Abstract
Sleep apnea syndrome (SAS) is a prevalent sleep disorder well-known nowadays. People with SAS cease breathing intermittently in sleeping and an episode without breathing is called a sleep apnea event. SAS often deteriorates life quality by frequent nocturnal awakenings, morning headache, excessive daytime sleepiness, and attention deficiency. Unfortunately, the suffering people are usually unconscious of it and diagnosis nowadays relies on expensive and labor-intensive Polysomnography (PSG). This thesis proposed a sleep apnea event detection algorithm which detects sleep apnea events based on solely thoracic (THO) and abdominal (ABD) movement signals during sleep. The algorithm is developed in two stages. In the first stage, the THO and ABD movement signals are segmented by 2-second and 5-second time window and then four features, cross-correlation, fundamental frequency, and maximum THO and ABD amplitude values, are extracted from the THO and ABD segments. Then, a state machine is designed to use the calibration signal information and the features to count the number of sleep apnea events in overnight THO and ABD movement signals. Hardware architecture is also built up in this stage. Based on the structure built in stage one, in stage two adaptive non-harmonic model is used to quantify the THO and ABD signals, more features are included, and support vector machine is introduced to construct classifiers for the state machine. To assess the event-by-event detection accuracy, two indexes, I and S, are proposed to evaluates the performance of the proposed sleep apnea event detection algorithm.