AHI estimation from sleep stages, breathing rate features, movement data
and STOP-BANG questionnaire responses using ML techniques

Apnea-hypopnea index (AHI) is a clinical parameter used to diagnose sleep apnea, a breathing disorder. The project would involve deriving features from various other data points which are not directly involved in AHI calculation, however can lead to a decent estimate of AHI. Also the project would require intense research on what data needs to be considered as well as which features can be derived from the datasets.

Responsibilities – Understand and study various data sets in order to obtain relevant clinical parameters that can be used to predict diseases.

Qualifications – Preferred bachelors /masters students from relevant field

Key skills –

Data science and Machine
Learning knowledge
Knowledge of Python / Pandas

Start date – September 2020

Automating digital signal processing of the data collected by
Sleepiz device and implementing on cloud infrastructure

The raw date collected by the Sleepiz device is sent and stored on a database hosted on the cloud. The project would involve implementing the in-house digital signal processing algorithm on a cloud infrastructure to generate the processed results in an automated way that can be displayed on the Sleepiz Webapp.

Responsibilities – To understand Sleepiz’s digital signal processing processes implemented on the cloud.

Qualifications– Preferred bachelors /masters students from relevant field.

Key skills –

Cloud infrastructure, IoT, Big data
Knowledge of Matlab and Python

Start date – September 2020

Sleepiz AG was founded in the beginning of 2018 by three graduates of ETH Zurich and one graduate of the University of St. Gallen with the aim of providing revolutionary technology to the medical community.