nils-herrmann/clean_scirepeval huggingface dataset:
Real-time separation of discontinuous adventitious sounds from vesicular sounds using a fuzzy rule-based filter

The separation of pathological discontinuous adventitious sounds (DAS) from vesicular sounds (VS) is of great importance to the analysis of lung sounds since DAS are related to certain pulmonary pathologies. An automated way of revealing the diagnostic character of DAS, by isolating them from VS, based on their nonstationarity, is presented. The proposed algorithm uses two adaptive network-based fuzzy inference systems to compose a generalized fuzzy rule-based stationary-nonstationary filter (GFST-NST). The training procedure of the fuzzy inference systems involves the outputs of the wavelet transform-based stationary-nonstationary filter (WTST-NST), proposed by Hadjileontiadis and Panas (1997). The basic idea of the GFST-NST was initially proposed by the authors with the introduction of the fuzzy rule-based stationary-nonstationary filter (FST-NST) (1997), tested with the separation of crackles from VS. The main contribution of this paper is the modification of the structure of the FST-NST filter to a serial-type fuzzy filter that, unlike the parallel operation of the FST-NST filter, sends a predicted stationary signal (VS) into the predictor of the nonstationary (DAS). Applying the GFST-NST filter to fine-coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are separated from VS. The separation performance of the GFST-NST filter was evaluated through quantitative and qualitative indexes that proved its efficiency and superiority against the FST-NST filter. When compared to the WTST-NST filter, the GFST-NST filter performed similarly in accuracy and objectiveness, but in a faster way. Thus, the GFST-NST filter combines the separation accuracy of the WTST-NST filter with the real-time implementation of the FST-NST filter, so it can easily be used in clinical medicine as a module of an integrated intelligent patient evaluation system.
A Texture-Based Classification of Crackles and Squawks Using Lacunarity

An automatic classification method to efficiently discriminate the types of discontinuous breath sounds (DBSs), i.e., fine crackles (FCs), coarse crackles (CC), and squawks (SQ), is presented in this paper. Using the lacunarity of the acquired DBS, the proposed classification method, namely LAC, introduces a texture-based approach that captures the differences in the distribution of FC, CC, and SQ across the breathing cycle, which may lead to more accurate characterization of the pulmonary acoustical changes due to the related pathology. Prior to the lacunarity analysis, wavelet-based denoising of DBS is employed to eliminate effects of the vesicular sound (background noise) to DBS oscillatory pattern. LAC analysis builds its classification power both upon the use of lacunarity at an optimum scale and the approximation of its trajectory across an optimum range of scales using a three-parameter hyperbola model. LAC is applied to 363 DBS corresponding to 25 cases included in four lung sound databases. Results show that LAC efficiently classifies the three DBS categories in the comparison groups of FC-CC, FC-SQ (both with mean accuracy of 100%), CC-SQ (mean accuracy of 99.62%-100%), and FC-CC-SQ (mean accuracy of 99.75%-100%). When compared to other classification tools, LAC seems quite attractive, since, without employing high computational complexity, it results in high classification accuracy. Moreover, LAC introduces a ldquotexturerdquo concept in the analysis of breath sounds, something that strongly relates to the perception of the bioacoustic signals by the physician. Due to its simplicity, LAC could be implemented in a real-time context and be used in clinical medicine as a module of an integrated intelligent patient evaluation system.
Incorporating Risk Attitude and Reputation into Infinitely Repeated Games and an Analysis on the Iterated Prisoner's Dilemma

Many real life situations can be modeled as Prisoner's dilemma. There are various strategies in the literature. However, few of which match the design objectives of an intelligent agent - being reactive and pro-active. In this paper, we incorporate risk attitude and reputation into infinitely repeated games. In this way, we find that the original game matrix can be transformed to a new matrix, which has a kind of cooperative equilibrium. We use the proposed concepts to analyze the Iterated Prisoner's dilemma. Simulation also shows that agents, which consider risk attitude and reputation in the decision-making process, have improved performance and are reactive as well as pro-active.
Optimal detector design for molecular communication systems using an improved swarm intelligence algorithm

