Diagnosis of Sleep Apnea from ECG Signals

This project aims to develop an automated machine-learning algorithm that can accurately identify sleep apnea in individuals by analyzing their electrocardiogram (ECG) signals. By focusing on the irregular breathing patterns that affect ECG signals, we aim to provide a more accessible and less burdensome alternative to the time-consuming and expensive polysomnography method currently used for sleep apnea diagnosis. To achieve this, the ECG signals will undergo a series of preprocessing steps, including the removal of high-frequency noises such as electromyogram noise, additive white Gaussian noise, and power line interference. Once the signals are filtered, statistical features will be extracted from them. These features capture relevant information about the ECG signal's characteristics and patterns. The extracted statistical features will then be utilized for classification purposes. Various classifiers will be trained using the values of these features, enabling the model to learn the distinguishing patterns between normal ECG signals and those associated with sleep apnea. By training the algorithm on labeled datasets, it will develop the ability to categorize incoming ECG signals as either indicative of sleep apnea or normal ECG signals.