Biomedical signal processing is aimed to acquire insight information about the signals to take effective decisions. It let them to measures, analyzes and monitors the patient’s health condition for diagnosing and treating their clinical disorders.
This page is about the innovative advancements in the Biomedical signal processing research field with creative research ideas!!!
In recent years, scholars are working with full determination to find out new technologies for handling these signals through various scientific algorithms and formulae. In conventional biomedical assisted measurement tool, to know the actual patient report with insight info for medical evaluation.
Through this kind of software, one can analyze physical body conditions by noninvasive measures to monitor patient’s fitness. For your reference, here we have listed the different sources for collecting bio-signals along with various frequencies of physiological signals.
What are the Sources of Biomedical Signals?
- Electrooculogram (EOG)
- Electrogastrogram (EGG)
- Electroretinogram (ERG)
- Electrical Biosignals
- Galvanic Skin Response (GSR)
- Electrocardiogram (ECG)
- Electroencephalogram (EEG)
- Electromyogram (EMG)
Frequency of Physiological Signals
- EMG (10 Hz – 5 kHz)
- ECG (0.5 – 100 Hz)
- EEG (0.5 – 75 Hz)
- Arterial Pressure Wave (DC – 40 Hz)
- Body Temperature (DC – 1 Hz)
- Nerve Action Potentials (10 Hz – 10 kHz)
- Respiration (DC – 10 Hz)
- Smooth Muscle Potentials (0.05 – 10 Hz)
Our professional researchers are intended to create remarkable contributions on research world which make future scholars to follow our created footsteps. By keeping this on mind, our experts have framed numerous creative Biomedical Signal Processing Ideas.
Accordingly, it springs out the new scientific developments over bio-signal and clinical applications. And, some of them are image guided therapy, patient monitoring, disorder prediction, disease diagnostic, disease prevention and risk possibility evaluation. Further, the following biomedical artifact signals bring the new dimension of the research.
Biomedical Artifact Signals
- Fractal Signals
- Here, the Fractal patterns and signals are similar at various magnification phases which also referred as self-similarity and scale-invariant.
- In specific, it is proof for showing the heart rate fluctuation and bronchial tree of lungs based on the self-replication
- Chaotic Signals
- In this, the chaotic signal determines the chaos in the nature where the estimation process is hard and requires long time
- Non-linear model features and very sensitivity initial state are the reason behind the prediction hardness
- On the one hand, chaos theory denotes the dynamic variables / temporal system development and on the other hand, fractal theory denotes the non-linear system’s spatial features.
- Stochastic or Random Signals
- In general, random signals demands more signal morphology due to its deficiency. For instance: EEGs, ECGs R-R intervals, field potentials and EMGs
- Based on physiology, these signals may fall under either stationary or non-stationary type
- In stationary, the signal statistics will not change. In non-stationary, the signal statistics will continuously vary because of the physiological perturbations like drug infusion / recovery / pathology
Some of the current researches are mainly focusing on overcoming the above specified artifacts in the biomedical signals. Generally, biosensor is small hardware to communicate with physiological / biological systems. After interaction, it aggregates the signals from those systems for treatment purpose. Next, apply the biomedical signal processing methods for machine or human understanding. And, some of the foremost biomedical signals processing data are given below,
- MIT-BIH Arrhythmia
- Kinematic Dataset (Emotions)
- MIMIC-III Clinical
- MIT-BIH Atrial Fibrillation
- eICU Dataset
Our resource team is currently on-going deep-study on all recent signal processing research areas such as tracking object in motion, DL based real time communication, EEG assisted human-robot interaction and learning based multilingual speech recognition. Then for your information, here we have given the way we support you in bio-signal research.
Steps Involved in Digital Signal Processing
- Emphasis on Significant Features of the Signal
- Provide Information on Number of signals is being used
- Describe the Essential Procedures for Preprocessing and Features Extraction
- Suggest Suitable Dataset for your handpicked Biomedical Signal Processing Ideas with Detailed Selection Reason
Next from of the development, we have shared the tools and programming languages that apt for biomedical signal processing projects. Based on the nature of our handpicked input data, the tool will be selected. We will suggest you the suitable software that produce desired outcome.
What is the software for biomedical signal processing?
- MATLAB is one of the best flexible software to cope with all respects of the bio-information such as EMG, DICOM images, ECG and simulations of 3D based radiation absorption
- Also, it has several built-in transforms such as radiation back projection
- Enable the easy customized GUIs creation to handle real time information by means of SIMULINK. However, it is little expensive in the case of whole package
- In contrast, R is cost-free statistical computing software. Also it is user-friendly open-source tool to use
- Similarly, COMSOL is a Multi-physics tool which is utilized to simulate the FEA
Programming Languages for Biomedical Signal Processing
- C / C++ is the globally recognized resourceful language for numerous microcontrollers and microprocessors
- It is more suitable for real-time models which consider storage and speed key factors
- The reasons for choosing this language are implicit declaration of variable and pointer capability to directly contact memory
- Though some developers use assembly language, they use C for writing custom libraries
- Similarly, Java also an amazing IDEs to run on any JRE installed environs. This, JRE makes the java as a portable language. However, it has so many advantages, still C / C++ language is best for real-world applications
- Next is Python language, it is presented with massive in-built libraries and supported compliers to make the whole thing easy for programming microprocessors. For instances: ARM is developed by python
Further, our team has proposed various Biomedical signal processing ideas in all different respects. For instance: if we take EMG, more researches are developing over muscle diseases and prostheses areas. Likewise, if we take EEG, many researches are evolving on brain syndromes like schizophrenia, epilepsy and seizure, etc.
Recent Research Ideas in Biomedical Signal Processing
- New Bio-Signal based Man-to-Machine Interfaces
- Neuro-Signal Processing and Computation in Neuroscience
- Advance Development in Biomedical Signals Control
- Advance Diagnostic Methodologies for Neurodegenerative Disease
- Bio-Medical Signals Modeling, Processing and Analysis
- Modern Bio-Medical Pattern Recognition and Prediction Approaches
- Bio-Signal Processing in Medical and Applied Science Applications
On the whole, if you are seeking for the best research topics, then communicate with us. We will give you and proper research guidance support in all the phases.