Assessing Anxiety Disorders Using Wearable Devices: Challenges and Future Directions
3. Results and Discussion
- Small sample sizes.
- Omission of discussion of confounding factors, such as psychiatric and medical co-morbidity.
- Limited information on subject medication intake status before running the study.
- Collection of ECG signals from different positions such as arm, chest, etc.
- Collection of ECG signals with different ECG devices such as Holter, portable, clinical setting, etc.
- Selection of ECG leads ranged from 1 lead to 12 leads.
- Sampling ECG signals varied from 100–1024 Hz, this may play a significant role in the extraction of frequency-based ECG features.
- Varying environments during the data collection that impacts emotions (e.g., subject laying down in the lab vs. going home).
4. Future Directions
- Collect multiple biosignals along with ECG;
- Ensure that all biosignals are collected at the same time without any delay (checking the time synchronization between all sensors is highly required);
- Collect biosignals using WDs along with an ECG monitor (preferably FDA approved device and calibrated once every two years) to validate results;
- Collect biosignals from a large number (>100) of study subjects; pure control, as well as pure AD (without no confounding factors) need to be used for study validation;
- collect biosignals from a diversity of subjects: young, old, female and males, different skin color and ethnicities;
- Collect data from subjects under both resting and movement conditions, to validate robustness, in case of developing a wearable device;
- investigate the correlation of other morphological ECG features (e.g., P wave, Q wave, R wave, S wave, T wave, and their changes in terms of amplitudes, slopes, intervals, energies, entropies, etc.) with AD;
- Collect a psychological questionnaire such as GAD-7 before/during/after running the study;
- Organize, if possible, a structured interview with a psychologist to validate results obtained from the previous two points;
- Produce publicly available physiological databases that contain time-synchronized biosignals. Although some public databases exist, such as Eight-emotion Sentics Data, MIT Affective Computing Group,  these data were collected from one subject targeting the eight emotional states: neutral, anger, hate, grief, love, romantic love, joy, and reverence;
- Encourage closer collaboration between the engineering community and clinical researchers who have access to patients and have experience designing and implementing the validation protocols that are necessary to move forward this type of work.
Conflicts of Interest
|GAD||Generalized anxiety disorder|
|MAD||Mixed anxiety disorder|
|PTSD||Post traumatic stress disorder|
|RR||R-peak to R-peak in ECG signal|
|RMSSD||Root mean square standard deviation|
|SAD||Social anxiety disorder|
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Elgendi, M.; Menon, C. Assessing Anxiety Disorders Using Wearable Devices: Challenges and Future Directions. Brain Sci. 2019, 9, 50. https://doi.org/10.3390/brainsci9030050
Elgendi M, Menon C. Assessing Anxiety Disorders Using Wearable Devices: Challenges and Future Directions. Brain Sciences. 2019; 9(3):50. https://doi.org/10.3390/brainsci9030050Chicago/Turabian Style
Elgendi, Mohamed, and Carlo Menon. 2019. "Assessing Anxiety Disorders Using Wearable Devices: Challenges and Future Directions" Brain Sciences 9, no. 3: 50. https://doi.org/10.3390/brainsci9030050