Analysis of the postural stabilization in the upright stance using optimization properties
This study investigated the whole-body stability dynamics during upright stance by applying a set of variables characterizing the mathematical optimization process that minimizes the postural sway as a cost-function.MethodThe downhill, stability, and convergence properties of postural sway were quantified by implementing a direct pattern search of downhill epochs, and locally and globally optimum valleys using the center-o-pressure (CoP) displacement vector as cost-function. The proposed pattern search was tested using a computational simulation of nine benchmark functions and subsequently applied to posturography data of ...
Source: Biomedical Signal Processing and Control - April 25, 2019 Category: Biomedical Science Source Type: research

Multifractal characterization of healing process after bone loss
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Marta Borowska, Ewelina Bębas, Janusz Szarmach, Edward OczeretkoAbstractObjectiveIn this work we have proposed to use multifractal analysis to evaluate the effectiveness of the healing process in postresectal and postcystal bone loss using the guided bone regeneration (GBR).MethodsThe material of the study consists of 19 radiographic images obtained from patients (13 females and 6 males), observed within 1-year-long period, who had undergone bone augmentation with xenogenic material. Using radiographic images (RVG) made with d...
Source: Biomedical Signal Processing and Control - April 25, 2019 Category: Biomedical Science Source Type: research

Synchrosqueezing transform based feature extraction from EEG signals for emotional state prediction
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Pinar Ozel, Aydin Akan, Bulent YilmazAbstractThis paper presents a novel method for emotion recognition based on time-frequency analysis using multivariate synchrosqueezing transform (MSST) of multichannel electroencephalography (EEG) signals. With the advancements of the multichannel sensor applications, the need for multivariate algorithms has become obvious for extracting features that stem from multichannel dependency in addition to mono-channel features. In order to model the joint oscillatory structure of these multichann...
Source: Biomedical Signal Processing and Control - April 25, 2019 Category: Biomedical Science Source Type: research

Digital contraceptives based on basal body temperature measurements
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Peter Händel, Johan WahlströmAbstractDigital contraceptives and fertility awareness products are currently offered as convenient smartphone applications. The first legitimate contraceptive smartphone app was recently introduced on the European market, with the digital processing based on measurements of the female user's basal body temperature (BBT). According to recent pilot market data, at some Swedish hospitals, up to 5–10% of women seeking abortion had become involuntarily pregnant while using the product. T...
Source: Biomedical Signal Processing and Control - April 25, 2019 Category: Biomedical Science Source Type: research

Medical gesture recognition using dynamic arc length warping
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Jenny Cifuentes, Minh Tu Pham, Richard Moreau, Pierre Boulanger, Flavio PrietoAbstractHand gesture recognition is a promising research area often used in applications of human–computer interactions in the medical field. In this paper, we present a novel approach to differentiate gestures based on an arc-length parametrization and a curvature analysis of 3D trajectories. This new method called dynamic arc length warping (DALW) can outperform classic multi dimensional-dynamic time warping (MD-DTW) algorithm as it is invaria...
Source: Biomedical Signal Processing and Control - April 25, 2019 Category: Biomedical Science Source Type: research

Deep Learning Approach to Cardiovascular Disease Classification Employing Modified ECG Signal from Empirical Mode Decomposition
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Nahian Ibn Hasan, Arnab BhattacharjeeAbstractMultiple cardiovascular disease classification from Electrocardiogram (ECG) signal is necessary for efficient and fast remedial treatment of the patient. This paper presents a method to classify multiple heart diseases using one dimensional deep convolutional neural network (CNN) where a modified ECG signal is given as an input signal to the network. Each ECG signal is first decomposed through Empirical Mode Decomposition (EMD) and higher order Intrinsic Mode Functions (IMFs) are com...
Source: Biomedical Signal Processing and Control - April 20, 2019 Category: Biomedical Science Source Type: research

Kullback-Leibler distance and graph cuts based active contour model for local segmentation
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Wenyan Sun, Enqing DongAbstractGraph cuts based active contour model can obtain the global optimal solution and run faster. To achieve robust local segmentation of inhomogeneous images and improve operation efficiency, a modified Kullback-Leibler distance and graph cuts based active contour (KLD-GCBAC) model in the iterative narrow band frame is proposed in this paper. The dynamic narrow band is constructed between the morphological erosion of evolving curve and the preset outer boundary. In the boundary term of discrete energy...
Source: Biomedical Signal Processing and Control - April 19, 2019 Category: Biomedical Science Source Type: research

