SECC: simultaneous extraction of context and community from pervasive signals

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated.

Bayesian nonparametric learning of contexts and activities from pervasive signals

This thesis develops machine learning techniques to discover activities and contexts from pervasive sensor data. These techniques are especially suitable for streaming sensor data as they can infer the context space automatically. They are applicable in many real world applications such as activity monitoring or organization management.

Distribution entropy analysis of epileptic EEG signals

It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p <; 0.05) increased DistEn for the interictal class in compassion with the normal class, whereas both analyses using relatively long EEG signals failed in tracking this difference between them, which may be due to a nonstationarity effect on entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

Robust time-spread echo watermarking using characteristics of host signals

A robust audio watermarking method based on the time-spread (TS) echo hiding scheme is proposed. Compared with existing TS watermarking methods, the approach is more robust as it exploits the characteristics of host signals in the encoding stage. Theoretical analysis and simulation examples demonstrate the effectiveness and advantages of the method.

A simultaneous extraction of context and community from pervasive signals using nested dirichlet process

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain human dynamics or behaviors and then use them as a way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated.

Investigating relative respiratory effort signals during mixed sleep apnea using photoplethysmogram

Sleep disordered breathing does show different types of events. These are obstructive apnea events, central apnea events and mixed sleep apnea (MSA) which have a central component with a pause in airflow without respiratory effort followed by an obstructive component with respiratory effort. The esophageal pressure (Pes) is the accurate method to assess respiratory effort. The aim of the present study is to investigate whether the features extracted from photo-plethysmogram (PPG) could relate with the changes in Pes during MSA. Therefore, Pes and PPG signals during 65 pre-scored MSA events and 10 s preceding the events were collected from 8 patients. Pulse intervals (PPI), Pulse wave amplitudes (PWA) and wavelet decomposition (Wv) of PPG signals at level 8 (0.15-0.32 Hz) were derived from PPG signals. Results show that significant correlations (r = 0.63, p < 0.01; r = 0.42, p < 0.05; r = 0.8, p < 0.01 for OSA part) were found between reductions in Pes and that in PPG based surrogate respiratory signals PPI, PWA and Wv. Results suggest that PPG based relative respiratory effort signal can be considered as an alternative to Pes as a means of measuring changes in inspiratory effort when scoring OSA and CSA parts of MSA events.