ABSTRACT
This study proposes a unifying framework for m-Health video communication systems that provides for the joint optimization of video quality, bitrate demands, and encoding time. The framework is video modality and infrastructure independent and facilitates adaptation to the best available encoding mode that satisfies underlying technology and application imposed constraints. The scalability of the proposed algorithm is demonstrated using different HEVC encoding configurations and realistic modelling of 802.11× wireless infrastructure for emergency scenery and response videos. Extensive experimentation shows that a jointly optimal solution in the encoding time, bitrate, and video quality space is feasible.
Subject(s)
Disasters , Emergency Medicine/methods , Video Recording/methods , Wireless Technology , Algorithms , Computer Communication Networks , Decision Support Techniques , Humans , Telemedicine/instrumentation , Wireless Technology/instrumentationABSTRACT
Advances in video compression, network technologies, and computer technologies have contributed to the rapid growth of mobile health (m-health) systems and services. Wide deployment of such systems and services is expected in the near future, and it's foreseen that they will soon be incorporated in daily clinical practice. This study focuses in describing the basic components of an end-to-end wireless medical video telemedicine system, providing a brief overview of the recent advances in the field, while it also highlights future trends in the design of telemedicine systems that are diagnostically driven.
Subject(s)
Cell Phone , Computer Communication Networks , Telemedicine/methods , Telemetry/methods , User-Computer Interface , Video Recording/methods , SpainABSTRACT
Directional information flow between coupled nonlinear systems is of practical interest in many areas like bioengineering, chemistry, physics and electrical engineering. Due to the high complexity and nonlinearity of the coupled chaotic systems, linear modeling approaches may fail to capture the proper dynamics and thus the proper directional information flow. This necessitates novel approaches to analyze signals derived from such systems. This paper proposes a novel approach for detecting such directional information flows between the subsystems involved. The dependability of the method is illustrated using coupled chaotic oscillators in various coupling configurations.
Subject(s)
Causality , Models, Biological , Nonlinear Dynamics , Systems Theory , Computer Simulation , Feedback , Stochastic ProcessesABSTRACT
This paper proposes a measure of complexity of the epileptic electroencephalogram (EEG) based on the dimensionality of the Karhunen-Loeve Transform (KLT) in the time domain. We estimate the KLT dimensionality by assuming the same observation noise level in the EEG during the interictal period (between the seizures) as the one during an epileptic seizure (ictal period). Utilizing an optimality criterion based on the T-index [1] and the predictability time, derived from the created KLT dimensionality profiles, we show that 10 out of 15 seizures in one patient with temporal lobe epilepsy were predictable with an average predictability time of about 36 minutes.
Subject(s)
Algorithms , Cerebral Cortex/physiopathology , Electroencephalography/methods , Epilepsy/diagnosis , Hippocampus/physiopathology , Brain/physiopathology , Brain Mapping/methods , Epilepsy/physiopathology , Humans , Linear Models , Models, Neurological , Monitoring, Ambulatory/methods , Quality Control , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
In this paper, a comparative study involving measures from the theory of chaos, namely the short-term largest Lyapunov exponent, Shannon and Kullback-Leibler entropies from information theory, has been carried out in terms of their predictability of temporal lobe epileptic seizures. These three measures are estimated from electroencephalographic (EEG) recordings with sub-dural and in-depth electrodes from various brain locations in patients with temporal lobe epilepsy. Techniques from optimization theory are applied to select optimal sets of electrodes whose dynamics is then followed over time. Results from analysis of multiple seizures in two epileptic patients with these measures are presented and compared in terms of their ability to identify pre-ictal dynamical entrainment well ahead of seizure onset time.
Subject(s)
Algorithms , Brain/physiopathology , Electroencephalography/methods , Epilepsy, Temporal Lobe/diagnosis , Models, Neurological , Brain Mapping/methods , Epilepsy/diagnosis , Epilepsy/physiopathology , Epilepsy, Temporal Lobe/physiopathology , Humans , Models, Statistical , Monitoring, Ambulatory/methods , Nonlinear Dynamics , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
In this paper, it is shown how an automatic recognition algorithm, based on hidden Markov models (HMM), can benefit by properly utilizing findings from perceptual experiments on nasals. Perceptual studies on nasal consonants have shown that both nasal murmurs and formant transitions are important in the identification of place of articulation. Thus both acoustic segments bordering the nasal release were incorporated into this HMM-based system. A 7% improvement in alveolar recognition was obtained by explicitly modeling the vowel-nasal transition segments. Further overall improvement (6%) was realized by making the HMM recognizer "focus" more on the vowel-nasal transition segments bordering the nasal release, and less on the nasal murmur and vowel portion of the /epsilon m/ and /epsilon n/ syllables. An overall average [m]-[n] recognition of 95% was obtained when testing this technique on 60 speakers outside the training set.
