Abstract — Simulia Abaqus Cochlear Implant
stacks, simulation worlds, reports and viva from Bangalore. Rebecca C. Felsheim*+1,2, Sabine Hochmuth*3, Alina Kleinow1, Andreas Radeloff2,3,4, Mathias Dietz1,2 Bimodal cochlear implant users show poor localization performance. One reason for this is a difference in the processing latency between the hearing aid and the cochlear implant side. It has been shown that reducing this latency difference acutely improves the localization performance of bimodal cochlear implant users. However, due to the frequency dependency of both the device latencies and the acoustic hearing ear, current frequency-independent latency adjustments cannot fully compensate for the differences, leaving open which latency adjustment is best. We therefore measured the localization performance of eleven bimodal cochlear implant users for multiple cochlear implant latencies. We confirm previous studies that adjusting the interaural latency improves localization in all eleven bimodal cochlear implant users. However, the latency that leads to the best localization performance for most subjects was not at the latency calculated to compensate for the interaural difference at intermediate frequencies (1 kHz). Nine of eleven subjects localized best with a cochlear implant latency that was slightly shorter than the calculated latency compensation.
Sensor Interfacing & Analog Front-End Conditioning
Binaural hearing, i.e., hearing with two ears, enables listeners to pinpoint the direction of a sound with remarkable accuracy. Trained normal hearing listeners can distinguish directions of two sources separated by angles as small as 1° in front of the head (Mills, 1958). In listeners with impaired hearing, the ability to localize sounds is reduced compared to normal hearing listeners. In a study by Dorman et al. (2016) which compared the localization performance of different subject groups, bilateral hearing aid users had an average root mean square error (RMSE) of 12° and bilateral cochlear implant (CI) users had an average RMSE of 29°. This stands against an average RMSE of 5° in an approximately age-matched normal hearing control group. An even larger average RMSE of 62°, close to chance performance, was reported in a small group of bimodal CI users with a hearing aid contralateral to the CI.
Bio-Signal Waveform Analysis & Simulation Findings
Most bimodal CI users used to be severely hearing impaired in both ears but were only eligible to receive one CI due to funding restrictions. With a high-power hearing aid, these patients receive some low-frequency hearing to complement their dominant CI, but, not surprisingly, they perform very poorly in sound localization, with little to no spectral overlap between CI and hearing aid stimulation. The bimodal participants in Dorman et al.
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
Bio-Potential Sensors
ECG, EEG, EMG & PPG optical probes
Low-Noise AFE
INA128 / AD8232 instrumentation amps
Arduino Firmware
Real-time ADC interrupt sampling & BLE
MATLAB Bio-DSP
Pan-Tompkins QRS & wavelet transforms
COMSOL Multiphysics
Tissue electro-thermal & SAR simulation
IEC 60601-1 Safety
Galvanic optical & magnetic isolation
Biomedical Sensors, Electronics & Simulation Suites
Engineered using clinical-grade bio-transducers, low-noise analog amplifiers, and multi-physics simulation platforms.
| Subsystem / Software | Diagnostic & Engineering Role | Instrumentation Tier |
|---|---|---|
| Arduino / ESP32 MCU | High-speed SPI/I2C sensor polling, 500 Hz timer interrupt ADC sampling, BLE telemetry streaming | Embedded Hardware |
| MATLAB R2024b | Pan-Tompkins QRS detection, 50 Hz Butterworth notch filtering, wavelet decomposition, SpO2 ratio | Signal Analytics |
| COMSOL Multiphysics | Electromagnetic tissue absorption (SAR), bio-heat thermal transfer, acoustic transducer wave propagation | Tissue Physics |
| ANSYS Mechanical | Orthopedic bone-implant contact mechanics, prosthetic socket stress distribution, dental load FEA | Biomechanics FEA |
| AD8232 / ADS1298 AFE | Medical-grade 3-lead ECG front-end with built-in instrumentation amp, DRL feedback, and high CMRR | Clinical AFE IC |
Biomedical Acquisition & Multi-Physics Simulation Workflow
Comprehensive signal flow from human bio-potential electrodes and optical sensor probes to embedded Arduino firmware, MATLAB DSP, and COMSOL tissue modeling.
