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In the earlier studies we have developed activity recognition algorithms which are based on features calculated from data of 3D accelerometer sensor placed on the hip, close to the centre of mass. In the development subjects have been young adults. Now we study if the input features of the algorithm are generalized for different set-ups; for older adults and when the sensor is worn as a necklace....
Entropy, as a measure of randomness in time-varying signals, is widely used in areas such as thermodynamics, statistical mechanics and information theory. This paper investigates the use of two commonly employed entropy measures, namely Wavelet Entropy and Approximate Entropy, as a predictor of tremor reappearance in Essential Tremor patients; the predictor input is a raw surface-electromyographic...
Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used...
In this paper, we propose a new joint watermarking/encryption algorithm for the purpose of verifying the reliability of medical images in both encrypted and spatial domains. It combines a substitutive watermarking algorithm, the quantization index modulation (QIM), with a block cipher algorithm, the Advanced Encryption Standard (AES), in CBC mode of operation. The proposed solution gives access to...
The respiratory rate is a vital sign that can provide important information about the health of a patient, especially that of the respiratory system. The aim of this study is to develop a simple method that can be applied in wearable systems to monitor the respiratory rate automatically and continuously over extended periods of time. In this paper, a novel respiratory rate estimation method is presented...
Parkinson's disease (PD) is characterized by motor disabilities that can be alleviated reasonably with appropriate medication. However, there is a lack of objective methods for quantifying the efficacy of treatment in PD. We applied here an objective method for quantifying the effects of medication in PD using EMG and acceleration measurements and analysis. In the method, four signal features were...
This study proposes a methodology to detect circaseptan (CS) rhythm in pulse rate (PR) data and to investigate the “Monday effect” in CS rhythm. Daily PR was collected from a middle-aged healthy working woman over one year. PR, SDNN index and sample entropy (SampEn) were chosen as the indexes of PR dynamics. In order to avoid interference from other biorhythms, ensemble empirical mode decomposition...
This paper discusses the effect of atrioventricular conduction time (AVCT) on the short-term Heart Rate Variability (HRV) by computing HRV parameters using intervals between the onsets of successive P waves (PP time series) for three groups: normal, arrhythmia and sudden cardiac death (SCD) patients. A very precise wavelet transform based ECG delineator was developed to detect PP, PR and RR time series...
The multiscale analysis of physiologic time series such as the RR interval time series has revealed that the entropy differs according to the scale. Furthermore, healthy subjects show different characteristics on the different time scales compared to patients. Instead of calculating entropies of the time series, the sequence of acceleration and deceleration of the instantaneous heart rate may also...
Calculating entropy rate in physiologic signals has proven very useful in many settings. Common entropy estimates for this purpose are sample entropy (SampEn) and its less robust elder cousin, approximate entropy (ApEn). Both approaches count matches within a tolerance r for templates of length m consecutive observations. When physiologic data records are long and well-behaved, both approaches work...
The rough entropy (RoughEn) is developed based on the rough set theory. It has the advantage of low computational complexity, because there is no parameter to set in RoughEn. In this paper, we characterized the feature of surface electromyography (SEMG) signal with RoughEn and then used support vector machine to classify six different hand motions. The sample entropy, wavelet entropy and approximate...
The present study investigated the difference in voice perturbation measures and parameters obtained from nonlinear dynamic analysis between normal laryngeal phonation and standard esophageal (SE) phonation. Jitter, shimmer, correlation dimension and Kolmogorov entropy were measured from 10 SE and 10 normal male speakers of Cantonese. Jitter and shimmer values were significantly higher for SE than...
While a healthy human heart produce a rhythmic pattern of sounds, some heart disorder induce deviations perceived as abnormal sounds called murmurs. Despite many murmurs can be considered harmless, other constitute the first basis of a heart disorder. In this sense, a correct diagnosis remains essential; however, due to the subjectivity on using human ear to make diagnosis, automatic detection systems...
The aim of this study was to analyze the magnetoencephalography (MEG) background activity in Attention-Deficit/Hyperactivity Disorder (ADHD) using a regularity measure: sample entropy (SampEn). Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 14 ADHD patients and 14 control subjects. Our results showed that ADHD patients' MEGs were more regular than controls' recordings...
MEET is an R package that integrates a set of algorithms for the detection of transcription factor binding sites (TFBS). The MEET R package includes five motif searching algorithms: MEME/MAST(Multiple Expectation-Maximization for Motif Elicitation), Q-residuals, MDscan (Motif Discovery scan), ITEME (Information Theory Elements for Motif Estimation) and MATCH. In addition MEET allows the user to work...
This work aims to propose new methodologies for the quantitative characterization of insomnia. Sleep microstructure, as expressed by Cyclic Alternatic pattern (CAP) sleep, is studied and differences between normal sleepers and insomniacs are investigated. The dynamic in the structure of CAP activation events is studied by use of wavelet analysis and the content of events, i.e. EEG dynamics, is studied...
The recently introduced multiscale entropy (MSE) method accounts for long range correlations over multiple time scales and can therefore reveal the complexity of biological signals. The existing MSE algorithm deals with scalar time series whereas multivariate time series are common in experimental and biological systems. To that cause, in this paper the MSE method is extended to the multivariate case...
This manuscript proposes a particle swarm-based feature extraction to monitors brain activity with the goal of identifying correlate cognitive states and intensity of a task. This in turn would allow us to develop a pattern recognition system that will classify such cognitive states and thus to redistribute the workload to other subjects. In this abstract, we present a recognition system that employ...
Monitoring procedures are the basis to evaluate the clinical state of patients and to assess changes in their status, thus providing necessary interventions in time. To obtain this important objective it is necessary to integrate technological development with systems performing biomedical knowledge extraction and classification. Methods extracting non linear characteristics from HRV signal are presented...
Cataract remains a leading cause for blindness worldwide. Cataract diagnosis via human grading is subjective and time-consuming. Several methods of automatic grading are currently available, but each of them suffers from some drawbacks. In this paper, a new approach for automatic detection based on texture and intensity analysis is proposed to address the problems of existing methods and improve the...
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