Innovative Health Data Solutions

Transforming multichannel time-series data into actionable insights for healthcare innovation and monitoring.

Innovating Healthcare Data Solutions

We specialize in advanced data collection and processing for healthcare, utilizing cutting-edge technology to enhance patient monitoring and improve outcomes.

A person wearing a smartwatch writes on a notepad while using a tablet that displays medical images. A stethoscope is positioned close by on a white desk, suggesting a medical setting.

Advanced Data Solutions

We specialize in multichannel time-series data collection and innovative machine learning architectures.

Hands typing on a laptop with a stethoscope resting on the wooden table beside it. The setting suggests a work environment that combines technology with healthcare.

Data Collection Services

Collect and preprocess multichannel time-series data from various healthcare sources efficiently.

Machine Learning Development

Develop advanced architectures for generating synthetic healthcare data using cutting-edge techniques.

Event Annotation Services

Annotate rare events and device failures in time-series data for accurate analysis.

Data Innovation

Transforming healthcare through advanced data collection and analysis.

A medical setting with two people, likely healthcare professionals, focusing on a patient who is surrounded by numerous medical devices and monitors. The environment is busy with various tubes and equipment connected to the patient.

IOMT-GAN Project

Developing a robust architecture for generating synthetic healthcare data using advanced machine learning techniques.

The image depicts a clean and modern hospital room featuring a hospital bed with advanced monitoring equipment attached. The room is well-lit with ceiling lights and has a large window with blinds, allowing natural light to enter. The floors are made of polished wood, and there are neutral-colored walls. Medical equipment and a computer monitor are suspended above the bed, contributing to the sterile and professional atmosphere of the room.

Phase Two

Building a generator and discriminator to enhance data authenticity and reliability in healthcare monitoring systems.

With the proliferation of medical IoT (IoMT) in hospital monitoring, remote care, and wearable devices, massive multichannel time-series sensor data present unprecedented opportunities for clinical decision-making and intelligent diagnostics. However, these data often suffer from high noise levels, missing segments, uneven device distributions, scarcity of rare-event samples, and privacy compliance constraints, limiting the performance and generalization of downstream supervised models. While Generative Adversarial Networks (GANs) have successfully synthesized medical images and textual data, there has been little systematic research on high-dimensional, multichannel, low‐signal‐to‐noise‐ratio IoMT time-series data synthesis.

Therefore, our central research question is:

Can we design a hybrid architecture combining conditional GANs (cGANs) and recurrent neural networks (RNNs) to generate synthetic IoMT time-series samples whose statistical properties and clinical-event correlations closely match real sensor data, in order to:

Augment Rare-Event Samples: Produce high-fidelity samples for low-frequency yet high-risk events such as arrhythmias and apnea to boost recall rates of detection models;

Protect Data Privacy: Preserve global distributions and temporal dependencies without leaking original patient data, enabling safe model training and sharing;