The authors optimise the detection process of diffusion-based molecular communication systems utilising the weighted sum detector with appropriate weight values. Interestingly, no optimisation technique has ever been proposed for the calculation of the weights. To this end, they build on the standard particle swarm optimisation (PSO) technique and propose a robust iterative optimisation algorithm, called acceleration-aided PSO (A-APSO). While modified swarm-based optimisation algorithms focus on slight variations of the standard mathematical formulas, in A-APSO, the acceleration variable of the particles in the swarm is also involved in the search space of the optimisation problem. Particularly, they implement the A-APSO algorithm to evaluate the detector's weights that minimise the closed-form expression of the error probability. Their findings reveal that, when employing the A-APSO weights, the error performance is superior to that achieved by utilising the weight values already existing in the literature or those evaluated with the standard PSO algorithm.
EEG-based emotion recognition using hybrid filtering and higher order crossings

EEG-based emotion recognition is a relatively new research field in the Human Computer Interaction area and its aim is the implementation of new algorithms that would identify and recognize emotions from EEG (electroencephalogram) signals. Towards that, a novel method is presented in this paper that employs an optimized hybrid filter, using Empirical Mode Decomposition (EMD) and Genetic Algorithms (GA), in order to isolate the Intrinsic Mode Functions (IMFs) corresponding to the plurality of the energy content of the initial signal for classification. The filtered signal is constructed by the selected IMFs and is subjected to Higher Order Crossings (HOC) analysis for feature extraction. The final feature vector is classified into six emotion classes, i.e., happiness, anger, fear, disgust, sadness, and surprise, using Quadratic Discriminant Analysis. The high classification performance (84.72% maximum mean classification rate) justifies the efficiency of the proposed EEG-based emotion recognition approach.
On modeling impulsive bioacoustic signals with symmetric /spl alpha/-stable distributions: application in discontinuous adventitious lung sounds and explosive bowel sounds

A new perspective in modeling impulsive bioacoustic signals (BS) by means of symmetric /spl alpha/-stable distribution (S/spl alpha/S) is presented in this work. The method was applied in discontinuous adventitious lung sounds and explosive bowel sounds drawn from BS databases, after de-noising. Their non-Gaussianity due to their strong impulsiveness was confirmed and S/spl alpha/S-based model was used for autoregressive (AR) estimation of their source signal. Via the parameters of S/spl alpha/S (a, /spl alpha/ and /spl gamma/) the effect of the transmission path on the impulsive nature of the source was described. Values of the covariation coefficient /spl lambda//sub XY/ confirmed a correlation of squawks and fine crackles in their production mechanism.
Nonlinear characteristics of wheezes as seen in the wavelet higher-order spectra domain

The aim of this study was to capture and analyze the nonlinear characteristics of asthmatic wheezes, reflected in the quadrature phase coupling of their harmonics, as they evolve over time within the breathing cycle. To achieve this, the continuous wavelet transform was combined with third-order statistics/spectra. Wheezes from diagnosed asthmatic patients were drawn from a lung sound database and analyzed in the time-bi-frequency domain. The analysis results justified the efficient performance of this combinatory approach to reveal and quantify the evolution of wheeze nonlinearities with time
Enhancement of R-wave detection in ECG data analysis using higher-order statistics

A new way of detecting the R-wave in a QRS complex of an electrocardiogram (ECG) based on higher-order statistics (HOS) is presented. The proposed method employs HOS-based parameters, such as skewness and kurtosis, in order to formulate an adaptive detector of the R peak with high accuracy. Experimental results, when applying the proposed method to pre-classified ECG data from the Massachusetts Institute of Technology/Beth Israel Hospital (MIT/BIH) ECG database, prove that the proposed method exhibits over 99% of detectability, even when the ECG data are contaminated with noise. Due to its simplicity it could be feasible in a real-time context and it could be applied in routine ambulatory and/or clinical heart rate screening.
Dynamic spectrum sensing through accelerated particle swarm optimization

