Abstract: A wireless body area sensor network (WBASN) is composed of a central node and heterogeneous sensors to supervise the physiological signals and functions of the human body. This overwhelmimg area has stimulated new research and calibration processes, especially in the area of WBASN’s attainment and fidelity. In the era of mobility or imbricated WBASN’s, system performance incomparably degrades because of unstable signal integrity. Hence, it is mandatory to define mitigation techniques in the design to avoid interference. There are various mitigation methods available e.g. diversity techniques, equalization, viterbi decoder etc. This paper presents equalization mitigation scheme in WBASNs to improve the signal integrity. Eye diagrams are also given to represent accuracy of the signal. Maximum no. of symbols is taken to authenticate the signal which in turn results in accuracy and increases the overall performance of the system.
Abstract: Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).
Abstract: An experiment to verify the relationships between
physiological indexes of an e-learner and the presence or absence of an
operation during e-learning is described. Electroencephalogram
(EEG), hemoencephalography (HEG), skin conductance (SC), and
blood volume pulse (BVP) values were measured while participants
performed experimental learning tasks. The results show that there are
significant differences between the SC values when reading with
clicking on learning materials and the SC values when reading without
clicking, and between the HEG ratio when reading (with and without
clicking) and the HEG ratio when resting for four of five participants.
We conclude that the SC signals can be used to estimate whether or not
a learner is performing an active task and that the HEG ratios can be
used to estimate whether a learner is learning.
Abstract: We present a refined multiscale Shannon entropy for
analyzing electroencephalogram (EEG), which reflects the underlying
dynamics of EEG over multiple scales. The rationale behind
this method is that neurological signals such as EEG possess
distinct dynamics over different spectral modes. To deal with the
nonlinear and nonstationary nature of EEG, the recently developed
empirical mode decomposition (EMD) is incorporated, allowing a
decomposition of EEG into its inherent spectral components, referred
to as intrinsic mode functions (IMFs). By calculating the Shannon
entropy of IMFs in a time-dependent manner and summing them over
adaptive multiple scales, it results in an adaptive subscale entropy
measure of EEG. Simulation and experimental results show that
the proposed entropy properly reveals the dynamical changes over
multiple scales.
Abstract: Nonstationary and nonlinear signals generated by living complex systems defy traditional mechanistic approaches, which are based on homeostasis. Previous our studies have shown that the evaluation of the interactions of physiological signals by using special analysis methods is suitable for observation of physiological processes. It is demonstrated the possibility of using deep physiological model, based on the interpretation of the changes of the human body’s functional states combined with an application of the analytical method based on matrix theory for the physiological signals analysis, which was applied on high risk cardiac patients. It is shown that evaluation of cardiac signals interactions show peculiar for each individual functional changes at the onset of hemodynamic restoration procedure. Therefore, we suggest that the alterations of functional state of the body, after patients overcome surgery can be complemented by the data received from the suggested approach of the evaluation of functional variables’ interactions.
Abstract: Emotions are related with learning processes and
physiological signals can be used to detect them for the
personalization of learning resources and to control the pace of
instruction. A model of relevant emotions has been developed, where
specific combinations of emotions and cognition processes are
connected and integrated with the concept of 'flow', in order to
improve learning. The cardiac pulse is a reliable signal that carries
useful information about the subject-s emotional condition; it is
detected using a classroom chair adapted with non invasive EMFi
sensor and an acquisition system that generates a ballistocardiogram
(BCG), the signal is processed by an algorithm to obtain
characteristics that match a specific emotional condition. The
complete chair system is presented in this work, along with a
framework for the personalization of learning resources.
Abstract: Avoiding learning failures in mathematics e-learning environments caused by emotional problems in students with autism has become an important topic for combining of special education with information and communications technology. This study presents an adaptive emotional adjustment model in mathematics e-learning for students with autism, emphasizing the lack of emotional perception in mathematics e-learning systems. In addition, an emotion classification for students with autism was developed by inducing emotions in mathematical learning environments to record changes in the physiological signals and facial expressions of students. Using these methods, 58 emotional features were obtained. These features were then processed using one-way ANOVA and information gain (IG). After reducing the feature dimension, methods of support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were used to classify four emotional categories: baseline, happy, angry, and anxious. After testing and comparisons, in a situation without feature selection, the accuracy rate of the SVM classification can reach as high as 79.3-%. After using IG to reduce the feature dimension, with only 28 features remaining, SVM still has a classification accuracy of 78.2-%. The results of this research could enhance the effectiveness of eLearning in special education.
