Diagnosis of Sleep Apnea from ECG Signals | Chapter 5 | Research Highlights in Science and Technology Vol. 9

This project aims to evolve an automated vehicle-learning algorithm that can correctly identify sleep interruption of activity in individuals by analyzing their electrocardiogram (ECG) signals. By attracting on the irregular respiring patterns that affect ECG signals, we aim to determine a more accessible and less burdensome alternative to moment of truth-consuming and high-priced polysomnography method currently secondhand for sleep apnea disease. To achieve this, the ECG signals will meet with a series of preprocessing steps, including the elimination of high-commonness noises to a degree electromyogram noise, additive silvery Gaussian noise, and power cable interference. Once the signals are filtered, mathematical features will be derived from them. These visage capture relevant information about the ECG signal's traits and patterns. The extracted mathematical features will then be handled for classification purposes. Various classifiers will acquire information using the principles of these features, enabling the model to determine the distinguishing patterns 'tween normal ECG signals and those associated with sleep interruption of activity. By training the invention on labeled datasets, it will expand the ability to categorize succeeding ECG signals as either indicative of sleep interruption of activity or normal ECG signals.

Author(s) Details:

Jagannatha K. B.,
Department of Electronics and Communication, BMS Institute of Technology and Management, Bengaluru-64, India.

Sabina Raman,
Department of Electronics and Communication, BMS Institute of Technology and Management, Bengaluru-64, India.

P. J. Kusuma,
Department of Electronics and Communication, BMS Institute of Technology and Management, Bengaluru-64, India.

B. Likitha,
Department of Electronics and Communication, BMS Institute of Technology and Management, Bengaluru-64, India.

A. H. Namrata,
Department of Electronics and Communication, BMS Institute of Technology and Management, Bengaluru-64, India.

Shreya Pattan,
Department of Electronics and Communication, BMS Institute of Technology and Management, Bengaluru-64, India.

Please see the link here: https://stm.bookpi.org/RHST-V9/article/view/11635