In this paper, a novel optimization algorithm, called the acceleration-aided particle swarm optimization (A-APSO), is proposed for reliable dynamic spectrum sensing in cognitive radio networks. In A-APSO, the acceleration variable of the particles in the swarm is also considered in the search space of the optimization problem. We show that the proposed A-APSO based spectrum sensing technique is more efficient in terms of performance than the corresponding one based on the standard particle swarm optimization algorithm.
“SEE and SEE”: An Educational Tool for Kids with Hard of Hearing

An educational software, namely ldquoSEE and SEErdquo, that aims to enhance literacy skills of deaf or hard of hearing children is presented in this paper. The proposed computer-based educational environment, takes into account childrenpsilas visual learning characteristics. The software provides a series of adjustable functionalities to the teacher, so s/he can create visual-kinetic educational information for each pupil. Moreover, pupils have the opportunity to use bilateral presentations of the information content, by evoking sign language video for each text sentence, accompanied with comprehensive diagrams and pictures. A series of test-questions are also incorporated that reflect the contribution of each educational process to the learning curve. The whole activity is logged and archived at a local database that outputs statistic/activity reports and files. ldquoSEE and SEErdquo can prove to be a useful learning object that contributes to the normalization of the educational environment towards the children needs.
Enhancement of bowel sounds by wavelet-based filtering

This paper evaluates the performance of an automatic method for structural decomposition, noise removal and enhancement of bowel sounds (BS), based on the wavelet transform. The proposed method combines multiresolution analysis with hard thresholding to compose a wavelet transform-based stationary-nonstationary (WTST-NST) filter, for enhanced separation of bowel sounds (BS) from superimposed noise. Quantitative and qualitative analysis of the experimental results, when applying the WTST-NST filter to BS recorded from controls and patients with gastrointestinal dysfunction, prove that the ability of the WTST-NST filter to remove noise and reveal the authentic structure of BS is excellent. By eliminating the need to record a noise reference signal, this method reduces hardware overhead when analysis of BS is the primary aim. The method is independent of subjective human judgement for selection of noise reference templates, is robust to different levels of signal interference, and, due to its simplicity, can easily be used in clinical medicine.
Bispectral analysis of surface EMG

The objective of this ongoing study is to investigate whether or not bispectral analysis (BS), a particular form of higher order spectra (HOS), may be utilized for analyzing the surface electromyographic signal (SEMG). The bicoherence index was used for characterizing the Gaussianity of the signal. Results indicate that SEMG signal distribution is highly nonGaussian at low and high levels of force whereas the distribution has its maximum Gaussianity at the mid level of maximum voluntary contraction (MVC), i.e. at 50%. A measure of the linearity of the signal, based on deciding whether or not the estimated bicoherence is constant, follows the reverse pattern with the measure of Gaussianity. The power spectrum's (PS) median frequency, decreases from 105 to 93 Hz with increase of force, whereas the number of turns and the number of zero crosses increase with force. Further work is currently in progress in order to evaluate the usefulness of HOS in normal subjects and subjects suffering from neuromuscular disorders.
Primary School Music Education and the Effect of Auditory Processing Disorders: Pedagogical/ICT-Based Implications

Primary music education introduces children to the world of sound and music. For some children, however, auditory process disorders (APDs) act as a barrier to the comprehension of the sound elements, like rhythmic motives, variations, etc. The effect of APD in music perception is often neglected within classroom activities. This paper tries to shed light upon this effect, by focusing on duration comprehension. Experimental results from APD testing in pupils (9-12 yrs) from three Greek primary schools show that age influences the duration pattern sequence perception. Moreover, sensitivity in spatial sound information is increased in the age of 10 yrs. These results initiate some implications for ICT-based approach in music education that would foster adaptation to pupilspsila special needs during the educational process.
Automatic P phase picking using maximum kurtosis and κ-statistics criteria

