Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Obstructive Sleep Apnea (OSA) is one of the main sleep disorders, but only 10% of the cases are diagnosed. Moreover, there is a lack of tools for long-term monitoring of OSA, since current systems are too bulky and intrusive to be used continuously. In this context, recent studies have shown that it is possible to detect it automatically based on single- lead ECG recordings. This approach can be used in non-invasive smart arable sensors which measure and process bio-signals online. This work focuses on the implementation, optimization and integration of an algorithm for OSA detection for preventive health-care. It relies on a frequency-domain analysis while tar- geting an ultra-low por embedded arable device. As it must share its resources usage with other computations, it must be as lightight as possible. Our current results based on publicly available signals show a classification accuracy of up to 83.2% for both the offline analysis and the embedded online one. This system gives an even better classification accuracy than the best offline algorithm when using the same features for classification
['Gregoire Surrel', 'Francisco J. Rincón', 'Srinivasan Murali', 'David Atienza']
I am writing a research paper on a idea Low-Power Wearable System for Real-Time Screening of Obstructive Sleep Apnea
[{"from": "human", "value": "I am writing a research paper on a idea Low-Power Wearable System for Real-Time Screening of Obstructive Sleep Apnea"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Obstructive Sleep Apnea (OSA) is one of the main sleep disorders, but only 10% of the cases are diagnosed. Moreover, there is a lack of tools for long-term monitoring of OSA, since current systems are too bulky and intrusive to be used continuously. In this context, recent studies have shown that it is possible to detect it automatically based on single- lead ECG recordings. This approach can be used in non-invasive smart arable sensors which measure and process bio-signals online. This work focuses on the implementation, optimization and integration of an algorithm for OSA detection for preventive health-care. It relies on a frequency-domain analysis while tar- geting an ultra-low por embedded arable device. As it must share its resources usage with other computations, it must be as lightight as possible. Our current results based on publicly available signals show a classification accuracy of up to 83.2% for both the offline analysis and the embedded online one. This system gives an even better classification accuracy than the best offline algorithm when using the same features for classification"}]