensile Stress Rate (Mean and Standard Deviation) formula. So, it does not require an add-on S–N curve for processing the mental fatigue level.    

On the whole, we hope that you are clear in recent research developments in the biomedical signal processing field. Further, if you want more, then contact our team for knowing up-to-date information to formulate best biomedical signal processing thesis. Reach us to know more information


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