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Identifying sleeping patterns in short- and long-sleepers using a wearable device
The project aims to develop an affordable wearable system that collects electroencephalograms (EEG) and electrocardiograms (ECG) to identify EEG and ECG features associated with short and long sleepers in a real-world environment.
Keywords: Wearables, sleep stages, brain waves, heart rate, breathing, and body and eye movements during sleep
The gold standard for measuring sleep characteristics is polysomnography (PSG), which is not convenient as at least 22 wires are needed during the data collection. On the other hand, wearables are easy to use, typically don't require wires, and could provide similar accuracy to the PSG. Moreover, the wearable can measure sleep efficiency, while PSG is usually measured in the clinic because of the complicated setting. This project aims to develop a wearable device that measures sleep onset, wake time, and total sleep duration based on EEG and ECG.
The gold standard for measuring sleep characteristics is polysomnography (PSG), which is not convenient as at least 22 wires are needed during the data collection. On the other hand, wearables are easy to use, typically don't require wires, and could provide similar accuracy to the PSG. Moreover, the wearable can measure sleep efficiency, while PSG is usually measured in the clinic because of the complicated setting. This project aims to develop a wearable device that measures sleep onset, wake time, and total sleep duration based on EEG and ECG.
- Use a wearable device to continuously monitor sleep and wake durations
- Potentially develop an app (a simple Android interface).
- Write a scientific project report.
**Tasks**
- Literature review (10%)
- Data collection and analysis (10%)
- Develop an automated filter to improve the EEG and ECG signal quality (10%)
- Develop a multi-class machine learning method to differentiate between long and short sleepers (40%)
- Provide feedback to the user on their sleep durations (20%)
- Reporting and presentation (10%)
**Your Profile**
- Background in Electrical Engineering, Biomedical Engineering, Mechanical Engineering, or related fields
- Experience with data analysis, signal processing, and machine learning (using Matlab or Python) is desirable.
- Independent worker with critical thinking and problem-solving skills.
- Use a wearable device to continuously monitor sleep and wake durations - Potentially develop an app (a simple Android interface). - Write a scientific project report.
**Tasks**
- Literature review (10%) - Data collection and analysis (10%) - Develop an automated filter to improve the EEG and ECG signal quality (10%) - Develop a multi-class machine learning method to differentiate between long and short sleepers (40%) - Provide feedback to the user on their sleep durations (20%) - Reporting and presentation (10%)
**Your Profile**
- Background in Electrical Engineering, Biomedical Engineering, Mechanical Engineering, or related fields - Experience with data analysis, signal processing, and machine learning (using Matlab or Python) is desirable. - Independent worker with critical thinking and problem-solving skills.
The research will be performed at ETH Zurich's Biomedical and Mobile Health Technology research group (www.bmht.ethz.ch) in the Balgrist Campus in Zurich, Switzerland. If you are interested and have questions regarding this project, please get in touch with Dr. Moe Elgendi (moe.elgendi@hest.ethz.ch).
For research-related topics, Google Scholar: https://scholar.google.com/citations?user=-WFwzjoAAAAJ&hl=en
The research will be performed at ETH Zurich's Biomedical and Mobile Health Technology research group (www.bmht.ethz.ch) in the Balgrist Campus in Zurich, Switzerland. If you are interested and have questions regarding this project, please get in touch with Dr. Moe Elgendi (moe.elgendi@hest.ethz.ch).
For research-related topics, Google Scholar: https://scholar.google.com/citations?user=-WFwzjoAAAAJ&hl=en
