Time-frequency (TF) signal analysis and processing techniques provide adequate tools to investigate non-stationary signals such as electroencephalogram (EEG). Nonetheless, the body of TF signal analysis and EEG processing holds literature gaps that mandate remedies to ensure successful adoption. Besides, the gravitas of epileptic seizures in newborns invites further efforts to understand its EEG manifestation, spatial characteristics, and non-stationary behavior. In this thesis, we hypothesize that multi-channel non-stationary signal applications must utilize the piece-wise spline Wigner-Ville distribution (PW-WVD) to design or select best performing TF signal processing techniques and incorporate inter-sensor awareness to comprehend spatiotemporal systems. Specifically, the thesis delivers the following contributions: (1) introducing the PW-WVD as an optimal TF distribution (TFD) and proposing new TFD selection strategies; (2) designing a process and deriving novel accuracy and resolution measures to evaluate the TFD performance; (3) offering new feature domains to extract meaningful information from multi-channel EEG recordings; (4) developing a multi-sensor newborn EEG model that takes into account its temporal, spectral, and spatial characteristics; and (5) proposing an adaptive multi-user multiple-modality signal compression paradigm based on deep learning techniques.
In this thesis, we divide the stated hypothesis into two parts; the TF prospect and the EEG aspect. First, we present the proposed optimal PW-WVD along with the new TFD performance evaluation process and measures. We confirm the PW-WVD optimality for arbitrary multi-component non-stationary signals with non-linear frequency and amplitude laws. Besides, we demonstrate the proposed process ability to quantify the TFD accuracy and resolution separately, identify the performance relative gain/loss among different TFDs, and its adequacy for signals with arbitrary parameters. Afterward, we introduce and validate the developed multi-sensor newborn EEG model. We report various comparisons that verify the model's ability to mimic real normal and seizure EEG patterns. Finally, we describe the deep learning-based adaptive multiple-modality signal compression scheme and illustrate its advantages in a multi-user mobile-health setup.
The thesis findings shed the light on a fundamental flaw in the design process of computationally expensive TFDs; they do not maximize both the TFD accuracy and resolution. Moreover, the EEG electrical manifestation is confined to some set of electrodes; hence, channel selection is paramount and inter-sensor understanding can improve the efficacy of EEG applications. The evidence from this dissertation appears to support the candidate's hypothesis and assert the contributions' role and significance to the body of knowledge. In addition, it reveals new research questions in need of exploration and invites further investigation
