SECTION A
(Attempt all | 2 × 10 = 20 Marks)
(a) General purpose microprocessor vs DSP
A general-purpose microprocessor is designed for a wide range of applications and performs sequential operations, whereas a DSP (Digital Signal Processor) is optimized for real-time signal processing with features like fast multiply-accumulate operations and parallel architecture.
(b) Dominant frequencies in sleep EEG
Delta (0.5–4 Hz): Deep sleep Theta (4–7 Hz): Light sleep
Alpha (8–13 Hz): Relaxed wakefulness Beta (13–30 Hz): Alert state
(c) QRS complex
The QRS complex represents ventricular depolarization in an ECG signal and indicates the contraction of the ventricles.
(d) Use of FAN algorithm
The FAN (Floating Amplitude Normalization) algorithm is used to detect QRS complexes in ECG signals by enhancing peak detection under varying signal amplitudes.
(e) EP estimation
EP (Evoked Potential) estimation refers to extracting small stimulus-related signals from EEG by averaging repeated responses.
(f) Different patterns of brain waves
Delta, Theta, Alpha, Beta, and Gamma waves.
(g) Action potential and resting potential
Resting potential is the voltage difference across a cell membrane when inactive. Action potential is the rapid change in membrane potential during nerve impulse transmission.
(h) Need for data reduction
Data reduction reduces storage requirements, transmission bandwidth, and computational complexity while preserving essential diagnostic information.
(i) Sleep EEG
Sleep EEG records electrical brain activity during sleep to analyze sleep stages and disorders.
(j) Types of biomedical signals
ECG, EEG, EMG, EOG, blood pressure signals, respiratory signals, and evoked potentials.
SECTION B
(Attempt any THREE | 10 × 3 = 30 Marks)
2(a) Objectives of biomedical signal analysis
Biomedical signal analysis aims to extract useful physiological information, detect abnormalities, assist diagnosis, monitor patient health, and support treatment planning.
Objectives include noise removal, feature extraction, pattern recognition, compression, and interpretation of physiological signals.
(Block diagram explanation: signal acquisition → preprocessing → feature extraction → analysis → diagnosis)
2(b) Portable arrhythmia monitor
A portable arrhythmia monitor continuously records ECG signals over long durations. It detects irregular heart rhythms using electrodes, amplifiers, signal processors, memory units, and display systems. These devices help diagnose intermittent cardiac abnormalities.
2(c) Three approaches for QRS detection
Amplitude thresholding: Detects peaks exceeding a threshold
Slope-based detection: Uses rapid slope changes in QRS
Digital filtering: Enhances QRS frequency components
2(d) EEG analysis using spectral estimation
EEG analysis uses methods like: FFT-based spectral analysis
Autoregressive (AR) modeling Time-frequency analysis
These techniques estimate power distribution across frequency bands to study brain activity.
2(e) Adaptive wavelet detection
Adaptive wavelet detection uses wavelet transforms to identify transient features like QRS complexes. Overlapping wavelets are detected by matching signal patterns at multiple scales, improving accuracy in noisy signals.
SECTION C
3(a) Run Length Encoding (RLE)
Run Length Encoding reduces data by representing consecutive repeated values as a single value and count.
Example: 111122 → (1,4)(2,2).
It is simple and effective for slowly varying biomedical signals.
3(b) Discrete signal epochs
Biomedical signals are divided into time segments (epochs) and correlated with physiological events such as heartbeats or brain responses to analyze functional relationships.
4(a) Importance of signal averaging
Signal averaging enhances weak signals by reducing uncorrelated noise.
In ECG averaging, repeated beats are aligned and averaged to improve signal-to-noise ratio.
(Block diagram + flow chart explanation: acquisition → alignment → averaging → output)
4(b) Lossless and lossy data compression
Lossless: No information loss (RLE, Huffman coding)
Lossy: Some information loss (AZTEC, wavelet compression)
Classification: Direct data reduction, transform-based, predictive coding.
Lossless algorithm example: Run Length Encoding.
5(a) AZTEC reconstructed waveform issues
AZTEC reconstruction produces staircase-like waveforms, distorting ECG morphology.
This is unacceptable to cardiologists as it alters diagnostic features.
Improvements include slope interpolation and adaptive thresholding.
5(b) Markov model for sleep EEG
Markov models represent sleep stages as probabilistic state transitions.
They work well for stage sequencing but fail when external factors (stress, drugs) influence sleep patterns.
6(a) AZTEC encoding problem
(i) Data reduction:
Original data: continuous samples Encoded data: pairs → significant reduction (typically >80%)
(ii) Peak-to-peak amplitude:
Maximum = +50, Minimum = −6 Peak-to-peak = 56 units
6(b) Removal of baseline wander and power-line interference
Baseline wander: Removed using high-pass filtering or polynomial fitting
Power-line interference: Removed using notch filters at 50/60 Hz
7(a) Detection and estimation of Epilepsy
Epilepsy detection involves identifying abnormal EEG spikes, rhythmic discharges, and seizure patterns. Techniques include time-domain analysis, frequency analysis, wavelet transforms, and pattern recognition.
7(b) Signal averaging SNR problem
Noise amplitude = 4 × signal Required SNR = 4:1
SNR improves as √N N=16⇒N=256\sqrt{N} = 16 \Rightarrow N = 256N=16⇒N=256
Answer: 256 sweeps are required.
