
Client
A U.S.-based MedTech company specializing in advanced solutions for neurosurgical and orthopedic operating rooms. The client focuses on enhancing intraoperative decision-making and patient outcomes through the use of intelligent medical technologies.
Challenges
The client aimed to develop a cutting-edge Intraoperative Neurophysiological Monitoring (IONM) platform capable of real-time signal acquisition, analysis, and anomaly prediction across various neurophysiological modalities. Key challenges included:
- Multi-modal Signal Integration: Acquiring and synchronizing signals such as SSEP, MEP, EMG, EEG, and BAEPs from different electrode locations with minimal latency.
- Noise Reduction & Signal Fidelity: Preprocessing high-frequency biological signals in noisy surgical environments.
- AI-Based Real-Time Decision Support: Detecting subtle signal anomalies with high precision and near-zero delay.
- Scalable OR Dashboard: Visualizing signals and AI outputs in an intuitive and actionable format for the surgical team.
- Regulatory Compliance: Designing a system that aligns with FDA and HIPAA standards while maintaining model transparency.
- LLM-Based Clinical Assessment & Recommendations: Integrating large language models to assess complex surgical signal patterns and provide contextual AI recommendations to assist clinicians during critical decision-making.
Solution
1. Signal Acquisition Subsystem: A robust signal acquisition interface was engineered using custom DAQ systems integrated with commercial biosignal amplifiers. This subsystem supported various electrode configurations (scalp, cranial, spinal, limb) and sampled at rates up to 10 kHz per channel, preserving waveform fidelity.
2. Signal Preprocessing Pipeline: A multi-stage filtering architecture was implemented to enhance signal clarity:
- Bandpass filters tuned per modality (e.g., 20–500 Hz for EMG, 1–70 Hz for EEG)
- Notch filters to eliminate 50/60 Hz interference
- Wavelet transforms and Z-score based artifact rejection
- Trigger-based segmentation to isolate stimulus-locked epochs
3. Feature Extraction Engine: Key diagnostic features were extracted from each signal type:
- SSEP: N20/P37 latency and amplitude
- MEP: Latency, morphology
- EMG: Spike train patterns, RMS power
- EEG: Band power, spike detection, coherence
- BAEPs: I–V wave interval and SNR
A patient-specific adaptive baseline calibration ensured high precision.
4. AI-Based Anomaly Detection Module: The core intelligence layer integrated multiple AI models for anomaly detection:
- Autoencoders: Identified waveform distortions from learned signal baselines
- LSTM networks: Predicted time-series deviations
- Random Forests: Classified anomalies based on extracted features
Training used 1.2M waveform samples from synthetic and open-source datasets. The models achieved:
- 95% precision for SSEP anomaly detection
- Sub-second alert latency post-distortion
5. Visualization & Alerting Dashboard: A responsive, web-based OR dashboard was developed for real-time visualization:
- Live waveform streaming with Plotly.js
- Color-coded alerts (green/yellow/red) tied to AI output
- Anomaly logs, manual tagging, and explainable AI tooltips for transparency
- Role-based access and secure communication in compliance with HIPAA
6. LLM-Driven Clinical Insight and Recommendation Engine
Integrated a domain-tuned large language model (LLM) to interpret detected anomalies in the context of surgical procedures, historical patient data, and clinical protocols. The LLM provided real-time narrative explanations, risk summaries, and recommended actions based on intraoperative signal trends.
- Enabled intelligent interaction between neural signal events and contextual clinical knowledge.
- Supported the surgical team with situational awareness and guided decision-making through dynamic AI-generated suggestions.
Benefits
- Enhanced Surgical Safety: Enabled rapid detection of neurological compromise during surgery, allowing for timely intervention.
- Real-Time Intelligence: AI-powered insights delivered within seconds of signal deviation, improving intraoperative decisions.
- Scalable and Configurable: The platform was adaptable to various electrode configurations, surgical workflows, and hospital systems.
- FDA-Ready Architecture: Included comprehensive audit trails, model interpretability, and compliance layers supporting future regulatory approval.
- Clinical Empowerment: Provided clinicians with a hybrid interface combining AI recommendations and manual override capabilities.
- Context-Aware Recommendations: The integrated LLM provided real-time, context-rich recommendations and explanations, helping surgical teams make informed decisions faster.

Tech stack
- Signal Acquisition: C++, Python (PyDAQ), ZeroMQ, Redis Streams
- Preprocessing: Python (SciPy, MNE), CUDA
- Feature Extraction: Python, custom modules for per-modality signal processing
- Anomaly Detection: Autoencoders, LSTM, Random Forest, TensorFlow/PyTorch
- Visualization & Dashboard: ReactJS, WebSockets, Plotly.js, Flask, MQTT, HIPAA-compliant user auth
- Clinical Insight & Guidance: GPT-based LLM, LangChain, Flask APIs, Fine-tuned Prompt Templates
About Emorphis
Emorphis Technologies is a world-class software development and solutions company that truly believes in “Innovation in motion”. Delivery innovation on the go at an accelerated pace has been our success mantra to date. Over the years we have provided value to our clients in the fields of enterprise mobility, cloud, IoT, backend development, Big Data Analytics, and Blockchain.
We serve industries ranging from unicorns, and startups to large multinationals in the healthcare, telecommunications, fintech, retail, and publishing industry. Our go-to-market software products – iStatement, iPublisher, and iBuggy have proved our metal with positive beneficial customer testimonials. We help our clients with successful product development, consulting services, and testing (manual & automated).
We have profound experience & expertise in various technologies like .Net, J2EE, PHP, iOS, Android, and Cloud Computing viz. Amazon Web services (AWS), Software QA & testing (Manual & Automation). Our designed products are cloud-ready and can be readily deployed on AWS/Azure cloud infrastructure.
Our pivot on engineering innovation and R&D helps quicken time-to-market, ensuring high quality at economies of scale, delivering cult competency for the global marketplace. We ensure that your ideas, concepts, and requirements are backed by brilliant execution at our end. Having said that we extend end-to-end ownership of product/application design, development, and deployment.