varunr14/newsummary huggingface dataset:
In this paper we propose a novel approach for the compression of multichannel electroencephalograph signals . The method assumes that EEG signals are the linear mixture of several independent components . To retain the ICs the proposed scheme first applies an independent component analysis with a preprocessing step of principal component analysis to EEG signals . Then the compression scheme is composed of two parts the ICs compression part and the residue compression part . Each IC is arranged in the form of matrix and then compressed with the algorithm of set partitioning in hierarchical trees . The residue signals are compressed in the same way as ICs but with a higher compression ratio . The appropriate combination of compression ratios of the ICs and the residue is explored to achieve desired performance . The compression scheme is tested with eight datasets sampled at two different frequencies . The experimental results demonstrate the high compression performance of the proposed approach and its potential usage in the EEG related telemedicine applications . 


Summary: PCA based ICA is proposed to exploit the inter channel correlations. 1 D signal is arranged in the form of matrix to exploit the intra channel correlations. SPIHT algorithm is used to compress the signals in matrix form.
Features greatly influence the results of speech emotion recognition among which Mel frequency Cepstral Coefficients is the most commonly used in speech emotion . However MFCC does not consider both the relationship among neighbor coefficients of Mel filters of a frame and the relationship among coefficients of Mel filters of neighbor frames which possibly leads to lose many useful features from spectrogram . This paper presents novel weighted spectral features based on Local Hu moments . The idea is motivated by that the energy on spectrogram would drastically vary with some emotion types such as angry and happy while it would slightly change with other emotion types such as sadness and fear . This phenomenon would affect the local energy distribution of spectrogram in both time axis and frequency axis of spectrogram . To describe local energy distribution of spectrogram Hu moments computed from local regions of spectrogram are used as Hu moments can evaluate the degree how the energy is concentrated to the center of energy gravity of local region of spectrogram and can significantly vary with the speech emotion types . The conducted experiments validate the proposed features in terms of the effectiveness of the speech emotion recognition . 


Summary: HuWSF is computed from local regions of a spectrogram by Hu moments. HuWSF can evaluates how the energy aggregate to some frequencies in a spectrogram. HuWSF extracts the features among neighbor coefficients of Mel filters of a frame. HuWSF extracts the features among coefficients of Mel filters of neighbor frames. HuWSF can reduce the changes brought by sentences speakers and speaking styles.
The feature extraction of event related potentials is a significant prerequisite for many types of P300 BCIs . In this paper we proposed a multi ganglion artificial neural network based feature learning method to extract a deep feature structure of single trial multi channel ERP signals and improve classification accuracy . Five subjects took part in the Imitating Reading ERP experiments . We recorded the target electroencephalography samples and non target samples for each subjects . Then we applied ANNFL method to extract the feature vectors and classified them by using support vector machine . The ANNFL method outperforms the principal component analysis method and conventional three layer auto encoder and then leads to higher classification accuracies of five subjects BCI signals than using the single channel temporal features . ANNFL is an unsupervised feature learning method which can automatically learn feature vector from EEG data and provide more effective feature representation than PCA method and single channel temporal feature extraction method . 


Summary: The multi ganglion ANN based feature learning ANNFL method is an unsupervised feature extraction method. This method can find an effective feature representation automatically for single trial P300 signal. The ANNFL method reduces the training time of conventional three layer auto encoder and leads to better classification results in the P300 BCI paradigm of our study.
One of the most common signs of tiredness or fatigue is yawning . Naturally identification of fatigued individuals would be helped if yawning is detected . Existing techniques for yawn detection are centred on measuring the mouth opening . This approach however may fail if the mouth is occluded by the hand as it is frequently the case . The work presented in this paper focuses on a technique to detect yawning whilst also allowing for cases of occlusion . For measuring the mouth opening a new technique which applies adaptive colour region is introduced . For detecting yawning whilst the mouth is occluded local binary pattern features are used to also identify facial distortions during yawning . In this research the Strathclyde Facial Fatigue database which contains genuine video footage of fatigued individuals is used for training testing and evaluation of the system . 


