Analysis of Heartbeat Signals to Detect Sleep Disorders Using Artificial Neural Network Methods

Abstract

A human sleep disorder detection system has been designed using an AD8232 Electrocardiogram sensor module integrated with a microcontroller and internet connection through ESP 32. The heartbeat signals from the sensor are analyzed using Artificial Neural Network (ANN) methods to determine normal conditions, Obstructive Sleep Apnea (OSA), or Central Sleep Apnea (CSA). The sensor's accuracy was measured over 10 measurements, resulting in 96.85%. Testing with 30 training data samples achieved an accuracy of 93.33%, and testing with 20 training data samples achieved an accuracy of 80%. The system displays output values through the Internet of Things (IoT) with an average computation time of around 7.6 ms.