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Data analysis: Detection of anxiety using non-invasive biosignals
We want to analyze biomedical signals collected from subjects with induced anxiety. We aim to detect instantaneous anxiety and mood changes via wearable devices.
Keywords: Stress detection, biomedical signal analysis, data science, medical technologies, and digital health
The purpose of this study is to effectively detect anxiety and monitor its progress, in an attempt to help boost mental performance and improve brain status. This will be accomplished by investigating the feasibility of using electrocardiogram (ECG) and respiration signals for tracking anxiety levels. Accurate detection of anxiety using biosignals such as ECG and respiration signals is challenging. Usually, the assessment is carried out by a psychologist 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 clinic setting, and ultimately at home by an individual.
The purpose of this study is to effectively detect anxiety and monitor its progress, in an attempt to help boost mental performance and improve brain status. This will be accomplished by investigating the feasibility of using electrocardiogram (ECG) and respiration signals for tracking anxiety levels. Accurate detection of anxiety using biosignals such as ECG and respiration signals is challenging. Usually, the assessment is carried out by a psychologist 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 clinic 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 Computer Science, Software Engineering, Information Systems 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 Computer Science, Software Engineering, Information Systems 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
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch) will supervise the student at the Biomedical and Mobile Health Technology Research Group in ETH Zurich’s D-HEST Department of Health Sciences and Technology.
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch) will supervise the student at the Biomedical and Mobile Health Technology Research Group in ETH Zurich’s D-HEST Department of Health Sciences and Technology.