Updating Deep Learning Algorithms for OSA Monitoring
- Conditions
- Obstructive Sleep Apnea-hypopneaObstructive Sleep Apnea
- Interventions
- Device: CART-I plusDevice: Polysomnography
- Registration Number
- NCT06522815
- Lead Sponsor
- Sky Labs
- Brief Summary
The objective is to enhance the reliability of the algorithm to match that of Level 1 polysomnography by leveraging the diverse data obtained from Level 1 polysomnography to refine the deep learning algorithm.
- Detailed Description
Patients undergoing Level 1 polysomnography are equipped with the CART-I PLUS device, for collecting polysomnography data alongside concurrent photoplethysmography (PPG) signals.
The collected data is categorized into apnea, hypopnea, and normal segments based on the polysomnography results. Utilizing the PPG and accelerometer (ACC) signals from the CART-I PLUS, metrics such as SaO2 (oxygen saturation), respiratory rate, heart rate (HR), heart rate variability (HRV), and body movement are calculated for each segment. These metrics, along with the PPG and ACC signals, are then used to develop a deep learning model that classifies the segments into apnea, hypopnea, or normal.
Participants are divided into training and validation sets. The deep learning model is trained on data from the participants in the training set, and its performance is evaluated using the validation set.
The algorithm is constructed using convolutional neural networks (CNN), recurrent neural networks (RNN), attention mechanisms, and other advanced techniques recognized for their efficacy in classification tasks, specifically for identifying apnea, hypopnea, and normal segments.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- Female
- Target Recruitment
- 107
Patients scheduled for Level 1 polysomnography at a sleep center who meet all of the following criteria:
- Aged 19 years or older
- Have listened to and understood a thorough explanation of the clinical study and voluntarily agreed to participate
- Under 19 years of age
- Unable to collect normal signals during the pre-test or wearing of the CART-I PLUS device
- Refuse to participate in the clinical study
- Have cognitive impairments to the extent that they cannot understand the explanation of the clinical study and therefore cannot make a voluntary decision to participate (e.g., legally incompetent individuals)
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Arm && Interventions
Group Intervention Description Level 1 polysomnography patient CART-I plus For patients undergoing Level 1 polysomnography, simultaneous collection of photoplethysmography (PPG) signals and polysomnography data is performed using the CART-I PLUS device. Level 1 polysomnography patient Polysomnography For patients undergoing Level 1 polysomnography, simultaneous collection of photoplethysmography (PPG) signals and polysomnography data is performed using the CART-I PLUS device.
- Primary Outcome Measures
Name Time Method Accuracy of the algorithm and the 95% confidence interval 11 hours Present the accuracy of the algorithm and the 95% confidence interval. If the lower bound of the 95% confidence interval exceeds a minimum accuracy of 0.85, it is considered clinically significant.
- Secondary Outcome Measures
Name Time Method Accuracy and 95% confidence intervals for each interval 11 hours Present the accuracy and 95% confidence intervals for each interval. Additionally, precision, recall, ROC curve, and AUC may be presented. The performance comparison between algorithms will use the bootstrap method, and a p-value less than 0.05 will be considered statistically significant.
Trial Locations
- Locations (1)
Gangnam Severance Hospital
🇰🇷Seoul, Korea, Republic of