The identification problem of P seismic phase onset has been addressed, based on the maximum-kurtosis assumption, /spl kappa/-statistics, and the Chebyshev inequality. Depending on two statistical decision criteria, the proposed approach provides either a single P onset peak or an interval in which the P onset exists, both with a confidence percentage. Results on real seismic data evaluated using a performance index justify the contribution of the proposed method toward an accurate and fully automated P onset identification.
Serious games as a means for holistically supporting Parkinson's Disease patients: The i-PROGNOSIS personalized game suite framework

Parkinson's Disease (PD) is a well-known progressive and neurodegenerative disorder, resulting in motor and non-motor symptoms that significantly reduce the quality of life. Motivated by the increased adoption of serious games in health care interventions sector in the last years, an unobtrusive approach is explored here, realised via the i-PROGNOSIS interventions platform (www.i-prognosis.eu), consisting of a game-based suite. In this way, targeting intelligent early detection and intervention in PD area, the Personalised Game Suite (PGS) design approach is presented, integrating different serious games in a unified platform. Overall, this comprehensive review gives an overview of studies examining motor and non-motor symptoms for PD along with the PGS design approach, in order to inform researchers and game designers for its consideration in the self-management of PD.
Toward an EEG-Based Recognition of Music Liking Using Time-Frequency Analysis

Affective phenomena, as reflected through brain activity, could constitute an effective index for the detection of music preference. In this vein, this paper focuses on the discrimination between subjects' electroencephalogram (EEG) responses to self-assessed liked or disliked music, acquired during an experimental procedure, by evaluating different feature extraction approaches and classifiers to this end. Feature extraction is based on time-frequency (TF) analysis by implementing three TF techniques, i.e., spectrogram, Zhao-Atlas-Marks distribution and Hilbert-Huang spectrum (HHS). Feature estimation also accounts for physiological parameters that relate to EEG frequency bands, reference states, time intervals, and hemispheric asymmetries. Classification is performed by employing four classifiers, i.e., support vector machines, k-nearest neighbors (k-NN), quadratic and Mahalanobis distance-based discriminant analyses. According to the experimental results across nine subjects, best classification accuracy {86.52 (±0.76)%} was achieved using k-NN and HHS-based feature vectors ( FVs) representing a bilateral average activity, referred to a resting period, in β (13-30 Hz) and γ (30-49 Hz) bands. Activity in these bands may point to a connection between music preference and emotional arousal phenomena. Furthermore, HHS-based FVs were found to be robust against noise corruption. The outcomes of this study provide early evidence and pave the way for the development of a generalized brain computer interface for music preference recognition.
On Applying Continuous Wavelet Transform in Wheeze Analysis

The identification of continuous abnormal lung sounds, like wheezes, in the total breathing cycle is of great importance in the diagnosis of obstructive airways pathologies. To this vein, the current work introduces an efficient method for the detection of wheezes, based on the time-scale representation of breath sound recordings. The employed Continuous Wavelet Transform is proven to be a valuable tool at this direction, when combined with scale-dependent thresholding. Analysis of lung sound recordings from ‘wheezing’ patients shows promising performance in the detection and extraction of wheezes from the background noise and reveals its potentiality for data-volume reduction in long-term wheezing screening, such as in sleep-laboratories.
Evidence of Variabilities in EEG Dynamics During Motor Imagery-Based Multiclass Brain–Computer Interface

