Researches in biomedical signal processing projects are mainly increasing due to the rise in demand for its enhancement in the medical field throughout the world. Biomedical signal processing is the method used for the extraction of valuable data from biomedical signals. The algorithms used for processing digital signals are designed to the following.

  • Remove noises (and other disturbances) in the signal
  • Allows for feature extraction
  • Monitoring defects
  • Explanation of certain functions (physiological)

Researchers are coordinating among themselves to bring many useful inventions in the field of medical image processing. In such a circumstance, it is important for you as a researcher to have an expert to support your project.

This is mainly because biomedical signal processing is not a small area that can be understood easily. It requires the proper explanation by specialists or experts in the fieldwork related to it as the system for processing signals depends primarily on the signals acquired, which are of many types that include the following.

  • Bioelectric signals
  • Bio-magnetic signals
  • Bio-acoustic signals
  • Biochemical signals
  • Bio-optical signals
  • Bio impedance signals

So it is important for us to have a detailed look into biomedical signal processing and its research. Here is an overview and the complete explanation that you expect on biomedical signal processing. First, let us start with the objectives of signal processing in the medical field.


Biomedical signals are all those signals obtained from the human body (or other living beings), which are then converted into their corresponding digital forms for manipulating them. Actually, these signals are wide-ranging, which include the different parts of the electromagnetic spectrum. What for is these biomedical signals being processed? Let us see the answer below.

  • Reduction of using measurements (manual)
  • Extracting parameters for characterization
  • Increasing accuracy and reproducibility
  • Interpretation and understanding of data from the signal
  • Separation of physiological data (by removing interference artefacts)
  • Noise removal
  • Mitigation of technical setbacks (in recording)

These are the various purposes for which biomedical signals have to be processed. Our engineers have been working in the field of biomedical signal processing projects for more than 20 years. We can guide you in the best way out of your project challenges.

We provide greater insight into the techniques that are used for making advanced systems and applications relating to biomedical signal processing. We help in writing surveys, paper publications, revising any major or minor comments in the high impact factor journals, etc. we provide you all the research support at any time you needed. Now let us see about the systems for biomedical signals processing.

Research Biomedical Signal Processing Project Support


Biomedical signals are used to detect the functioning and abnormalities in the bodily tissues and organs. The signals need not essentially be electromagnetic. Hormones are also a form of signal that is used for biomedical signal processing purposes.

  • Bio signal processing techniques include ECG, EOG, EEG and EMG
  • Biological signals are weak and are of low voltage.
  • There are various factors of artefacts and noise associated with these small voltage bio signals
  • Processing these signals in frequency domain is easier and comfortable when compared with processing them in the domain of time
  • Removal of artefacts is one of the key objectives of signal processing.
  • Manipulating the signal means is to make adjustments and up gradation  to some of the signal characteristics

Estimation of spectral features, signal filtration, averaging of multiple signals, processing two or more signals by multiplying them, etc., are some of the functions that are performed by a biomedical signal processing system. What is the purpose of performing these processes on a signal?

  • Diagnosis of diseases (in heart, brain etc) is the utmost important function for which biomedical signal processing is used.
  • Analyses of signals (EEG and ECG like signals) require enhancing them and removing the noise and artefacts from them.

These are the common but major applications or objectives of biomedical signal processing systems. Specifically, signal processing applications in the field of medicine and health can be listed as follows.

  • Signal for measuring oxygen saturation level
    • Sensor – pulse oximetry
    • Data – blood oxygen level
  • Rate of respiration
    • Sensor – piezoelectric and piezo-resistive
    • Data – rate of breathing
  • Electrocardiogram
    • Sensor – electrodes in skin and chest
    • Data – heart’s electrical activity is recorded
  • Blood pressure
    • Sensor – monitor based arm cut-off
    • Data – force on blood vessels
  • Body temperature
    • Sensor – thermometer
    • Data – measuring body heat

There are many facilities created for research communities across the world to increase the infrastructural requirements for doing biomedical signal processing projects. This is mainly done so as to enhance the research and, in turn, bring many direct positive impacts broadly. Against this backdrop, let us understand the novel researches going on in the field of biomedical signal processing projects.



Research People from top universities of the world are doing research in the following aspects of biomedical signal processing.

  • Analysis of human actions (using signals)
  • Authentication on the basis of bio signals
  • Processing of multi-modal audio (and speech)
  • Analysis of activity of brain
  • Analysis of cardiovascular signals
  • Machine learning applications on Bio-signals

Our experts have been co-ordinately working with those researchers and are updating themselves every day to deliver a better research experience to our customers. We have been working with the researchers in biomedical signal processing and had supported them throughout their research journey.

As a result, we have built many successful projects in the field, which marks our expertise and proves our experience. We give you entire support starting from choosing your topic to submission of thesis and publication of the paper.

We guide you from the basics and help you achieve greater heights by extending our support beyond your research project design that is to implement them in real-time. Now it is important for us to know or recall if you already know about the processes involved in biomedical signal processing.


Biomedical signals are of many forms. And so, the system used for processing them is also many. Whatever is the signal or its processing system, the process remains the same. The following are the basic biomedical signal processing processes involved.

  • Classification
    • Deep learning
      • SliceNet
      • AlexNet
      • Quantum neural network
      • Spiking neural network
    • Machine learning
      • Fuzzy intelligence
      • K-means
      • K-methods
      • SVM (one class SVM)
  • Data pre-processing
    • Normalization
    • Filtration
  • Extraction of signals
    • Real time EEG signal usage
  • Segmentation of signals
    • Reduction of dimensionality functions
      • ISOMAP
      • PCA
      • K-PCA
    • Extraction of features
      • Frequency domain extraction
      • Time domain extraction
      • Time and frequency extraction
    • Selection of features
    • Techniques for optimization
    • Correlation methods
    • Decision making methods based on multiple criteria

Our researchers and experts are fully engaged in developing research projects for one or more of these processes. The ability of a system to precisely acquire the signals and process them without losing any part is the achievement of a signal processing system.