Multi-lead ECG signal analysis for myocardial infarction detection and localization through the mapping of Grassmannian and Euclidean features into a common Hilbert space
ConclusionsThe Experimental results presented in this paper show the superiority of the proposed methodology against a number of state-of-the-art approaches. The main advantage of the proposed approach is that it exploits better the intercorrelations between signals of different ECG leads, by extracting feature representations that lie in different geometrical spaces and contain complementary information with regard to the dynamics of signals. (Source: Biomedical Signal Processing and Control)
Source: Biomedical Signal Processing and Control - April 16, 2019 Category: Biomedical Science Source Type: research

Robust identification of unknown inputs in electrical stimulation of ex-vivo animal models
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Iván Salgado, Mariel Alfaro-Ponce, Oscar Camacho, Isaac ChairezAbstractThe non-parametric identification problem aims to estimate a suitable model based on the response produced by a given stimulus on an uncertain model. Complementary, input estimation considers a different problem where the model and the output are known. However, if neither model nor input is known, the identification problem seems to be more complicated. This is a major challenge in electrophysiological systems where the output signal can be measured,...
Source: Biomedical Signal Processing and Control - April 11, 2019 Category: Biomedical Science Source Type: research

EEG alpha rhythm detection on a portable device
This study explored two signal analysis techniques, Matching Pursuit (MP) and Fast Fourier Transform (FFT), for differentiation between two states, eyes open (EO) and eyes closed (EC), through the detection of EEG alpha activity obtained from seven scalp regions, using a portable EEG device. Subjects were ten healthy male volunteers. MP results generally reproduced the results from FFT analysis, and all methods performed well on the occipital region. However, there was better state discrimination with MP atom number, and MP atom number was the only variable that reached statistical significance on all locations under study...
Source: Biomedical Signal Processing and Control - April 10, 2019 Category: Biomedical Science Source Type: research

Wavelet-based computationally-efficient computer-aided characterization of liver steatosis using conventional B-mode ultrasound images
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Manar N. Amin, Muhammad A. Rushdi, Raghda N. Marzaban, Ayman Yosry, Kang Kim, Ahmed M. MahmoudAbstractHepatic steatosis occurs when lipids accumulate in the liver leading to steatohepatitis, which can evolve into cirrhosis and consequently may end with hepatocellular carcinoma. Several automatic classification algorithms have been proposed to detect liver diseases. However, some algorithms are manufacturer-dependent, while others require extensive calculations and consequently prolonged computational time. This may limit the de...
Source: Biomedical Signal Processing and Control - April 7, 2019 Category: Biomedical Science Source Type: research

Editorial Board
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): (Source: Biomedical Signal Processing and Control)
Source: Biomedical Signal Processing and Control - April 5, 2019 Category: Biomedical Science Source Type: research

Automatic staging model of heart failure based on deep learning
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Dengao Li, Xuemei Li, Jumin Zhao, Xiaohong BaiAbstractHeart failure (HF) is a disease that is harmful to human health. Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. To improve the diagnostic accuracy of HF staging, this study evaluates the performance of deep learning-based models on combined features for its categorization. We proposed a novel deep convo...
Source: Biomedical Signal Processing and Control - April 3, 2019 Category: Biomedical Science Source Type: research

Temporal super resolution of ultrasound images using compressive sensing
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Mina Hosseinpour, Hamid Behnam, Maryam ShojaeifardAbstractIncreasing the frame rate is a challenging problem for tracking the fast transient motions of the heart in ultrasound imaging with diagnostic goals. In this paper, compressive sensing (CS) is used for super temporal resolution. Compressive sensing is an acquisition method where only a few random samples of a signal are blindly measured, and the full signal is reconstructed under certain conditions. The proposed method uses spatial and temporal information of radio freque...
Source: Biomedical Signal Processing and Control - March 29, 2019 Category: Biomedical Science Source Type: research

Using the frequency signature to detect muscular activity in weak and noisy myoelectric signals
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Carmen D’Anna, Tiwana Varrecchia, Maurizio Schmid, Silvia ConfortoAbstractThe detection of muscular activity for signals characterized by low amplitude and low signal-to-noise ratio – weak and noisy – is a challenge in biomedical data processing. The aim of this paper is to introduce a method based only on the frequency characteristics of the weak and noisy EMG to detect muscular activity. The algorithm is window-based and consists of two processing steps: i) estimation of zero-crossings and mean instantaneous...
Source: Biomedical Signal Processing and Control - March 29, 2019 Category: Biomedical Science Source Type: research

