Simulia Abaqus Disc Degeneration — Biomedical Instrumentation & Clinical Sensing

Abstract — Simulia Abaqus Disc Degeneration

(may require institutional access). Bangalore guidance for biomedical FEA and medical device simulation students. Biomedical signal processing extract meaningful information from physiological signals like electrocardiograms (ECGs), electroencephalograms (EEGs), and electromyograms (EMGs) to diagnose, monitor, and treat medical conditions and diseases such as seizures, cardiomyopathy, and neuromuscular disorders, respectively. Traditional manual physician analysis of electrical recordings is prone to human error as subtle anomolies may not be detected. Recently, advanced deep learning has significantly improved the accuracy of biomedical signal analysis. A multi-modal deep learning model is proposed that utilizes discrete wavelet transforms for signal pre-processing to reduce noise. A multi-modal image fusion and multimodal feature fusion framework is utilized that converts numeric biomedical signals into 2D and 3D images for image processing using Gramian angular fields, recurrency plots, and Markov transition fields. In this paper, deep learning models are applied to ECG, EEG, and human activity signals using actual medical datasets, brain, and heart recordings. The results demonstrate that using a multi-modal approach using wavelet transforms improves the accuracy of disease and disorder classification.

Sensor Interfacing & Analog Front-End Conditioning

Electroencephalography (EEG) is the recording of electrical signals emanated from human brain, which can be collected non-invasively from the scalp by amplifying voltage differences between electrodes placed on the scalp or cerebral cortex and a brain-computer interface (BCI). A brain-computer interface (BCI) is a direct communication and control system that is established between the human brain and an electronic device. The number of electrodes can vary from one to 256. The electrodes are placed at certain predefined positions according to the international 10/20 system or its variants. The weak electrical activity detected by the electrodes ranges from 5 to 100 µ \mu V, and the frequency range of interest is between 1 - 40 Hz . EEG converts “brain activity to time series data with amplitude on the y-axis, and this data can then be used to understand brain functions. ” Figure 8 illustrates the placement of the electrodes on the scalp.

Bio-Signal Waveform Analysis & Simulation Findings

Thus, each electrode has a number code that indicates where on the brain it is located. The EEG measures voltage (difference in electrical potential) between each electrode and a reference electrode. Thus, whatever signal is present at the reference electrode is effectively subtracted from all the measurement electrodes. The measurements generate for each electrode can serve as features of the EEG signal for deep learning.

Scope & Clinical Applications

• Real-time multi-parameter vital signs telemetry (ECG, Heart Rate, SpO2, Temperature, Respiration)
• Low-noise Analog Front-End (AFE) conditioning with active Driven Right Leg (DRL) 50 Hz cancellation
• Digital signal processing in MATLAB: Pan-Tompkins QRS detection, FIR/IIR notch, and Continuous Wavelet Transforms
• Finite element tissue bio-electromagnetics and orthopedic mechanical simulations in COMSOL & ANSYS
• Complete Arduino firmware .INO sketches, circuit schematics, and university dissertation reports