The human postural control system is complex and it combines information from different sources. The most important information comes from vestibular, visual and proprioceptive senses. We studied the ...effects of removing the visual and proprioceptive information simultaneously. The force feedback from the ground was removed with vibrators attached on the musculus soleus of both calves. By using features of force platform signals, when the vibrators were on, the lengths of the swaying paths were four times the lengths when the vibrators were off. Our results show that it is possible to separate the effects of the visual and proprioceptive senses from that of vestibular sense, which is very useful for investigations of balance problems in otoneurology. This supports our future aim to classify between healthy subjects and different otoneurological patients with signal analysis and pattern recognition methods to be used for force platform signals.
This paper proposes an integrated approach for the identification of daily hand movements with a view to control prosthetic members. The raw EMG signal is decomposed into Intrinsic Mode Functions ...(IMFs) with the use of Empirical Mode Decomposition (EMD). A number of features are extracted in time and in frequency domain. Two different dimentionality methods are tested, namely the Principal Component Analysis (PCA) technique and the RELIEF feature selection algorithm. The outputs of the dimensionality reduction stage are then fed to a linear classifier to perform the detection task. The approach was tested on a group of young individuals and the results appear promising.
Wavelets analysis methods have been widely used in the signal processing of biomedical signals. These methods represent the temporal characteristics of a signal by its spectral components in the ...frequency domain. In this way, important features of the signal can be extracted in order to understand or model the physiological system. This paper reviews the widely used orthogonal wavelet transform method in the biomedical applications.
Eye movements have been investigated in several areas of medicine and also elsewhere, such as in psychology or even in the development of human-computer interfaces. In the last few years we have ...designed a technique to stimulate, measure and analyze vestibulo-ocular reflex eye movements. In the otoneurological literature these are seen as a novel and promising means of revealing certain disorders and diseases associated with vertigo. Vestibulo-ocular reflex is stimulated by impulsive head movements. We developed the present pattern recognition technique to detect the stimulus (impulsive head movements) and the vestibulo-ocular reflex (response eye movements) generated from signals and to compute the latency and the gain values between them. Using our technique to calculate these attributes, we obtained clearly different results for a group of 22 dizzy patients than for a group of 30 healthy subjects.
The ability to predict episodes of acute hypotension (abnormal drop in arterial blood pressure) would be of immense benefit to the healthcare community, and is therefore a focus of research in both ...medical and engineering domains. This paper presents the use of Hidden Markov Models to predict the onset of acute hypotension, using blood pressure measurements over time. Our use of HMMs has been motivated by their ability to characterize sequential/temporal trends in a given time signal. This lends the ability to infer the health status based on blood pressure information collected over an interval of time, rather than just instantaneous measurements. We have tested the proposed technique on standard physiological signal datasets available online and have obtained promising results. As part of a bigger project, we see potential in the proposed technique being used in real time health monitoring systems.
Biochips are emerging as a useful tool for high-throughput acquisition of biological data and continue to grow in information quality and in discovering new applications. Recent advances include ...CMOS-based integrated biosensor arrays for deoxyribonucleic acid (DNA) expression analysis (Hassibi and Lee, 2005), (Schienle , 2004), and active research is ongoing for the miniaturization and integration of protein microarrays (Kiyonaka , 2004), (Rubina , 2003), (Scrivener , 2003), tissue microarrays (TMAs), (Chen , 2004), (Shergill , 2004), and fluorescence-based multiplexed cytokine immunoassays (Wang , 2002). The main advantages of microfluidic lab-on-a-chip include ease of use, speed of analysis, low sample and reagent consumption, and high reproducibility due to standardization and automation. Without effective data-analysis methods, however, the merit of acquiring massive data through biochips will be marginal. The high-dimensional nature of such data requires novel techniques that can cope with the curse of dimensionality better than conventional data-analysis approaches. In this paper, the authors proposed a pattern-mining method to analyze large-scale biological data obtained from high-throughput biochip experiments. In particular, when a data set is given as a matrix, the method can find patterns appearing in the form of (possibly overlapping) submatrices of the input matrix. The method exploits the techniques developed for the symbolic manipulation of Boolean functions. Leveraged by this approach, the method can find, given a data matrix, all patterns that satisfy specific input parameters. The authors tested the method with several large-scale biochip data and observed that the proposed method outperforms the alternatives in terms of efficiency and the number of patterns discovered.
