Biomedical signal processing involves applying engineering principles and techniques to medical fields. It combines engineering design skills with medical sciences to improve healthcare diagnosis and treatment. Some key biomedical signals discussed include ECG, EMG, EEG, and others. There are several research gaps and areas discussed such as signal conditioning, feature extraction, optimization techniques, and classification methods. Machine learning and deep learning approaches using techniques like convolutional neural networks show promise for biomedical signal processing applications in areas like medical research.

1. BIO MEDICAL SIGNAL
PROCESSING:
RESEARCH PERSPECTIVE
Dr. Meenakshi Sood
Department of Electronics & Communication Engg
Jaypee University of Information Technology
Meenakshi.s.sood@ieee.org, meenakshi.sood@juit.ac.in

2. Biomedical SIGNAL PROCESSING
• Application of engineering principles and techniques to
the medical field to close the gap between engineering
and medicine.
• Guide the medicine to use innovative educational tools
such as humanistic models, realistic simulations, web-
based online resources, etc.
• It combines the design and problem solving skills of
engineering with medical and biological sciences to
improve healthcare diagnosis and treatment.
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3. The Need…
• Medical decision-making requires the clinician to apply
accumulated knowledge to a specific amount of patient
information to produce a result that may be
 a diagnosis,
 prognosis,
 course of therapy, or
 the selection of further tests.
• Limited resources - increased demand
• Insufficient time available for diagnosis and treatment.
• Need for systems that can improve health care processes
and their outcomes in this scenario
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4. Sub-disciplines
• Bioinstrumentation
• Biomaterials
• Biomechanics
• BioInformatics
• Biomedical computing & signal processing
• Medical Imaging
• Orthopaedic Bioengineering
• Rehabilitation Engineering
• Minimally invasive surgery
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5. Application Areas

6. Examples of Biomedical Signals
Electroneurogram (ENG)
Electromyogram (EMG)
Electrocardiogram (ECG)
Electroencephalogram (EEG)
Electrogastrogram (EGG)
Electroocculogram (EOG)
Phonocardiogram (PCG)
Vibromyogram (VMG)
Vibroarthogram (VAG)
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7. ECG (electrocardiograph)
• electrical activity of the heart
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EMG
EMG (Electromyogram)
signal generated by muscle cells

8. EGG(Electrogastrogram)
• The electrical activity of the stomach consists of rhytmic waves of
depolarization and repolarization of its constituent smooth muscle
cells.
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EOG(Electroocculogram)
•Eye movements produce a moving (rotating) dipole
source and, accordingly, signals that are a measure of
the movement are obtained .

9. IMAGES- 2D SIGNAL
Benign Malignant
CT Images US Images
Microscopic Images of Blood
MRI Images
DNA sequence signal

10. RESEARCH FIELDS
User/Patient
Interface
Technology
Signal Analysis
Pre processing
Data Acquisition
Statistical Analysis:
Pattern Recognition
Initiate a warning or a variety
of therapies
Stimulator
Drug
Optimization:
Feature Extraction/
Clustering
Nurse
Feature 1
Feature 2
Feature 3
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11. RESEARCH GAPS
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Signal Conditioning
Transformation and reduction of the signals
Identification of Diagnostic features
Non Stationarity
Non Linearity
Optimization
Classifiers
Pattern Recognition

12. Signal Conditioning
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13. Signal transformation
• Noise component:
– due to the electronics in the measuring device,
– artifacts related to the patient’s movements, or
– other background signals recorded simultaneously
• More data than actually needed to derive parameters offering
semantic information
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14. Parameter/ Feature selection
• Usually, relevant information is not the direct result
of a sample or recording of a signal.
• Parameters bearing resemblance to the signs and
symptoms that are used to make diagnosis are
extracted from the signal.
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15. Featureextraction

16. Feature selection

17. Feature Selection
Steps
• Feature selection is an
optimization problem.
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Search strategies
Optimum
Heuristic
Randomized
Evaluation strategies
-Filter methods
-Wrapper methods

18. Optimization Techniques
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19. Classification methods
• Classification algorithms learn from the given input
data and uses this learning to classify new set of
observations.
• Different classification algorithms that can be used
are:
Classification Methods
Nearest
Neighbor
Naïve
Bayes
Decision
Tree
Random
Forest
ANN PNN SVM ANFC

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Signal classification:
Computer Aided Diagnostic system

21. Transition…….
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Machine Learning approach:

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24. Deep Learning approach
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Convolutional Neural Network

27. EXAMPLE
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28. Research and Application
• Phyological Research
• Neurological Research
• Medical Research
• BioInformatics Research
• Educational Research and Application
• Therapeutic Application
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