nils-herrmann/clean_scirepeval huggingface dataset:
Blind source separation for ambulatory sleep recording

This paper deals with the conception of a new system for sleep staging in ambulatory conditions. Sleep recording is performed by means of five electrodes: two temporal, two frontal and a reference. This configuration enables to avoid the chin area to enhance the quality of the muscular signal and the hair region for patient convenience. The electroencephalopgram (EEG), eletromyogram (EMG), and elctrooculogram (EOG) signals are separated using the Independent Component Analysis approach. The system is compared to a standard sleep analysis system using polysomnographic recordings of 14 patients. The overall concordance of 67.2% is achieved between the two systems. Based on the validation results and the computational efficiency we recommend the clinical use of the proposed system in a commercial sleep analysis platform
Comparison of two estimators of time-frequency interdependencies between nonstationary signals: application to epileptic EEG

Numerous works have been dedicated to the development of signal processing methods aimed at measuring the degree of association between EEG signals. This interdependency parameter is often used to characterize the functional coupling between different brain structures or regions during either normal or pathological processes. In this paper we focus on the time-frequency characterization of interdependencies between nonstationary signals. Particularly, we propose a novel estimator based on the cross correlation of narrow band filtered signals. In a simulation framework, results show that this estimator may exhibit higher statistical performances (bias and variance) compared to a more classical estimator based on the coherence function. On real data (intracerebral EEG signals), they show that this estimator enhances the readability of the time-frequency representation of the relationship and can thus improve the interpretation of nonstationary interdependencies in EEG signals. Finally, we illustrate the importance of characterizing the relationship in both time and frequency domains by comparing with frequency-independent methods (linear and nonlinear).
Time-frequency characterization of interdependencies in nonstationary signals: application to epileptic EEG

For the past decades, numerous works have been dedicated to the development of signal processing methods aimed at measuring the degree of association between electroencephalographic (EEG) signals. This interdependency parameter, which may be defined in various ways, is often used to characterize a functional coupling between different brain structures or regions during either normal or pathological processes. In this paper, we focus on the time-frequency characterization of the interdependency between signals. Particularly, we propose a novel estimator of the linear relationship between nonstationary signals based on the cross correlation of narrow band filtered signals. This estimator is compared to a more classical estimator based on the coherence function. In a simulation framework, results show that it may exhibit better statistical performances (bias and variance or mean square error) when a priori knowledge about time delay between signals is available. On real data (intracerebral EEG signals), results show that this estimator may also enhance the readability of the time-frequency representation of relationship and, thus, can improve the interpretation of nonstationary interdependencies in EEG signals. Finally, we illustrate the importance of characterizing the relationship in both time and frequency domains by comparing with frequency-independent methods (linear and nonlinear).
Radix-3$\,\times\,$3 Algorithm for The 2-D Discrete Hartley Transform

In this correspondence, we propose a vector-radix algorithm for the fast computation of a 2-D discrete Hartley transform (DHT). For data sequences whose length is a power of three, a radix-3 times 3 decimation in frequency algorithm is developed. It decomposes a length-N times N DHT into nine length-(N/3) times N (N/3) DHTs. Comparison of the computational complexity with known algorithms shows that the proposed algorithm, in some cases, reduces significantly the number of arithmetic operations.
Application of the matching pursuit method for the analysis and synthesis of the phonocardiogram

The matching pursuit method of Mallat and Zhang (1993) is applied to the analysis and synthesis of phonocardiograms (PCGs) using a Gabor wavelet or time-frequency atom which is the product of a sinusoid and a Gaussian window function. It decomposes a signal into a series of time-frequency atoms by an iterative process based on selecting the largest inner product of the signal (and the subsequent residues) with atoms from a redundant dictionary. The method was applied to a data base which includes eleven PCGs representing the normal and pathological conditions of the heart. The normalized root-mean-square error (NRMSE) was computed between the original and the reconstructed PCGs. The results show that the matching pursuit method is very suitable to the transient and complex properties of the PCGs, as it yielded excellent NRMSEs around 2.2 % for the eleven PCGs tested.
Field return on a Chinese-French double graduation of an international master in electronics and telecommunications on the base of the Bologna process

With the decrease in France of the number of students in the field of electrical engineering and information processing, that is unfortunately more and more common in Europe, several French Institutions are trying to build remote formations in Asia-Pacific region. This paper deals with the setup of a new master degree in the field of electrical and information engineering with two specialties, microelectronics and signal and image processing. It is a double diploma between South-East University (SEU) at Nanjing (China) and the University of Rennes 1, at Rennes (France), fully compatible with the Bologna process rules more especially in terms of ECTS. In this paper, some details on the structure and on the specific points that were solved are described and commented. In particular, the pedagogical program was built on the bases of the reciprocal competencies in order to take at the maximum the benefit of skills available on each side.
Localization of spatially distributed brain sources after a tensor-based preprocessing of interictal epileptic EEG data