Summary: Yawning is detected upon mouth opening mouth covering and facial feature distortion. Mouth covered detection is based on classification of LBP features extracted from mouth covering hand. Proposed approach has been tested on real video sequences of sleep deprived volunteers.
Feature extraction and automatic classification of mental states is an interesting and open area of research in the field of brain computer interfacing . A well trained classifier would allow the BCI system to control an external assistive device in real world problems . Sometimes standard existing classifiers fail to generalize the components of a non stationary signal like Electroencephalography which may pose one or more problems during real time usage of the BCI system . In this paper we aim to tackle this issue by designing an interval type 2 fuzzy classifier which deals with the uncertainties of the EEG signal over various sessions . Our designed classifier is used to decode various movements concerning the wrist and finger . For this purpose we have employed extreme energy ratio to construct the feature vector . The average classification accuracy achieved during offline training and online testing over eight subjects are 86.45 and 78.44 respectively . On comparison with other related works it is shown that our designed IT2FS classifier presents a better performance . 


Summary: Session to session EEG variation is addressed and tackled by the proposed method. Interval type 2 fuzzy classification approach adopted for the purpose. Studied on discrimination of wrist and fingers motor imagery EEG signals. Offline and online experiments establish the efficacy of the proposed method. Using Extreme Energy Ratio as features 86.45 and 78.44 accuracies are obtained.
The signal preprocessing is prerequisite for reduction of noise and for better estimation of sources from the measured field distribution of multichannel data since different measurement channels may be contaminated by different types of artifacts and noise . Toward this we use a combination of independent component analysis and ensemble empirical mode decomposition to denoise the multichannel magnetocardiography data . In this technique MCG time series data is first subjected to ICA to obtain the statistically independent components and subsequently the EEMD interval threshold based denoising is applied to the ICs prior to the reconstruction of the signal . We compare the results obtained from EEMD ICA with those obtained using the conventional ICA alone and also using the wavelet enhanced ICA . We illustrate the effect of these denoising techniques on the pseudo current density maps which aid in visualizing the source location . The results obtained from the EEMD ICA are seen to be decidedly superior compared to those obtained by ICA alone and wICA methods . 


Summary: The combination of EEMD and ICA proposed and used for multichannel MCG signal denoising. Reduces the computational complexity associated with EEMD alone and effectively enhances the signal quality. The effect of ICA alone wICA and EEMD ICA on the magnetic field map and pseudo current density maps is investigated. The EEMD ICA combination yield robust source estimate on the PCD map. The EEMD ICA can be applied to beat to beat analysis of MCG signals.
A novel feedback controlled hydrodynamic human circulatory system simulator well suited for in vitro validation of cardiac assist devices is presented in this paper . The cardiovascular system simulator consists of high bandwidth actuators allowing a high precision hardware in the loop hydrodynamic interface in connection with physiological circulatory models calculated in real time . The hydrodynamically coupled process dynamics consist of several actuator loops and demand a multivariable control design approach in the face of system nonlinearities and uncertainties . Based on a detailed model employing the Lagrange formalism a robust decentralised controller is designed . Fixed structural constraints and the minimisation of the norm necessitate the application of nonsmooth optimisation techniques . The robust decentralised norm optimal controller is tested in extensive in vitro experiments and shows good performance with regard to reference tracking and system coupling . In vitro experiments include multivariable reference step tests and frequency analysis tests of the vascular impedance transfer function . 


Summary: We develop a novel decentralised feedback controller for a hydrodynamic human circulatory system simulator. We present a detailed model of the human circulatory system simulator. Nonlinear simulation and experimental results underline the performance of the proposed controller. In vitro experiments with a blood pump show sufficiently fast reference tracking to realise aortic and ventricle pressures.
The response of the brain to a sensory stimulus may present itself in the electroencephalogram as evoked and or induced activity . While the evoked response is given by peaks and troughs in the signal time locked and phase locked to the stimuli the induced response is time but not phase locked and can be considered as an increase or a decrease in the power of EEG in a specific frequency band at a specific time range with regard to the stimulus onset . The induced response does not have the same phase following successive stimuli . It is believed that cognition and perception of a stimulus present themselves primarily as the induced response in the EEG . In this paper the induced response of the brain to auditory speech stimuli is investigated and different approaches to detect induced activity are compared . It is shown that there is an increase in theta and delta power in response to words compared to the baseline starting around 500ms after their onset . During this time there is also an increase in pairwise coherence between the posterior electrodes . In response to tone bursts a change in pairwise coherence was observed in the beta band starting around 200ms . To the best of our knowledge this is the first time such responses have been described using simple protocols without complex stimulus manipulations being involved . Responses in the EEG to speech rather than the more conventional tone bursts or clicks suggests that it may be feasible to use the EEG as an objective means to demonstrate brain activation to salient real world stimuli . This would be of particular benefit in investigating access to speech in patients who are unable or unwilling to reliably respond to conventional subjective experimental protocols such as infants . 


