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Anxiety Alert: Stay in Control of Your Stress Levels Anywhere, Anytime!
We want to analyze biomedical signals collected from subjects with induced anxiety using a portable device. We aim to detect instantaneous anxiety and mood changes via portable devices. The data were already collected and ready to be analyzed. The data description is here: https://www.mdpi.com/2306-5729/7/9/132
Keywords: Stress detection, biomedical signal analysis, data science, medical technologies, and digital health
This study aims to effectively detect anxiety and monitor its progress in real-time in an attempt to help boost mental performance and improve brain status. This will be accomplished by investigating the feasibility of using an electrocardiogram (ECG) and respiration signals for tracking anxiety levels. Accurate detection of anxiety using biosignals such as ECG and respiration signals is challenging. Usually, a psychologist carries out the assessment via a face-to-face interview and by answering a few written questions. It would be desirable to build an affordable, automated, lightweight wearable device that can monitor anxiety in any clinical setting and ultimately at home by an individual.
This study aims to effectively detect anxiety and monitor its progress in real-time in an attempt to help boost mental performance and improve brain status. This will be accomplished by investigating the feasibility of using an electrocardiogram (ECG) and respiration signals for tracking anxiety levels. Accurate detection of anxiety using biosignals such as ECG and respiration signals is challenging. Usually, a psychologist carries out the assessment via a face-to-face interview and by answering a few written questions. It would be desirable to build an affordable, automated, lightweight wearable device that can monitor anxiety in any clinical setting and ultimately at home by an individual.
- Segmentation of biomedical signals based on anxiety levels
- Identify patterns in biomedical signals that are correlated with anxiety
- Provide statistical analysis of patterns associated with and without anxiety
- Visualize results
- If possible: develop a model to differentiate between subjects with and without anxiety.
**Tasks**
- Literature review (10%)
- Data analysis (loading data, extracting segments, and basic statistical analysis) (40%)
- Design and implement a basic machine learning algorithm (20%)
- Test and evaluation (10%)
- Report and presentation (20%)
**Your Profile**
- Background in Electrical Engineering, Biomedical Engineering, Computer Science, Software Engineering, or related fields Prior experience with programming (MATLAB or Python)
- Able to work independently, pay attention to detail, and deliver results remotely
- Can visualize data using different charts such as boxplot and scatter plots
- Aware of data filtering, data segmentation, and data manipulation techniques
- Developed linear regression and classification models
- Segmentation of biomedical signals based on anxiety levels - Identify patterns in biomedical signals that are correlated with anxiety - Provide statistical analysis of patterns associated with and without anxiety - Visualize results - If possible: develop a model to differentiate between subjects with and without anxiety.
**Tasks**
- Literature review (10%) - Data analysis (loading data, extracting segments, and basic statistical analysis) (40%) - Design and implement a basic machine learning algorithm (20%) - Test and evaluation (10%) - Report and presentation (20%)
**Your Profile**
- Background in Electrical Engineering, Biomedical Engineering, Computer Science, Software Engineering, or related fields Prior experience with programming (MATLAB or Python) - Able to work independently, pay attention to detail, and deliver results remotely - Can visualize data using different charts such as boxplot and scatter plots - Aware of data filtering, data segmentation, and data manipulation techniques - Developed linear regression and classification models
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 Researchgate: https://www.researchgate.net/profile/Mohamed-Elgendi
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 Researchgate: https://www.researchgate.net/profile/Mohamed-Elgendi
