<span>Well-being sleep is a significant segment for maintaining mental comparably as genuine flourishing. More than six-hour recordings are required to distinguish sleep apnea, which are extremely long duration recordings. It's difficult for a human to deduce the problem from electrocardiogram (ECG) readings. As a result, automated PC-based assessment is expected to detect <span>abnormalities as early as possible. An automated framework for the classification of obstructive sleep apnea (OSA) can moreover be distinguished from the ECG Signals. From the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) polysomnographic informational collection, 18 subjects have been considered as data signals. The signal is segmented into 30 seconds and features are extracted by using the discrete wavelet transform (DWT). DWT of seven-level decomposition is applied on the segmented signal by using</span> the wavelet 'sym3'. 12 features were extracted from each level and all of them are used to categorize the five types of sleep apnea. Random forest, k-nearest neighbor (KNN), and support vector machine (SVM) are used for classification of apnea. The random forest (RF) classifier outperformed the others with an average of accuracy (Acc) of 98.53% according to the study's findings. The experimental results show the developed model outperforms the state of art algorithms in the literature.</span>

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The demand of ventilators has been increasing dramatically from the past few years due to the spike in the COVID-19 cases globally. Around the World, the abscence of availability of ventilators have taken a lot of lives in just the past couple of years. The use of ventilators has been proven to be helpful from preventing the danger of lung harm through low- quantity airflow and helps us to get the adequate amount of influx of pure air. The ventilators available are expensive and scarce in supply. They are heavy and would normally weigh around 7 to 8 kgs, which makes it inconvenient to carry from place to place due to its enormous size. Our project aims at developing a smart ventilator system using a microcontroller board and sensors based on Internet of Things (IOT). The smart ventilator will be portable and very light in weight, which makes it handy to use and requires no additional expertise to handle it. The usage of the high torque motor enables us to change the pressure as per the requirement. The sensors used collects the temperature and the Pulse oximetry levels and the same is updated on the LCD display.