Z3Pulse

Z3Pulse is a technology platform to capture sleep biomarkers. We track and analyze your vitals like heartbeats, respiration, temperature, and movements up to a thousand times every second while you sleep to extract incredible insights into your physical and mental wellbeing. We use sleep as a portal to understand your present and future health and provide specific actions to improve it.

The data collected is processed by Neurobit’s proprietary AI which is backed by decades of research and is trained on trillions of health data points allowing it to understand you both in reference to the general population as well as “you” as a unique person. We strive to continuously add new insights and measurements backed by research and clinical data to better understand yourself and help you and your family lead a healthier and happier life.

The Z3Pulse platform is:

- Clinically validated*

- Device & Signal Agnostic

- Personalized report with AI-driven actionable insights

- Highly detailed sleep biomarker report spanning sleep, respiration, and cardiac health. New measurements will be added continuously.

- Raw data includes hypnograms, overnight heart rate, respiratory obstructions.

The Z3Pulse platform is fully HIPAA compliant and is designed to fit into many different use cases:

- Consumer Health

- Clinical Trials

- Outcome-Based Systems

- Telehealth

- Academic Research

- Population Health

- Lab Testing Platform

- Remote Monitoring

DISCLAIMER:

The Z3Pulse APP provides you with the analysis of the data collected through the Z3Pulse device or a third-party monitor. The information presented within the APP or associated report is not intended to diagnose, treat, cure or prevent any disease. All information presented within the APP and the reports are not meant as a substitute for or alternative to information from healthcare practitioners. You may use it as a starting point for any conversation you may have with your doctor.

Clinical Validations*:

Pini, N., Ong, J. L., Yilmaz, G., Chee, N. I., Siting, Z., Awasthi, A., ... & Lucchini, M. (2021). An automated heart rate-based algorithm for sleep stage classification: validation using conventional PSG and innovative wearable ECG device. medRxiv.

Chen, Y. J., Siting, Z., Kishan, K., & Patanaik, A. (2021). Instantaneous Heart Rate-based sleep staging using deep learning models as a convenient alternative to Polysomnography.

Siting, Z., Chen, Y. J., Kishan, K., & Patanaik, A. (2021). Automated sleep apnea detection from instantaneous heart rate using deep learning models.