Summary: Brain s induced response was measured in response to auditory tones and words. Power and coherence analyses were used for this purpose. Power analyses detected induced activity in response to words. Coherence analyses showed significant induced activity in response to both stimuli. The results may be of interest in studies involving hearing impaired patients.
Bipolar disorders are characterized by a mood swing ranging from mania to depression . A system that could monitor and eventually predict these changes would be useful to improve therapy and avoid dangerous events . Speech might convey relevant information about subjects mood and there is a growing interest to study its changes in presence of mood disorders . In this work we present an automatic method to characterize fundamental frequency dynamics in voiced part of syllables . The method performs a segmentation of voiced sounds from running speech samples and estimates two categories of features . The first category is borrowed from Taylor s Tilt intonational model . However the meaning of the proposed features is different from the meaning of Taylor s ones since the former are estimated from all voiced segments without performing any analysis of intonation . A second category of features takes into account the speed of change of F0 . In this work the proposed features are first estimated from an emotional speech database . Then an analysis on speech samples acquired from eleven psychiatric patients experiencing different mood states and eighteen healthy control subjects is introduced . Subjects had to perform a text reading task and a picture commenting task . The results of the analysis on the emotional speech database indicate that the proposed features can discriminate between high and low arousal emotions . This was verified both at single subject and group level . An intra subject analysis was performed on bipolar patients and it highlighted significant changes of the features with different mood states although this was not observed for all the subjects . The directions of the changes estimated for different patients experiencing the same mood swing were not coherent and were task dependent . Interestingly a single subject analysis performed on healthy controls and on bipolar patients recorded twice with the same mood label resulted in a very small number of significant differences . In particular a very good specificity was highlighted for the Taylor inspired features and for a subset of the second category of features thus strengthening the significance of the results obtained with patients . Even if the number of enrolled patients is small this work suggests that the proposed features might give a relevant contribution to the demanding research field of speech based mood classifiers . Moreover the results here presented indicate that a model of speech changes in bipolar patients might be subject specific and that a richer characterization of subject status could be necessary to explain the observed variability . 


Summary: We present an automatic method for estimating features describing speech F0 contour. Analysis on acted emotional speeches distinguished high from low arousal emotions. Intra subject analysis on bipolar patients found out differences between mood states. The results on bipolar patients are subject specific and task dependent. Results on controls confirmed a good specificity of the proposed features.
Muscle fiber conduction velocity can be measured by estimating the time delay between surface EMG signals recorded by electrodes aligned with the fiber direction . In the case of dynamic contractions the EMG signal is highly non stationary and the time delay between recording sites may vary rapidly over time . Thus the processing methods usually applied in the case of static contractions do not hold anymore and the delay estimation requires processing techniques that are adapted to non stationary conditions . The current paper investigates several methods based on time frequency approaches or adaptive filtering in order to solve the time varying delay estimation problem . These approaches are theoretically analyzed and compared by Monte Carlo simulations in order to determine if their performance is sufficient for practical applications . Moreover results obtained on experimental signals recorded during cycling from the vastus medialis muscle are also shown . The study presents for the first time a set of approaches for instantaneous delay estimation from two channels EMG signals . 


Summary: Muscle fiber conduction velocity estimators during dynamic exercises are proposed. Time varying delay estimators are compared by Monte Carlo simulations. TFTS methods are robust to noise and accurate for the conduction velocity tracking.
Fatty liver or steatosis is a pathology characterized by fat accumulation in the liver cells . Ultrasound is the most common technique used for its evaluation however the diagnosis is strongly dependent on the physician s expertise and system settings . These drawbacks have motivated the development of procedures for the quantitative analysis of ultrasound images to help the steatosis diagnosis . In this work three approaches are presented and tested with human liver images . The first one addresses textural analysis of the hepatic parenchyma using five classifiers 357 features a feature selector and classifiers fusion . Its performance is measured by two parameters accuracy and area under the ROC curve . The second makes use of the hepatorenal coefficient followed by a statistical analysis to discriminate echogenicity differences between liver and kidney . The third is based on the acoustical attenuation coefficient evaluated over a line traced in the images with parallel orientation to the acoustical beam . The use of classifiers fusion has provided better results when compared with the performance of the best one considered alone . The hepatorenal coefficient proved to be a good parameter for steatosis detection with calculated sensitivity and specificity of 0.90 and 0.88 respectively . It was observed the hepatorenal coefficient is not influenced by the ultrasound machine parameters . The attenuation coefficient provided lower sensitivity and specificity values than the ones from the hepatorenal coefficient . 


