Real-time sleep apnea detection by classifier combination.

To find an efficient and valid alternative of polysomnography (PSG), this paper investigates real-time sleep apnea and hypopnea syndrome (SAHS) detection based on electrocardiograph (ECG) and saturation of peripheral oxygen (SpO(2)) signals, individually and in combination. We include ten machine-learning algorithms in our classification experiment. It is shown that our proposed SpO (2) features outperform the ECG features in terms of diagnostic ability. More importantly, we propose classifier combination to further enhance the classification performance by harnessing the complementary information provided by individual classifiers. With our selected SpO(2) and ECG features, the classifier combination using AdaBoost with Decision Stump, Bagging with REPTree, and either kNN or Decision Table achieves sensitivity, specificity, and accuracy all around 82% for a minute-based real-time SAHS detection over 25 sleep-disordered-breathing suspects' full overnight recordings.

Apnea MedAssist: real-time sleep apnea monitor using single-lead ECG.

We have developed a low-cost, real-time sleep apnea monitoring system ''Apnea MedAssist" for recognizing obstructive sleep apnea episodes with a high degree of accuracy for both home and clinical care applications. The fully automated system uses patient's single channel nocturnal ECG to extract feature sets, and uses the support vector classifier (SVC) to detect apnea episodes. "Apnea MedAssist" is implemented on Android operating system (OS) based smartphones, uses either the general adult subject-independent SVC model or subject-dependent SVC model, and achieves a classification F-measure of 90% and a sensitivity of 96% for the subject-independent SVC. The real-time capability comes from the use of 1-min segments of ECG epochs for feature extraction and classification. The reduced complexity of "Apnea MedAssist" comes from efficient optimization of the ECG processing, and use of techniques to reduce SVC model complexity by reducing the dimension of feature set from ECG and ECG-derived respiration signals and by reducing the number of support vectors.

Real-time sleep quality assessment using single-lead ECG and multi-stage SVM classifier.

Sleep efficiency measures provide an objective assessment to gauge the quality of individual's sleep. In this study we present a home-based, automated and non-intrusive system that is based on Electrocardiogram (ECG) measurements and uses a multi-stage Support Vector Machines (SVM) classifier to measure three indices for sleep quality assessment per 30 s epoch segment: Sleep Efficiency Index, Delta-Sleep Efficiency Index and Sleep Onset Latency. This method provides an alternative to the intrusive and expensive Polysomnography (PSG) and scoring by Rechtschaffen and Kales visual method.

A patient-adaptive profiling scheme for ECG beat classification.

Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ an efficient wavelet-based beat-detection mechanism to extract precise fiducial ECG points. Then, we implement a novel local ECG beat classifier to profile each patient's normal cardiac behavior. ECG morphologies vary from person to person and even for each person, it can vary over time depending on the person's physical condition and/or environment. Having such profile is essential for various diagnosis (e.g., arrhythmia) purposes. One application of such profiling scheme is to automatically raise an early warning flag for the abnormal cardiac behavior of any individual. Our extensive experimental results on the MIT-BIH arrhythmia database show that our technique can detect the beats with 99.59% accuracy and can identify abnormalities with a high classification accuracy of 97.42%.