This document discusses neural networks and their applications in signal processing. It begins with an introduction to neural networks and their biological inspiration. Applications of neural networks in signal processing are then outlined, including EEG, EMG, medical diagnosis, and more. The document reviews two papers on deep learning applications in biomedical fields and identifies challenges around model building and interpretability. It sets research objectives to optimize mathematical models and improve result interpretation. In conclusion, deep learning has advanced pattern recognition but challenges remain around data availability and model methodology.

1. Neural networks in signal processing
Presented By :- Abida Yousuf

2. Presentation outline
 Introduction to Neural networks
 Why neural networks?
 Applications of neural networks
 Literature survey
 Research gap
 Research objectives
 Conclusion and future scope
 References

3. Introduction
Artificial neural network
 Artificial Neural Network (ANN) is a branch of
Artificial intelligence.
 It is a mathematical model that tries to simulate
the structure and functionalities of biological
neural networks.
 Neural networks(REGRESSION)…....Prediction
Training
Generalization
Back propagation

4. Biological neuron vs artificial neuron

5. Continued…
 The weights can be implemented in a square
weight matrix W with the row number
indicating where the connection begins, and
the column number which neuron is the
target.
 The propagation function converts vector
inputs to scalar network inputs.
 The activation is the "switching status" of a
neuron.
 Neurons get activated if the network input
exceeds their threshold value.
 The activation function determines the
activation of a neuron dependent on network
input and threshold value.

6. Why Neural networks?
An ANN derives its power through its massive parallel distributed structure, and, its ability to learn and
therefore generalize. Generalization refers to getting outputs for inputs not encountered during training.
Some of the typical characteristics of the use of ANN are :
 Nonlinearity
 Input-output Mapping
 Adoptability.
 Fault Tolerance.
 VLSI implementability.
All these characteristics make the ANN an ideal tool for use in adaptive pattern classification, signal
processing, and control. The VLSI implementation provides a means for capturing truly complex behaviour
in hierarchical fashion and thus suitable for real-time applications.

7. Applications of neural networks and signal processing
 EEG and EMG
 Machine diagnostics
 ECG signal processing
 Image compression
 Medical diagnosis
 Voice recognition
 Fraud detection

8. Appl. Sci. 2019, 9, 1526;
doi:10.3390/app9081526
www.mdpi.com/journal/applsci
Ryad Zemouri.et al: Deep Learning in
the Biomedical Applications: Recent and
Future Status
Literature survey
 This paper reviews the major deep learning
concepts pertinent to biomedical applications.
Research Gap
Many difficulties such as model building or the
interpretability of the obtained results are
encountered by deep learning users.

9. Daniele Ravi.et al: Deep Learning for
Health Informatics
Literature survey
The paper mainly focuses on key applications of deep
learning in the fields of translational bio-informatics,
medical imaging, pervasive sensing, medical
informatics, and public health.
Research gap
important aspect to take into account when deep
learning tools are employed, is that for many
applications the raw data cannot be directly used as
input for the DNN. Thus, pre processing, normalization
or change ofinput domain is often required before the
training
Ieee journal of biomedical and health informatics, vol. 21,
no. 1, january 2017

10. Research objectives
 Merger of neural networks and signal processing.
 Using deep learning in biomedical field with main focus on model building i.e
somehow trying to optimize the complexity of mathematical models and work on
improving the interpretability of the results.

11. Conclusion
• Deep learning has gained a central position in recent years in machine learning and pattern recognition. This
is advantageous for many problems in health informatics and has eventually supported a great leap
forward for unstructured data such as those arising from medical imaging, medical informatics, and
bioinformatics.
• However, several technical challenges remain to be solved. Patient and clinical data is costly to obtain and
healthy control individuals represent a large fraction of a standard health dataset. Deep learning
algorithms have mostly been employed in applications where the datasets were balanced, or, as a work-
around, in which synthetic data was added to achieve equity. The later solution entails a further issue as
regards the reliance of the fabricated biological data samples. Therefore, methodological aspects of NNs
need to be revisited in this regard.

12. References
[1]Ryad Zemouri , Noureddine Zerhouni , and Daniel Racoceanu , “Deep Learning in the
Biomedical Applications” Appl. Sci. 2019, 9, 1526; doi:10.3390/app9081526
www.mdpi.com/journal/applsci
[2]Daniele Rav`ı, Charence Wong, Fani Deligianni, and Melissa Berthelot, “Deep Learning for
Health Informatics”, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 21, NO. 1,
JANUARY 2017