Vital Signals Systems develops AI-powered software and hardware that analyze biomedical signals across brain, heart, sleep, movement, and respiratory systems.
Modern healthcare generates more biomedical data than ever before, yet many patients, clinicians, and communities still struggle to benefit from it.
Vital Signals Systems was founded to bridge that gap.
We design and validate our software and hardware with deliberate attention to the patients and communities that clinical research has too often missed, building from the start for the diversity of the people our work is meant to serve.
AI-based systems that analyze physiological and neurological signals to identify patterns associated with seizure risk.
Tools to monitor motor symptoms, progression, cognition, and sleep disturbances.
AI-driven technologies for continuous cardiac monitoring, abnormality detection, and clinical decision-making.
Machine learning applied to sleep data to detect and classify sleep disorders and related patterns.
Platforms that transform complex biomedical signals into clinical insights for providers and researchers.
Digital tools that support health education, awareness, and engagement for communities.
Sensors and device systems designed to record biomedical signals reliably across diverse bodies and real-world settings.
AI and machine learning models for prediction, classification, and pattern recognition, validated for the populations they are meant to serve.
Software and communication tools that turn complex biomedical data into insights clinicians, patients, and communities can actually use.
EEG · ECG · Sleep
Movement · Respiratory
Acoustic
Prediction · Classification
Anomaly detection
Progression modeling
Community engagement
Women's health
Digital health equity
| Year | Title | Venue |
|---|---|---|
| 2026 | Extraction and interpretation of EEG features for diagnosis and severity prediction of Alzheimer's Disease and Frontotemporal dementia using deep learning | Biomedical Signal Processing and Control |
Dr. Rim Chaif is a multilingual health AI researcher specializing in computational analysis of patient communication, with particular focus on women's health and underrepresented populations. She holds a Ph.D. in Journalism and Mass Communications from the University of Kansas. At Vital Signals Systems, she leads research on AI-driven health communication and community-facing applications of biomedical insight.
Dr. Ali Ibrahim is a biomedical engineer and machine learning researcher with a decade of published work in signal processing and deep learning across neurological, cardiovascular, and oncology applications. He holds a Ph.D. in Electrical Engineering and is currently Assistant Professor at Florida Atlantic University. At Vital Signals Systems, he leads the development of sensor architectures and AI systems that translate biomedical signals into clinical insight.
Identify objective biomarkers, accelerate endpoint detection, characterize patient populations, and improve the precision of clinical decision-making.
Strengthen monitoring capabilities, integrate validated signal analytics, and accelerate product development with rigorous AI methodology.
Expand health literacy, support patient engagement, and translate clinical insights into accessible information for diverse populations.
Co-founder Dr. Ali Ibrahim's recent paper on deep learning extraction and interpretation of EEG features for Alzheimer's disease and frontotemporal dementia diagnosis was published in Biomedical Signal Processing and Control.
Founder Dr. Rim Chaif's research examines how gender shapes the way people find, share, and receive health information in health communities.
We are always interested in hearing from researchers, engineers, clinicians, designers, and health communication specialists who share our mission.
We may not have active openings at all times, but we welcome introductions from people interested in biomedical signal processing, health AI, digital health, and community health communication.
We are also building an advisory network in biomedical engineering, clinical medicine, and health communication. Introductions welcome.
We welcome research, clinical, nonprofit, and industry collaborations across biomedical signal processing, health AI, and equitable digital health.