SECTION A
(Attempt all – 2 marks each)
(a) Types of biomedical transducers
Biomedical transducers convert physiological signals into electrical signals. Common types include pressure transducers, temperature transducers, displacement transducers, optical transducers, chemical transducers, and bioelectrical transducers.
(b) Bradycardia, tachycardia, and arrhythmia
Bradycardia is a condition where heart rate is below normal.
Tachycardia is a condition where heart rate is above normal.
Arrhythmia refers to irregular heart rhythm.
(c) ERG
Electroretinogram (ERG) is a diagnostic test that measures the electrical activity of the retina in response to light stimulation.
(d) Joint probability
Joint probability is the probability that two or more random events occur simultaneously.
(e) Biomedical signals
Examples include ECG, EEG, EMG, ERG, EOG, blood pressure signals, and respiratory signals.
(f) Direct vs indirect measurement of blood pressure
Direct measurement involves inserting a catheter into an artery and provides accurate continuous readings.
Indirect measurement uses a cuff and is non-invasive but less precise.
(g) Maximum entropy method
The maximum entropy method states that among all possible spectral estimates, the one with maximum
entropy best represents the signal without introducing additional assumptions.
(h) Amplifier used in ECG
An instrumentation amplifier is used in ECG systems because it amplifies weak ECG signals while rejecting common-mode noise and interference.
(i) Brain wave patterns
Brain waves include alpha, beta, theta, delta, and gamma waves, each corresponding to different mental states.
(j) EP estimation
Evoked Potential (EP) estimation refers to extracting small stimulus-related signals from background noise in EEG recordings.
SECTION B
(Attempt any three – 10 marks each)
2(a) Most common artifact in ambulatory ECG
The most common artifact in ambulatory ECG is motion artifact. It occurs due to patient movement, electrode displacement, and changes in skin-electrode impedance. Motion artifacts distort ECG signals and may mimic abnormal heart activity, leading to misinterpretation.
2(b) Algorithm for QRS complex detection
QRS detection algorithms typically involve signal preprocessing, noise filtering, differentiation to highlight slope changes, squaring to enhance peaks, integration to obtain waveform energy, and thresholding to detect QRS complexes accurately.
2(c) Time-frequency analysis in biomedical signals
Time-frequency analysis helps analyze non-stationary biomedical signals like ECG and EEG. It provides information about how frequency components change over time, improving diagnosis and signal interpretation.
2(d) Electrical activity of the heart and Einthoven’s triangle
The heart’s electrical activity originates from the sinoatrial node and propagates through atria and ventricles. Einthoven’s triangle represents the spatial relationship of limb leads and helps measure ECG signals accurately.
2(e) Low-pass filter using Kaiser window
A low-pass filter using Kaiser window is designed by selecting cutoff frequency, determining filter order, computing Kaiser window parameters, and applying it to the ideal impulse response to reduce high-frequency noise.
2(f) Short notes
(i) MATLAB in biomedical signals
MATLAB is widely used for signal analysis, filtering, visualization, and algorithm development in biomedical applications.
(ii) Laser applications in biomedical field
Lasers are used in surgery, diagnostics, imaging, therapy, and tissue treatment due to their precision and minimal invasiveness.
SECTION C
3(a) AZTEC algorithm
(i) Reconstructed waveform
AZTEC reconstructs the signal using alternating amplitude and duration values, producing a step-like waveform.
(ii) Data reduction
Data reduction is achieved by storing only significant signal changes instead of all samples.
(iii) Peak-to-peak amplitude
Peak-to-peak amplitude is the difference between maximum and minimum signal values.
3(b) Adaptive noise canceller
An adaptive noise canceller removes unwanted noise using a reference noise input and adaptive filtering. It continuously updates filter coefficients to minimize error between noisy signal and estimated noise.
4(a) Discrete signal epochs and physiological correlation
Biomedical signals can be divided into epochs corresponding to physiological events such as heartbeat phases or neural responses. Correlating these epochs improves diagnosis and understanding of body functions.
4(b) EEG spectral estimation methods
EEG analysis uses methods such as Fourier transform, power spectral density estimation, autoregressive modeling, and maximum entropy methods to study brain activity.
5(a) Auto-correlation and cross-correlation
Auto-correlation measures similarity of a signal with itself over time delay.
Cross-correlation measures similarity between two different signals and helps identify relationships.
5(b) Analog-to-Digital Conversion
ADC converts continuous biomedical signals into digital form using sampling, quantization, and encoding, enabling digital processing and storage.
6(a) System function H(z)
The system function is obtained by dividing output Z-transform by input Z-transform:
H(z)=Y(z)X(z)H(z) = \frac{Y(z)}{X(z)}H(z)=X(z)Y(z)
This represents the filter characteristics of the system.
6(b) Adaptive noise canceller
(Explained earlier with example – same concept applies.)
7(a) Huffman coding for ECG data
Huffman coding assigns shorter codes to frequently occurring values and longer codes to rare values, reducing data size efficiently.
7(b) Wavelet detection
Wavelet detection analyzes biomedical signals at multiple resolutions. Adaptive wavelet detection adjusts wavelets based on signal characteristics for improved accuracy.
