Südwestdeutscher Verlag für Hochschulschriften Sleep related Breathing Disorders and Sleep Stages from ECG Signals

This book introduces a physiologically driven approach to detect sleep related breathing disorder events as well as different sleep stages by evaluating an ECG signal. The introductory chapters provide medical background knowledge concerning this sleep induced breathing failure and the ECG. On this basis, the following chapter discloses the connection between features extracted from the ECG and the occurrence of sleep related breathing disorder events or sleep stages. The features are derived from the heart rate and the morphological side effects of cardiac load fluctuations. In the subsequent chapters, the extracted features are evaluated by applying pattern recognition methods to answer the central question: Is it possible to reduce the diagnostic complexity of this disease by simply recording and processing an ECG signal? The assessment of the presented approach starts by selecting the most suitable classifiers. Subsequently, these classifiers are put to tests in patient dependent and independent scenarios. The achieved classification rates for the sleep related breathing disorder episodes as well as the sleep stages prove that an ECG driven basic diagnosis is feasible. | This book introduces a physiologically driven approach to detect sleep related breathing disorder events as well as different sleep stages by evaluating an ECG signal. The introductory chapters provide medical background knowledge concerning this sleep induced breathing failure and the ECG. On this basis, the following chapter discloses the connection between features extracted from the ECG and the occurrence of sleep related breathing disorder events or sleep stages. The features are derived from the heart rate and the morphological side effects of cardiac load fluctuations. In the subsequent chapters, the extracted features are evaluated by applying pattern recognition methods to answer the central question: Is it possible to reduce the diagnostic complexity of this disease by simply recording and processing an ECG signal? The assessment of the presented approach starts by selecting the most suitable classifiers. Subsequently, these classifiers are put to tests in patient dependent and independent scenarios. The achieved classification rates for the sleep related breathing disorder episodes as well as the sleep stages prove that an ECG driven basic diagnosis is feasible.

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