Inter-subject and inter-session variabilities pose a significant challenge in electroencephalogram (EEG)-based brain–computer interface (BCI) systems. Furthermore, high dimensional EEG montages introduce huge computational burden due to excessive number of channels involved. Two experimental, i.e., inter-session and inter-subject, variabilities of EEG dynamics during motor imagery (MI) tasks are investigated in this paper. In particular, the effect on the performance of the BCIs due to day-to-day variability in EEG dynamics during the alterations in cognitive stages is explored. In addition, the inter-subject BCIs feasibility between cortically synchronized and desynchronized subject pairs on pairwise performance associativity is further examined. Moreover, the consequences of integrating spatial brain dynamics of varying the number of channels - from specific regions of the brain - are also discussed in case of both the contexts. The proposed approach is validated on real BCI data set containing EEG data from four classes of MI tasks, i.e., left/right hand, both feet, and tongue, subjected prior to a preprocessing of three different spatial filtering techniques. Experimental results have shown that a maximum classification accuracy of around 58% was achieved for the inter-subject experimental case, whereas a 31% deviation was noticed in the classification accuracies across two sessions during the inter-session experimental case. In conclusion, BCIs, without the subject-and session-specific calibration and with lesser number of channels employed, play a vital role while promoting a generic and efficient framework for plug and play use.
Short-term analysis of heart-rate variability using wavelet packets: an efficient detector of sleep apnea episodes

Obstructive sleep apnea is a very common condition characterized by over 10 seconds interruption of breathing during sleep. The accurate and fast detection of sleep apnea stages is of great importance for patients' recovery and treatment. A criterion for the diagnosis of such respiratory disorder based on the characteristics of the heart-rate variability signal in the wavelet packets domain is presented. Preclassified data of 20 apneic patients and 20 controls drawn from two international databases were subjected to analysis. Experimental results show that a wavelet packet-based measure can be formed for noninvasive diagnosis of sleep apnea. This was justified in all cases of apneic patients where all apneic episodes were accurately identified. The technique is rather simple and could be easily used for prescreening apneic patients in sleep laboratories and intensive care units.
Are Elderly Less Responsive to Emotional Stimuli? An EEG-based Study across Pleasant, Unpleasant and Neutral Greek Words

A plethora of studies has shown that working memory, processing speed and fluid intelligence are diminished with aging. However, emotional processing remains relatively stable even though emotional processing alters through aging. Neurophysiological studies have employed emotional stimuli to investigate age differences through Event Related Potentials (ERPs). The present approach used affective visual word stimuli derived from the Greek language. Healthy young and elderly volunteers passively viewed the stimuli which were divided into pleasant, unpleasant and neutral. The study shows differential processing of emotional stimuli in comparison to the neutral in terms of temporal resolution (latency) and activation of neuronal assembles (amplitude). The age factor interacts with emotional dimension through a complex pattern while laterality differences also occur. Our results suggest a difference in the way emotional stimuli are processed during aging through functional compensation.
Multimedia database "Marburg Respiratory Sounds (MARS)"

Lung sounds and their relation with the pulmonary pathology make their accurate interpretation crucial. The traditional method of auscultation with the stethoscope is rather subjective and requires long experience. For the development of advanced detection algorithms of pathological lung sounds a large number of validated lung sound recordings are needed. In contrast to existing commercial lung sound CDs we compiled a database which contains many cases with typical auscultation findings and findings with difficult evaluation. A novelty is the combination between auscultation results and lung function parameters.
Detecting fetal heart sounds by means of Fractal Dimension analysis in the Wavelet domain

Phonocardiography is a low-cost technique for the detection of fetal heart sounds (FHS) that can extend clinical auscultation in mobile and home care setups. The work presented here examines the transferability of a Wavelet Transform (WT)-based method that combines also Fractal Dimension (FD) analysis, previously proposed as WT-FD for the cases of lung and bowel sound analysis , to the extraction of FHSs. The WT-FD method has been evaluated with 12 simulated FHS signals and has shown promising results in terms of accuracy and performance (89%) in identifying the location of heartbeat, even in cases of signals with additive noise up to (6dB). This robustness paves the way for WT-FD testing in real FHSs, recorded under clinical setting, clearly contributing to better evaluation of the fetal heart functionality.