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.

In this letter we consider burst switching for very high speed routing in the next generation Internet backbone. In this scenario, Internet protocol (IP) packets to a given destination are collected in bursts at the network edges. We propose a burst assembly mechanism that can reduce the traffic autocorrelation or degree of self-similarity, and at the same time keep the delay due to burst formation limited at the network edges.

We discuss the introduction/implementation of optical IP routers, then we introduce a novel scheduling algorithm incorporating void filling and aimed at optical routing of asynchronous, variable packet length packets. We describe its structure and discuss the complexity issues. Albeit introduced with the purpose of cancelling expensive optical synchronization, we argue that this approach represents the most viable all-optical approach for implementing packets-over-SONET (IP-centric scenario). We also present simulations under self-similar traffic conditions which point to the inefficiency of optical buffering to combat the effects of self-similarity, and we outline alternative strategies for proper buffer dimensioning.

In this paper, we propose an online QRS detector algorithm using Stationary Wavelet Transforms (SWT) for real time beat detection from single-lead electrocardiogram (ECG) signals. Daubechies 3 (†db3’) wavelet is chosen as the mother wavelet for SWT analysis. The information from the first ten seconds of the ECG signal is used as a learning template by the algorithm to initialize thresholds for beat detection. These thresholds are then modified every three seconds, thereby quickly adapting to changes in heart rate and signal quality. Hence false beat detections are vastly suppressed in this approach, while identifying true beats with a high degree of accuracy. Our algorithm yields a sensitivity (SE) of 99.88% and a positive predictive value (PPV) of 99.84% on the MIT-BIH Arrhythmia Database, SE of 99.80% and PPV of 99.91% on the AHA database and an SE of 99.97% and PPV of 99.90% on the QT database.

Pressure ulcer is an age-old problem imposing a huge cost to our health care system. Detecting and keeping record of the patient's posture on bed, help care givers reposition patient more efficiently and reduce the risk of developing pressure ulcer. In this paper, a commercial pressure mapping system is used to create a time-stamped, whole-body pressure map of the patient. An image-based processing algorithm is developed to keep an unobtrusive and informative record of patient's bed posture over time. The experimental results show that proposed algorithm can predict patient's bed posture with up to 97.7% average accuracy. This algorithm could ultimately be used with current support surface technologies to reduce the risk of ulcer development.