(ECG/EEG/PPG/Temp)
(INA128 / DRL Loop)
500Hz ADC Sampling
& QRS Detection
Tissue FEA Models
& EHR Dashboard
Verified Medical Waveforms, Diagnostic Images & Sensor Circuits
Advantages of the Simulia Abaqus Cochlear Implant Project
Why this biomedical engineering project delivers superior academic grades and provides high technical evaluation standing.
Clinical Diagnostic Precision
Combines high-CMRR analog front-ends with precision digital filtering to extract microvolt bio-potentials with high SNR (> 35 dB).
Rigorous IEC 60601-1 Patient Safety
Incorporates dual galvanic isolation barriers (optical/magnetic) ensuring patient auxiliary leakage currents remain strictly under 10 μA.
Multi-Sensor Vital Signs Fusion
Simultaneously captures ECG, PPG, SpO2, Core Temperature, and Respiration for comprehensive physiological health monitoring.
Real-Time Arduino Embedded Firmware
Features optimized C++ interrupt-driven ADC sampling routines ready for immediate deployment on Arduino, ESP32, and STM32.
Validated Pan-Tompkins QRS Algorithm
Executes derivative, squaring, moving-window integration, and dual-threshold peak detection with > 99% R-peak accuracy.
COMSOL & ANSYS Multi-Physics Coupling
Models complex anatomical tissue structures, bio-electromagnetic Specific Absorption Rates (SAR), and prosthetic stress fields.
Driven Right Leg (DRL) Noise Suppression
Actively senses and neutralizes 50/60 Hz powerline hum and common-mode displacement currents without loading patient electrodes.
DICOM Medical Imaging Pipeline
Processes clinical MRI, CT, and Ultrasound scans using MATLAB contrast enhancement and morphological lesion boundary segmentation.
Direct Telehealth & Hospital IoT Readiness
Streams validated vital signs to mobile dashboards and electronic health records over encrypted BLE and MQTT protocols.
Complete Turnkey Project Package
Delivered with full Arduino .INO sketches, circuit schematics, MATLAB .m algorithms, university-format report, and viva defense slides.
Sample Arduino Firmware, ECG Waves & Sensor Telemetry
Representative excerpts illustrating the embedded sensor acquisition loop, live physiological waveform traces, and clinical parameter schedules.
[*] Initializing Analog Front-End (AFE) Signal Acquisition... [✓] Driven Right Leg (DRL) Active Feedback: 50 Hz Hum Attenuated by 42 dB ──────────────────────────────────────────────────────────────────────── [10:14:02.102] TRACE ECG Lead-II: ---__/\_/\_/\___ (Baseline: 0.02 mV | Normal ST Segment) [10:14:02.115] QRS R-Peak Registered (Fiducial Trigger: 1.24 mV | R-R Interval: 832 ms) [10:14:02.128] VITALS Heart Rate: 72.1 BPM | SpO2: 98.5% | PR Interval: 156 ms [10:14:02.140] TRACE ECG Lead-II: ---__/\_/\_/\___ (Baseline Stable | SNR = 36.8 dB) ──────────────────────────────────────────────────────────────────────── [✓] Diagnostic Evaluation: Sinus Rhythm Verified. Zero Ectopic Ventricular Beats.
[*] Synchronizing vital signs telemetry stream across bio-sensor channels...
| Physiological Parameter | Primary Transducer | Measured Amplitude | Sampling Bandwidth | Clinical Status |
|---|---|---|---|---|
| Cardiac Electrocardiogram (ECG) | Ag/AgCl + AD8232 AFE | 1.24 mV (R-Peak) | 0.05 Hz - 150 Hz | NORMAL SINUS |
| Pulse Oximetry (SpO₂) | MAX30102 Dual Red/IR | 98.5% Saturation | 100 Hz Optical | OPTIMAL |
| Heart Rate (HR) | R-R Peak Derivative | 72.1 BPM | 30 - 220 BPM range | EUKINETIC |
| Core Body Temperature | NTC Medical Thermistor | 36.8 °C | 0.1 Hz Thermal | AFEBRILE |
[✓] Diagnostic Evaluation: Patient physiological parameters within verified clinical tolerance band.
Frequently Asked Questions
Project Deliverables & Support
Everything needed from project inception to final viva defense — delivered with expert biomedical support.
.INO Sketches
Schematics
Algorithms
Report
Slides
Models
Datasets
Q&A
Support
Extension