This paper addresses the localization of spatially distributed sources from interictal epileptic electroencephalographic data after a tensor-based preprocessing. Justifying the Canonical Polyadic (CP) model of the space-time-frequency and space-time-wave-vector tensors is not an easy task when two or more extended sources have to be localized. On the other hand, the occurrence of several amplitude modulated spikes originating from the same epileptic region can be used to build a space-time-spike tensor from the EEG data. While the CP model of this tensor appears more justified, the exact computation of its loading matrices can be limited by the presence of highly correlated sources or/and a strong background noise. An efficient extended source localization scheme after the tensor-based preprocessing has then to be set up. Different strategies are thus investigated and compared on realistic simulated data: the “disk algorithm” using a precomputed dictionary of circular patches, a standardized Tikhonov regularization and a fused LASSO scheme.
An Alternating Direction Method of Multipliers for constrained joint diagonalization by congruence

In this paper, we address the problem of joint diagonalization by congruence (i.e. the canonical polyadic decomposition of semi-symmetric 3rd order tensors) subject to arbitrary convex constraints. Sufficient conditions for the existence of a solution are given. An efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) is then designed. ADMM provides an elegant approach for handling the additional constraint terms, while taking advantage of the structure of the objective function. Numerical tests on simulated matrices show the benefits of the proposed method for low signal to noise ratios. Simulations in the context of nuclear magnetic resonance spectroscopy are also provided.
Time-frequency scaling transformation of the phonocardiogram based of the matching pursuit method

A time-frequency scaling transformation based on the matching pursuit (MP) method is developed for the phonocardiogram (PCG). The MP method decomposes a signal into a series of time-frequency atoms by using an iterative process. The modification of the time scale of the PCG can be performed without perceptible change in its spectral characteristics. It is also possible to modify the frequency scale without changing the temporal properties. The technique has been tested on 11 PCGs containing heart sounds and different murmurs. A scaling/inverse-scaling procedure was used for quantitative evaluation of the scaling performance. Both the spectrogram and a MP-based Wigner distribution were used for visual comparison in the time-frequency domain. The results showed that the technique is suitable and effective for the time-frequency scale transformation of both the transient property of the heart sounds and the more complex random property of the murmurs. It is also shown that the effectiveness of the method is strongly related to the optimization of the parameters used for the decomposition of the signals.
A dynamical model for generating synthetic Phonocardiogram signals

In this paper we introduce a dynamical model for Phonocardiogram (PCG) signal which is capable of generating realistic synthetic PCG signals. This model is based on PCG morphology and consists of three ordinary differential equations and can represent various morphologies of normal PCG signals. Beat-to-beat variation in PCG morphology is significant so model parameters vary from beat to beat. This model is inspired of Electrocardiogram (ECG) dynamical model proposed by McSharry et al. and can be employed to assess biomedical signal processing techniques.
Sliding conjugate symmetric sequency-ordered complex hadamard transform: Fast algorithm and applications

This paper presents a fast algorithm for the computation of forward and backward sliding conjugate symmetric se-quency-ordered complex Hadamard transform (CSSCHT). The forward CSSCHT algorithm calculates the values of window i+N/4 from those of window i and one length-N/4 CSSCHT, one length-N/4 Walsh Hadamard transform (WHT) and one length-N/4 modified WHT. The backward CSSCHT algorithm can be obtained by transposing the signal flow graph of that of the forward one. The proposed algorithm requires O(N) arithmetic operations, which is more efficient than the block-based algorithm and those based on the sliding FFT and the sliding DFT. The applications of the sliding CSSCHT in spectrum estimation and transform domain adaptive filtering (TDAF) are also provided with supporting simulation results.
Multilinear Principal Component Analysis Network for Tensor Object Classification

The recently proposed principal component analysis network (PCANet) has performed well with respect to the classification of 2-D images. However, feature extraction may perform less well when dealing with multi-dimensional images, since the spatial relationships within the structures of the images are not fully utilized. In this paper, we develop a multilinear principal component analysis network (MPCANet), which is a tensor extension of PCANet, to extract the high-level semantic features from multi-dimensional images. The extracted features largely minimize the intraclass invariance of tensor objects by making efficient use of spatial relationships within multi-dimensional images. The proposed MPCANet outperforms traditional methods on a benchmark composed of three data sets, including the UCF sports action database, the UCF11 database, and a medical image database. It is shown that even a simple one-layer MPCANet may outperform a two-layer PCANet.
Signal processing in prolonged EEG recordings during intensive care

