JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Prerequisite

Advanced Digital Signal Processing

Course Objectives

The main objectives of the course are:

• 1. To use basic probability theory to model random signals in terms of Random Processes.
• 2. To understand various cardiological signal processing techniques and noise cancellation techniques.
• 3. To understand estimation of signals using Prony’s and least square and linear prediction methods.
• 4. To comprehend EEG signals, modeling and sleep stages.

Course Outcomes

After studying the course, each student is expected to be able to:

• 1. Use probability theory to model random processes.
• 2. Compare various lossless and lossy data compression techniques.
• 3. Compare various ECG processing and noise cancellation techniques.
• 4. Model and estimate EEG signals and various sleep stages.

UNIT - I

Random Processes: Stationary random process, Ergodicity, Power spectral density and autocorrelation function of random processes. Noise power spectral density analysis, Noise bandwidth and noise figure of systems.

UNIT - II

Data Compression Techniques: Lossy and Lossless data reduction Algorithms. ECG data compression using Turning point, AZTEC, CORTES, Huffman coding, vector quantisation, DICOM Standards

UNIT - III

Cardiological Signal Processing: Pre-processing, QRS Detection Methods, Rhythm analysis, Arrhythmia Detection Algorithms, Automated ECG Analysis, ECG Pattern Recognition. Adaptive Noise Cancelling: Principles of Adaptive Noise Cancelling, Adaptive Noise Cancelling with the LMS Adaptation Algorithm, Noise Cancelling Method to Enhance ECG Monitoring, Fetal ECG Monitoring.

UNIT - IV

Signal Averaging, polishing: Mean and trend removal, Prony’s method, Prony's Method based on the Least Squares Estimate, Linear prediction, Yule – Walker (Y –W) equations, Analysis of Evoked Potentials.

UNIT - V

Neurological Signal Processing: Modelling of EEG Signals, Detection of spikes and spindles Detection of Alpha, Beta and Gamma Waves. Auto Regressive (A.R.) modelling of seizure EEG. Sleep Stage analysis, Inverse Filtering, Least squares and polynomial modelling.

R19 M.TECH. SYSTEMS AND SIGNAL PROCESSING

TEXT BOOKS:

• 1. Probability, Random Variables & Random Signal Principles – Peyton Z. Peebles, 4th Ed., TMH, 2009.
• 2. Biomedical Signal Processing- Principles and Techniques - D. C. Reddy, TMH, 2005.

REFERENCES:

• 1. Digital Bio Dignal Processing - Weitkunat R, Elsevier, 1991.
• 2. Biomedical Signal Processing -Vol. I Time & Frequency Analysis - Cohen.A, CRC Press, 1986.
• 3. Biomedical Digital Signal Processing: C-Language Experiments and Laboratory Experiments, Willis J. Tompkins, PHI, 1998. R19 M.TECH. SYSTEMS AND SIGNAL PROCESSING M.TECH. I YEAR - I SEMESTER SYSTEMS AND SIGNAL PROCESSING