Cardiovascular diseases are one of the major causes of death. Regular checkups and preventive actions can drastically help reducing fatal incidences. This can be achieved by monitoring the carotid ...artery or rather the carotid pulse signal. Commonly, ultrasound devices are used for that purpose. However, these devices are costly, mostly stationary and their usage requires training and experience. \mathbf{This} paper investigates the possible usage of radar systems as a contactless and low-cost alternative for carotid pulse measurements. Theoretical investigations reveal a linear relationship between the measurands of both devices and synchronous recordings from three test persons further confirm the feasibility of using radar systems as a potential device for monitoring cardiovascular diseases.
Mobility and cognition decline associated with aging and illness are the two most common, intertwined complexities faced by older adults. Cognitive changes are often associated with physical changes ...and a resulting increase in fall risk. The ongoing assessment of individuals can be limited by frequency of clinical appointments and as dementia progresses, assessments tools can have floor and accuracy issues. This paper presents the longitudinal data analysis of a 10-month pilot study of persons with dementia for the 6 participants that have completed the study out of 9 total to determine the feasibility of a balance assessment system. The participants were recruited from two different adult Day Programs and performed a balance monitoring task using the Biodex Balance System™ which analyzes and measures a participant's ability to remain steady while stationary. These preliminary results show that participants were generally able to use the system that required them to stand in position without moving their feet during assessment. This system has not been used previously in study of persons with dementia (PWD). The system requires participants to position their feet so their CoG is centered on a system target screen and participants were not consistently able to keep their feet located in this precise location for comfort or memory reasons. Additional study is required to correlate results to health outcomes such as fall risk.
The domain of Brain-Computer Interface (BCI) explores how humans can interact with the computer without giving direct instruction. Recognizing the activity from brain signals affiliated with an ...electronic device might (i.e. MEG) provide a stepping stone in the field of BCI. The intended algorithm in the paper aimed at presenting a statistical strategy to classify the brain signal from the MEG signal data, provided by BCI competition IV dataset III. The algorithm is compartmentalized in three levels: preprocessing, feature extraction, and classification. Autoregressive features have been extracted from the signals to classify using UNEQ, KNN and SIMCA, discuss the data distribution and asses how well the algorithm performs on unknown yet similar distribution. The proposed algorithm has obtained 64% prediction accuracy and 67% validation accuracy, which exceeds the current highest result reported on the same dataset.
Secondary measures of regularity from an entropy profile in detecting Arrhythmia Udhayakumar, Radhagayathri K.; Karmakar, Chandan; Palaniswami, Marimuthu
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
07/2017, Letnik:
2017
Conference Proceeding, Journal Article
The most recently introduced concept of a `complete entropy profile' is a non-parametric (with regard to tolerance r) approach of entropy estimation. Given a signal, on generating its complete ...entropy profile, numerous secondary measures of regularity can be derived from the same. These profile based measures are seen to outperform the traditional ApEn statistic (evaluated at a single r) in estimating signal regularity. In this paper, we compare the performance of ApEn (evaluated at an r = 0.15 * SD of signal and an m = 2) with that of profile based measures such as MaxApEn, TotalApEn, AvgApEn, SDApEn, kurtApEn and skewApEn, in detecting `Arrhythmic' RR interval signals from `Normal' RR interval signals. Results indisputably prove the superiority of AvgApEn (AUC > 0.9 at data lengths N ≥ 200) and MaxApEn (AUC > 0.75 at all data lengths) as regularity statistics in detecting Arrhythmia, above all the other measures used.
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