Methods for analyzing and displaying EEG signals are discussed. The increasing availability and affordability of powerful computer equipment makes possible the use of ever more sophisticated signal processing techniques, which extract relevant (but not readily discernible) information from long-term EEG recordings and can easily identify important features in the EEG. Whether these techniques are actually taken up in clinical practice is heavily dependent on how well they match clinical requirements. This article concentrates on requirements set in the context of long-term recordings in the ICU that demand the ability to process short-term discrete events as well as long-term trend information. A huge range of potentially useful signal processing techniques exists. This article illustrates the value of some of these techniques for ICU signals using the EEG recordings collected during the IMPROVE project.
Fast Algorithms for the Computation of Sliding Sequency-Ordered Complex Hadamard Transform

Fast algorithms for computing the forward and inverse sequency-ordered complex Hadamard transforms (SCHT) in a sliding window are presented. The first algorithm consists of decomposing a length- N inverse SCHT (ISCHT) into two length-N/2 ISCHTs. The second algorithm, calculating the values of window i + N/4 from those of window i and one length-N/4 ISCHT and one length-N/4 Modified ISCHT (MISCHT), is implemented by two schemes to achieve a good compromise between the computation complexity and the implementation complexity. The forward SCHT algorithm can be obtained by transposing the signal flow graph of the ISCHT. The proposed algorithms require O(N) arithmetic operations and thus are more efficient than the block-based algorithms as well as those based on the sliding FFT or the sliding DFT. The application of the sliding ISCHT in transform domain adaptive filtering (TDAF) is also discussed with supporting simulation results.
Optimal Algorithm Switching for the Estimation of Systole Period From Cardiac Microacceleration Signals (SonR)

Previous studies have shown that cardiac microacceleration signals, recorded either cutaneously, or embedded into the tip of an endocardial pacing lead, provide meaningful information to characterize the cardiac mechanical function. This information may be useful to personalize and optimize the cardiac resynchronization therapy, delivered by a biventricular pacemaker, for patients suffering from chronic heart failure (HF). This paper focuses on the improvement of a previously proposed method for the estimation of the systole period from a signal acquired with a cardiac microaccelerometer (SonR sensor, Sorin CRM SAS, France). We propose an optimal algorithm switching approach, to dynamically select the best configuration of the estimation method, as a function of different control variables, such as the signal-to-noise ratio or heart rate. This method was evaluated on a database containing recordings from 31 patients suffering from chronic HF and implanted with a biventricular pacemaker, for which various cardiac pacing configurations were tested. Ultrasound measurements of the systole period were used as a reference and the improved method was compared with the original estimator. A reduction of 11% on the absolute estimation error was obtained for the systole period with the proposed algorithm switching approach.
Wavelets extrema representation for QRS-T cancellation and P wave detection

P wave detection requires a robust QRS-T cancellation method. Interesting algorithms have been proposed for beat-to-beat QRS-T cancellation. Previous studies have shown that adaptive methods lead to good cancellation of the QRS-T interval which generally guarantees the performance of P wave detection. However, adaptive methods suffer from the nonstationary behavior of ECG signals and particularly beat-to-beat morphology changes of the QRS. We present a new approach for two ECG channel QRS-T cancellation based on the dyadic wavelet transform. The method is insensitive to QRS morphology changes and performs well in the presence of ectopic beats, transient artifacts, baseline drifts and isolated P waves. Our approach allows the P wave to be enhanced better than methods recently proposed.
Detection and classification of fast ripples using wavelets

Fast ripples (FRs) are hypothesized to be a biomarker of epileptogenic processes. In this communication, we introduce a two-step procedure for automatically detecting and classifying FRs. In the first step, we detect all events of interest (EOIs) in the frequency band ranging from 250 Hz to 600 Hz. Then, based on wavelet transform, a local energy vs frequency analysis is performed to assign each EOIs to a specific class: FRs, interictal epileptic spikes (IESs), and artifact. The results obtained in the context of real depth-EEG signals (human and animal) show high performance in term of sensitivity and specificity.
Canonical decomposition of semi-symmetric semi-nonnegative three-way arrays

In this paper, we focus on a special case of canonical decomposition, say the canonical decomposition of three-way arrays, which have two equal and nonnegative loading matrices. This problem has a great interest in blind source separation, particularly in magnetic resonance spectroscopy, where each observation spectrum is a nonnegative linear combination of different constituent spectra. In order to achieve the semi-symmetric semi-nonnegative canonical decomposition of a given three-way array, we propose a novel technique named ELS-ALSsym+, which optimizes an unconstrained problem obtained by means of a square change of variable. The method is compared with a Levenberg-Marquardt-like approach recently submitted for publication in Linear Algebra and its applications and classical methods, which uses no a priori about the considered array, such as the ELS-ALS technique. Such a comparison is made in terms of performance and numerical complexity on random synthetic arrays.
Inserting the Effects of Ion Channels in Mean Field Models: Application to generation of anesthetic slow waves

