It demonstrates 95.8% accuracy for defining severe OSAS
TAICHUNG, March 30, 2024 /PRNewswire/ — Snoring can be an alarming sign of obstructive sleep apnea syndrome (OSAS). Studies showed that 50% of individuals who snore may experience sleep apnea, which can lead to life-threatening conditions such as heart disease, hypertension, intracerebral hemorrhage, stroke, and even sudden death during night-time in severe cases. Sleep Medicine Center at China Medical University Hospital (CMUH, Taiwan) has introduced iDREAM (Intelligent Detection of Respiratory Events through Automated Monitoring), incorporating Quanta’s QOCA Portable ECG Monitoring Device, as a simple solution to efficiently detect symptoms of apnea patients during their sleep at home. More than 100 patients have been in clinical trials of iDREAM. With this AI-powered ECG analysis, CMUH’s physicians are able to identify obstructive sleep apnea syndrome (OSAS) more accurately and to reduce the time spent for diagnosis and treatment.
Dr. Yung-An Tsou, Director of Laryngology Division at CMUH, stated that, according to a survey by Asian Society of Sleep Medicine, over 20% of the population in Taiwan (approximately 5 million people) suffer from sleep disorders. However, the actual number of people willing to seek medical treatment is much lower than this figure.
Mr. Chang, 36, snored loudly while sleeping and was unable to sustain undisrupted sleep for himself and his family. Preliminary examinations by CMUH’s Otorhinolaryngology expert team indicated that Mr. Chang required a sleep exam. Interpreting the report, Director Tsou discovered that Mr. Chang had OSAS. Mr. Chang was given an uvulopalatopharyngoplasty (UPPP) and follow-up monitored with iDREAM. With this easy-to-carry device, records were sent to the Cloud server and ECG data was swiftly analyzed with AI model driven by big data. According to CMUH, iDREAM serves as continuous monitoring at home, which results in greatly reducing scheduling issues at the Sleep Medicine Center and making it easier to keep track of outcomes. Mr. Chang’s sleep apnea was relieved and his sleep quality improved considerably.
Dr. Liang-Wen Hang, Chief of CMUH Sleep Medicine Center, said, since the capacity of CMUH’s Sleep Medicine Center is always limited, the need of the patients staying overnight, however, are increasing. At the Sleep Medicine Center, patients are traditionally required with several physiological sensors (for electroencephalography [EEG], chin electromyogram, airflow, oxygen saturation, ECG or heart rate). Those cables make patients anxious and sleep onset less likely, often affecting accuracy of patients’ reports. More, the Sleep Medicine Center has its own manpower issues, as sleep technicians are required to keep watch of recorded parameters and interpret data overnight. Thanks to iDREAM, the issues mentioned above are resolved, creating a more comprehensive and convenient tracking system for OSAS patients.
iDREAM , the home-based sleep detection system, detects ECG change from OSAS episodes and determines severity using its deep learning methodology. It demonstrates 92.7% and 93.2% accuracy for sleep apnea and wake-up events (interruption of sleep from apnea), and 95.8% accuracy for defining severe OSAS (30 episodes per hour). iDREAM is still undergoing clinical testing but plans are in place to file for Taiwan Food and Drug Administration smart medical device certification and then formal introduce it into clinical practice. It is expected to be impactful in sleep medicine.
Dedicated to the application of clinical information, AI Center at CMUH trains artificial neural networks using the Hospital’s medical data. Such models can provide suggestions in clinical diagnostics and may be combined with clinical decision-making systems to lessen the burden on healthcare personnel. CMUH AI Center has already obtained more than a dozen certifications for intelligent medical materials from Taiwan Food and Drug Administration. It will continue to research and innovate, bringing more anticipated breakthroughs and evolutions in the field of smart healthcare.
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SOURCE China Medical University Hospital
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