Abstract: The technological concepts such as wireless hospital
and portable cardiac telemetry system require the development of
physiological signal acquisition devices to be easily integrated into
the hospital database. In this paper we present the low cost, portable
wireless ECG acquisition hardware that transmits ECG signals to a
dedicated computer.The front end of the system obtains and
processes incoming signals, which are then transmitted via a
microcontroller and wireless Bluetooth module. A monitoring
purpose Bluetooth based end user application integrated with patient
database management module is developed for the computers. The
system will act as a continuous event recorder, which can be used to
follow up patients who have been resuscitatedfrom cardiac arrest,
ventricular tachycardia but also for diagnostic purposes for patients
with arrhythmia symptoms. In addition, cardiac information can be
saved into the patient-s database of the hospital.
Abstract: The standard investigational method for obstructive
sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG),
which consists of a simultaneous, usually overnight recording of
multiple electro-physiological signals related to sleep and
wakefulness. This is an expensive, encumbering and not a readily
repeated protocol, and therefore there is need for simpler and easily
implemented screening and detection techniques. Identification of
apnea/hypopnea events in the screening recordings is the key factor
for the diagnosis of OSAS. The analysis of a solely single-lead
electrocardiographic (ECG) signal for OSAS diagnosis, which may
be done with portable devices, at patient-s home, is the challenge of
the last years. A novel artificial neural network (ANN) based
approach for feature extraction and automatic identification of
respiratory events in ECG signals is presented in this paper. A
nonlinear principal component analysis (NLPCA) method was
considered for feature extraction and support vector machine for
classification/recognition. An alternative representation of the
respiratory events by means of Kohonen type neural network is
discussed. Our prospective study was based on OSAS patients of the
Clinical Hospital of Pneumology from Iaşi, Romania, males and
females, as well as on non-OSAS investigated human subjects. Our
computed analysis includes a learning phase based on cross signal
PSG annotation.
Abstract: Blood pulse is an important human physiological signal commonly used for the understanding of the individual physical health. Current methods of non-invasive blood pulse sensing require direct contact or access to the human skin. As such, the performances of these devices tend to vary with time and are subjective to human body fluids (e.g. blood, perspiration and skin-oil) and environmental contaminants (e.g. mud, water, etc). This paper proposes a simulation model for the novel method of non-invasive acquisition of blood pulse using the disturbance created by blood flowing through a localized magnetic field. The simulation model geometry represents a blood vessel, a permanent magnet, a magnetic sensor, surrounding tissues and air in 2-dimensional. In this model, the velocity and pressure fields in the blood stream are described based on Navier-Stroke equations and the walls of the blood vessel are assumed to have no-slip condition. The blood assumes a parabolic profile considering a laminar flow for blood in major artery near the skin. And the inlet velocity follows a sinusoidal equation. This will allow the computational software to compute the interactions between the magnetic vector potential generated by the permanent magnet and the magnetic nanoparticles in the blood. These interactions are simulated based on Maxwell equations at the location where the magnetic sensor is placed. The simulated magnetic field at the sensor location is found to assume similar sinusoidal waveform characteristics as the inlet velocity of the blood. The amplitude of the simulated waveforms at the sensor location are compared with physical measurements on human subjects and found to be highly correlated.
Abstract: Electrophysiological signals were recorded from primary cultures of dissociated rat cortical neurons coupled to Micro-Electrode Arrays (MEAs). The neuronal discharge patterns may change under varying physiological and pathological conditions. For this reason, we developed a new burst detection method able to identify bursts with peculiar features in different experimental conditions (i.e. spontaneous activity and under the effect of specific drugs). The main feature of our algorithm (i.e. Burst On Hurst), based on the auto-similarity or fractal property of the recorded signal, is the independence from the chosen spike detection method since it works directly on the raw data.
Abstract: With getting older in the whole population, the
prevalence of stroke and its residual disability is getting higher and
higher recently in Taiwan. The functional electrical stimulation
cycling system (FESCS) is useful for hemiplegic patients. Because
that the muscle of stroke patients is under hybrid activation. The raw
electromyography (EMG) represents the residual muscle force of
stroke subject whereas the peak-to-peak of stimulus EMG indicates the
force enhancement benefiting from ES. It seems that EMG signals
could be used for a parameter of feedback control mechanism. So, we
design the feedback control protocol of FESCS, it includes
physiological signal recorder, FPGA biomedical module, DAC and
electrical stimulation circuit. Using the intensity of real-time EMG
signal obtained from patients, as a feedback control method for the
output voltage of FES-cycling system.