In this paper, effects of general anesthesia on the electroencephalogram (EEG) have been modeled with an enhanced physiological mean field theory of electrocortical activity. Enhancement is done by inserting two intrinsic ion channels (IKNa and I AR) in Lilly's mean field model. In addition to excitatory and inhibitory synapses, intrinsic ion channels can generate or manipulate the brain rhythms. IKNa and IAR can produce slow brain rhythms (delta band frequency) in deep levels of anesthesia. We represent the activities of each mentioned ion channels by cascading a nonlinear function and a first order low pass filter. Linearized and numerical solutions of the modified model show that the power spectrum densities of simulated EEG signals are more closed to reality
Fractional Wavelet Scattering Network and Applications

This study introduces a fractional wavelet scattering network (FrScatNet), which is a generalized translation invariant version of the classical wavelet scattering network.  In our approach, the FrScatNet is constructed based on the fractional wavelet transform (FRWT). The fractional scattering coefficients are iteratively computed using FRWTs and modulus operators. The feature vectors constructed by fractional scattering coefficients are usually used for signal classification. In this paper, an application example of the FrScatNet is provided in order to assess its performance on pathological images. First, the FrScatNet extracts feature vectors from patches of the original histological images under different orders. Then we classify those patches into target (benign or malignant) and background groups. And the FrScatNet property is analyzed by comparing error rates computed from different fractional orders, respectively. Based on the above pathological image classification, a gland segmentation algorithm is proposed by combining the boundary information and the gland location.  The error rates for different fractional orders of FrScatNet are examined and show that the classification accuracy is improved in fractional scattering domain. We also compare the FrScatNet-based gland segmentation method with those proposed in the 2015 MICCAI Gland Segmentation Challenge and our method achieves comparable results.  The FrScatNet is shown to achieve accurate and robust results. More stable and discriminative fractional scattering coefficients are obtained by the FrScatNet in this paper.  The added fractional order parameter is able to analyze the image in the fractional scattering domain.
Extraction and separation of eyes movements and the muscular tonus from a restricted number of electrodes using the independent component analysis

Up to now the diagnosis of sleep apnea syndrome (SAS) is based on the analysis of a polysomnogram. This latter however is costly and uncomfortable for the patient. Therefore, we are working on the elaboration of a small data acquisition system. This communication focuses on a method for recovering from a reduced number of sensors informative signals to establish a diagnostic. The proposed method makes use of the independent component analysis (ICA). Several methods of ICA were applied on electroencephalogram (EEG) and magnetoencephalogram (MEG) signals for artifacts removal. On the contrary to previous works, ICA method in this paper is shown to extract separately the EEG, the electroocculogram (EOG) and the muscular activity (EMG) from a very restricted number of electrodes. Moreover, since we are confronted with an underdetermined problem in ICA (less observations than sources), increasing the number of mixing observations by filtering the signals issued from the restricted channels is demonstrated to be an efficient solution.
Analysis-synthesis of the phonocardiogram based on the matching pursuit method

The matching pursuit method of Mallat and Zhang (1993) is applied to the analysis and synthesis of phonocardiograms (PCGs). The method is based on a classical Gabor wavelet or time-frequency atom which is the product of a sinusoid and a Gaussian window function, it decomposes a signal into a series of time-frequency atoms by an iterative process based on selecting the largest inner product of the signal (and the subsequent residues) with atoms from a redundant dictionary. The Gaussian window controls the envelope duration and time position of each atom; and the sinusoid represents the frequency. The method was applied to two sets of PCGs: one with very low-noise level and the other with 10% noise energy. Each database includes 11 PCGs representing the normal and the pathological conditions of the heart. The normalized root-mean-square error (NRMSE) was computed between the original and the reconstructed signals. The results show that the matching pursuit method is very suitable to the transient and complex properties of the PCGs, as it yielded excellent NRMSEs around 2.2% for the two sets of 11 PCGs tested.
Modified signal dependent time-frequency representation for analysis of the simulated first heart sound

Describes a modification of the Signal Dependent Time-Frequency Representation (SDTFR) developed by Baraniuk and Jones (1993) in order to applying it to a simulated first heart sound signal composed of a rising chirp and two damped sinusoid components. This modification is performed by introducing an additional constraint on the design of the kernel to emphasize a specific section of the ambiguity plane. The result of this improved method is compared to those obtained by the SDTFR and the Wigner-Ville distribution of the simulated S1.