Biomedical Signal Processing Project

The experts with us have gained ample knowledge in the field as they made themselves potential enough by solving many research questions. We are highly qualified and experienced in providing you any kind of research assistance.

If detection of biomedical signals to diagnose the disorders is the main objective of biomedical signal processing, then it is the removal of noise and artifacts that play a major game in it. Our engineers have supported enough number of projects on noise removal techniques. We are providing the noise sources in biomedical signal processing, especially its acquisition.


Noise in biomedical signals is mainly due to the artifacts and interference factors acquired along with the physiological signals. Various sources of noise include the following.

  • Heat in electronic implants
  • Electrical signals in the environment
  • Subject and sensor motion

Let us consider an example to understand the noise factors in a biomedical signal processing system. Electrical (man-made) sources are the reasons for noise in ECG.

  • It is especially 50 to 60 Hz frequency power line that has to be removed completely
  • For this purpose a filter (primarily a notch filter) is to be employed
  • Also low pass filters are used for allowing only the signals of lower frequencies (avoidance of anti-aliasing effect)

We know that the bio-medical signals are usually very feeble. Therefore encountering noise and allowing it to be a part of the signal, you will end up losing the important information from it. So it becomes essential for any researcher to build the most optimal system to get rid of the noise and artifacts present in it.

Our engineers have developed many systems to remove these noises efficiently. We have also implemented our project in real-time, and we are greatly motivated to share the ideas that we used with you. Get in touch with us by any means, and our technical team will engage with you. We will then share with you all the technical details of our projects you.

As we all know that filters are of great importance in biomedical signal processing. Now we will give you some details on filtering techniques for removing artifacts.


The following is a description of the filtering techniques used for removing artifacts. Have a look at them, and we are ready to provide you all the technical information on the methods that we adopted to design efficient filters.

  • Better setup for measurement (avoidance of noise and interference signals)
  • Noise
    • Random (noise due to thermal fluctuations leading to waveforms that are not predictable)
    • Structured (pre-determined waveform – 50 to 60 Hz power line)
  • Interference due to the other physiological signals
    • ECG signals interfered into EMG signals (back muscles)
    • EMG signals of inter-costal muscles in the ECG signals from chest leads

Based on the above objectives of filtration, we have delivered many biomedical signal processing projects. We have rendered guidance to many researchers across the world regarding the rectification of issues in designing suitable filters.

You can seek the advice of our experts in making the best-suited filter for your signal processing system. We have handled almost all types of noises and the ways in which we rectified these defects. Now let us have some idea of the signal noises types.


As the signals are very wide-ranging, the signal noises also vary. The following are the different types of signal noises.

  • Noise addition (into datasource)
  • Noise induced by equipments
  • Gaussian White Noise
  • Interference from power-line

There are also some other noises that are specific to the applications or circumstances. In such cases, we go for field analysis and grab the experience from world-class experts in our alliance and find the best solution to design the most suitable filter.

Different techniques are to be followed for filtering different types of signals. Our projects have excelled in the performance metrics used for analyzing the efficiency of a filtration technique.


As it is mentioned above, there are various methods for removing noise and artifacts. Filtration of noise artifacts or the removal of noise associated with the signals is the primary function to be performed in any signal processing method.

Especially in biomedical signal processing methods, we need to be very much careful of removing these noises. This is because; patients cannot be subjected to imaging processes just because the obtained signals are feeble and not sufficient. The following are the various techniques used for removing noise from biomedical signals.

  • Digital filter in the frequency domain
    • Savitzky – golay
    • Chebyshev and butterworth
    • Comb and notch
  • Filtration in time domain
    • Detrending
    • Moving filter (average)
    • Median filter
    • Offset filter
  • Adaptive filters (optimal filtration)
    • Kalman
    • Wiener

Our engineers have gained expertise in handling these filtering techniques. So you can approach our research experts for any guidance regarding these filters designs.

Our experts are working significantly in the development of advanced techniques for filtering and signal processing purposes. They choose their topic by considering the latest biomedical signal processing topics. So after thorough analysis, we are providing you the recent research areas in biomedical signal processing in the next section.


The following are the various latest biomedical signal processing ideas. We provide you research support on all these topics, and we also encourage you to come up with your own ideas too as our team is ready to guide you on any topic of research in biomedical signal processing.

  • Diagnosing Alzheimer’s diseases
  • Oscillations of cardiovascular functioning
  • Designing of hearing aid
  • Multimodal speech recognition
  • Processing of audio signals
  • Telemetry (and monitoring)
  • Bio-signal information (from neural networks)
  • Physiological processes
  • Modelling of bio signal
  • System and sensors (wearable)
  • Signals from cardiovascular functioning
  • Identifying and detecting functions
  • Acquisition of medical images (analysis and detection)
  • Video and image information (analysis and processing)
  • Electromagnetic fields (medicine and biology)
  • User interfaces based on bio-signals
  • Systems for real time applications
  • Biometric applications
  • Transformation of wavelet by analysing frequency
  • Recognition of pattern
  • Machine learning applications for bio signal information

We hope this overview supported you immensely. In the same way, our research experts are waiting to support your research based on biomedical signal processing projects. Get in touch with us and experience a breakthrough victory in your research career.