Summary: Identification of steatosis through three distinct approaches. We show that it is possible to detect fatty liver using classifiers with a reduced number of features. We prove that the hepatorenal coefficient and the attenuation coefficient are capable to successfully discriminate steatotic from normal livers. Our methodologies could be also used to detect the degrees of steatosis useful for analyzing its evolution over time.
When multiple acquisition systems are used to simultaneously acquire signals synchronization issues may arise potentially causing errors in the determination of acquisition starting points and continuous clock offsets and shifts on each device . This paper introduces a processing method to efficiently synchronize these signals in the presence of white noise sources without the requirement of clock sharing or any other digital line exchange . The use of a signal source such as white noise with a very wide frequency band is of great interest for synchronization purposes due to its aperiodic nature . This high bandwidth signal is simultaneously acquired by all the acquisition channels on distinct systems and synchronized afterwards using cross correlation methods . Two different correlation methods were tested a global method used when clock system frequencies are exactly known and a local method used when independent clocks evidence shifts over time that cumulatively account for long term acquisition errors in the synchronization process . In a computational simulation with known clock frequencies the results show a synchronization error of 1 10 of the time resolution for both methods . For unknown clock frequencies the global method achieved an error of 24 10 the time resolution indicating a much poorer performance . In the experimental set up only the local method was tested . The best result shows a synchronization error of 4 10 of the time resolution . All the signal conditioning and acquisition parameters were chosen taking into account potential biomedical applications . 


Summary: Motivation Synchronize biomedical signals from several acquisition systems is a difficult task. Methodology Use of white noise and correlation methods to synchronize biomedical signals. Results Synchronization with an error of 0.2ms in an experimental case. Conclusions This is a reliable and flexible method for several biomedical acquisition systems.
This review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces . The major benefits and challenges of myoelectric interfaces are evaluated . The article aims to fill gaps left by previous reviews and identify avenues for future research . Recommendations are given for example for electrode placement sampling rate segmentation and classifiers . Four groups of applications where myoelectric interfaces have been adopted are identified assistive technology rehabilitation technology input devices and silent speech interfaces . The state of the art applications in each of these groups are presented . 


Summary: Critical issues and practices for myoelectric interfaces are discussed. Robust classification in real use is the main challenge of sEMG interfaces. Expected trend is toward regression and factorization methods and sensor fusion. Simplified sEMG setup may be adequate in real life environment. Potential uses of sEMG interfaces are rapidly increasing.
A minimal recruitment model can be used to guide mechanical ventilation PEEP selection for acute respiratory distress syndrome patients . However implementation of this model requires a specific clinical protocol and is computationally expensive and thus not suitable for bedside application . This work aims to improve the performance and bedside utility of the minimal recruitment model through simplifying the model and improving the identification algorithm without compromising the model s physiological relevance to the disease . Identifying the model requires fitting of 8 unique parameters to pressure volume data at multiple levels of positive end expiratory pressure . A minimal algorithm is proposed to improve the model s performance . The algorithm utilises a non linear least squares solver to estimate a global set of the parameters to a pressure volume curve at one PEEP level . These global parameters were then subsequently used to estimate other parameters at different pressure volume curves . The accuracy and computational performance of the minimal algorithm is compared to the grid search algorithm for 2 ARDS patient cohorts . The median absolute percentage curve fitting error over all patients for grid search algorithm is 1.40 and for the minimal algorithm is 2.43 . The median computational time for all patients for the grid search algorithm is 394.51s and for the minimal algorithm is 0.72s where a 600 of reduction of computational time was found for the minimal algorithm . The estimated parameters using the minimal algorithm are correlated with the grid search algorithm with median person s correlation of R 0.9 . The model fitting error for the minimal algorithm is higher than the grid search algorithm . However the model was able capture similar trends in physiologically relevant parameters without the loss of important clinical information . The minimal algorithm is less computationally intensive than the grid search algorithm whilst still providing a means of selecting PEEP with only a small increase in model fitting error . The minimal algorithm is able to improve computational performance while maintaining the ability to capture physiological parameters as the grid search algorithm . The significant reduction in computational time encourages its clinical application at the bedside for decision making . 


Summary: A minimal recruitment model which captures alveolar opening and closing pressures is presented. The model is computationally intense and not suitable for bedside application. The model is simplified while maintaining its physiological relevance. A new algorithm is used to improve computational performance.