Early identification of ischemic stroke in noncontrast computed tomography
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Guoqing Wu, Jixian Lin, Xi Chen, Zeju Li, Yuanyuan Wang, Jing Zhao, Jinhua YuAbstractEarly identification of stroke is critical for the treatment and subsequent recovery. Non-contrast computed tomography (ncCT) is a routinely employed imaging modality for stroke evaluation. However, the early identification of stroke in ncCT images is very difficult, since there are subtle differences between lesion and healthy tissue during the hyperacute phase. In this paper, an image patch classification-based method was developed to detect ...
Source: Biomedical Signal Processing and Control - March 28, 2019 Category: Biomedical Science Source Type: research

Cardiovascular response as a marker of environmental stress caused by variations in geomagnetic field and local weather
We report the results of a physiological study that include ECG analysis, capillary blood velocity (CBV) data, and blood pressure (BP) measurements obtained under conditions of modified external magnetic field (MF). Each of eight volunteers was sequentially exposed to MFs of three different types for 22 h. A Helmholtz-like MF exposure system was used. The system was specially designed for long-term exposures of human beings to static and low frequency MFs. The MF of the first type reproduced an initially recorded geomagnetic storm (GS). The MF inductions of the other two types were about 55 and 49 μT, which correspo...
Source: Biomedical Signal Processing and Control - March 27, 2019 Category: Biomedical Science Source Type: research

Are you afraid of heights and suitable for working at height?
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Hong Wang, Qiaoxiu Wang, Fo HuAbstractFear of highs is one of the most common phobias all around world. It could affect people’s life, work and health. Standing on high-altitude can lead to fear, anxiety or even panic to some people. In this paper, EEG method is creatively combined with VR technology to assess the severity of fear of heights. By doing time-frequency analysis, we found that alpha band (8–13 Hz) and high beta (20–30 Hz) are sensitive to fear of heights and frontal and parietotempor...
Source: Biomedical Signal Processing and Control - March 27, 2019 Category: Biomedical Science Source Type: research

Acoustic feedback cancellation in hearing aids using dual adaptive filtering and gain-controlled probe signal
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Muhammad Tahir Akhtar, Felix Albu, Akinori NishiharaAbstractIn this paper, we propose a probe signal-based adaptive filtering method for acoustic feedback cancellation (AFC) in hearing aids. The proposed method consists of two adaptive filters. The first adaptive filter is excited by the receiver (loudspeaker) signal, and uses the microphone signal as its desired response. The first adaptive filter shows a fast convergence speed, however, it may converge to a biased solution at the steady-state because its input and desired res...
Source: Biomedical Signal Processing and Control - March 27, 2019 Category: Biomedical Science Source Type: research

ADMM based low-rank and sparse matrix recovery method for sparse photoacoustic microscopy
Publication date: July 2019Source: Biomedical Signal Processing and Control, Volume 52Author(s): Ting Liu, Mingjian Sun, Yang Liu, Depeng Hu, Yiming Ma, Liyong Ma, Naizhang FengAbstractPhotoacoustic microscopy (PAM) has evolved into a new promising medical imaging tool available for both in vivo surficial and deep-tissue imaging with a high spatial resolution. However, the long data acquisition time has made real-time imaging highly challenging. This paper presents an Alternating Direction Method of Multipliers (ADMM) based low-rank and sparse matrix recovery method for a sparse optical-scanning PAM system to realize fast ...
Source: Biomedical Signal Processing and Control - March 27, 2019 Category: Biomedical Science Source Type: research

Supervised model for Cochleagram feature based fundamental heart sound identification
In this study, an acoustic feature based heart sound segmentation algorithm has been proposed for automatic identification of the fundamental heart sounds (FHS). Gammatone filter bank energy has been introduced to represent the heart sound distinctive features. A supervised artificial neural network (ANN) model is used to detect S1-S2 and non S1-S2 segments of the cardiac cycle. Finally time based information is utilized to identify S1 and S2 positions. Performance of the system is evaluated using 764 real and noisy heart sound cycles (both normal and abnormal domains) from the 2016 PhysioNet/CinC challenge database with a...
Source: Biomedical Signal Processing and Control - March 27, 2019 Category: Biomedical Science Source Type: research

Comparison of brain effective connectivity in different states of attention and consciousness based on EEG signals
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Masoomeh Rahimi, Mohammad Hassan Moradi, Farnaz GhassemiAbstractEvidence from recent studies on attention and consciousness has raised many questions about their inherent natures and the existence of a possible relationship between them. The main goal of this study is to investigate the significant difference among different states of attention and consciousness, through the connectivity method. These two cognitive phenomena have been studied in recent years with different approaches, but none of them has employed connectivity m...
Source: Biomedical Signal Processing and Control - March 22, 2019 Category: Biomedical Science Source Type: research

Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Md. Sayed Tanveer, Md. Kamrul HasanAbstractAlthough photoplethysmogram (PPG) and electrocardiogram (ECG) signals can be used to estimate blood pressure (BP) by extracting various features, the changes in morphological contours of both PPG and ECG signals due to various diseases of circulatory system and interaction of other physiological systems make the extraction of such features very difficult. In this work, we propose a waveform-based hierarchical Artificial Neural Network – Long Short Term Memory (ANN-LSTM) model for ...
Source: Biomedical Signal Processing and Control - March 21, 2019 Category: Biomedical Science Source Type: research

A transversal study of fundamental frequency contours in parkinsonian voices
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Pablo Rodríguez-Pérez, Rubén Fraile, Miguel García-Escrig, Nicolás Sáenz-Lechón, Juana M. Gutiérrez-Arriola, Víctor Osma-RuizAbstractA transversal study of the pitch variability of parkinsonian voices in read speech is presented. 30 patients suffering from Parkinson's disease (PD) and 32 healthy speakers were recorded while reading a text without voiceless phonemes. The fundamental frequency contours were calculated from the recordings, and the following measures wer...
Source: Biomedical Signal Processing and Control - March 18, 2019 Category: Biomedical Science Source Type: research

Empirical wavelet transform based photoacoustic spectral response technique for assessment of ex-vivo breast biopsy tissues
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Deblina Biswas, Abhijeet Gorey, George C.K Chen, Srivathsan Vasudevan, Nornam Sharma, Priyanka Bhagat, Satish PhatakAbstractEmpirical wavelet transform (EWT) based Photoacoustic spectral response technique is proposed for quantitative assessment of human breast masses (e.g. normal, benign and malignant) based on tissue mechano-biological property. EWT derived parameters such as spectral magnitude and total energy of mono frequency components were utilised for quantitative analysis. Spectral magnitude of second frequency componen...
Source: Biomedical Signal Processing and Control - March 16, 2019 Category: Biomedical Science Source Type: research

A simple realtime algorithm for automatic external defibrillator
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Somaye Abedini Khadar, Narges Tabatabaey-Mashadi, Gheshlaghi Mojtaba DaliriAbstractAutomatic External Defibrillator is a device that automatically diagnoses the heart rhythm which requires an electric shock. In general the signal processing unit of an AED aims to immediately identify the occurrence of ventricular fibrillation and ventricular tachycardia in a patient’s electrocardiograph signal. In this research, a quick and high-precision method is presented that identifies peaks and heart rate arrhythmias of the cardiac s...
Source: Biomedical Signal Processing and Control - March 16, 2019 Category: Biomedical Science Source Type: research

Toward a bio-inspired rehabilitation aid: sEMG-CPG approach for online generation of jaw trajectories for a chewing robot
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Hadi Kalani, Sahar Moghimi, Alireza AkbarzadehAbstractThe purpose of this study was to develop a bio-inspired masticatory robot that generates real-time trajectories, using surface electromyography signals (sEMG). We employed the central pattern generator (CPG) concept to generate smooth transitions from one chewing pattern to another during an exercise. Online changes in the recreated chewing patterns were provided based on the features extracted from the sEMG of the masticatory muscles of a tele-operator. The proposed method e...
Source: Biomedical Signal Processing and Control - March 16, 2019 Category: Biomedical Science Source Type: research

Training state and performance evaluation of working memory based on task-related EEG
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Hong Wang, Chengcheng Hua, Qiaoxiu Wang, Qiang Fu, Tenssay FetleworkAbstractThe working memory (WM) refers to the information maintaining and manipulation during a short period, and it is corresponding to human ability in many tasks. The correlation between EEG features and the training state of the subjects or their performance in WM tasks had been investigated by many researches. However, there was no research done on the comparison between the training and performance to investigate which one is more correlated with the EEG f...
Source: Biomedical Signal Processing and Control - March 16, 2019 Category: Biomedical Science Source Type: research

Suppression of acute and chronic mesial temporal epilepsy by contralateral sensing and closed-loop optogenetic stimulation with proportional-plus-off control
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Bing-Hong Lin, Ming-Shaung Ju, Chou-Ching K. LinAbstractA proportional-plus-off control of optogenetic stimulation is implemented using the sample entropy and frequency bands of contralateral depth EEG as the afferent signals for seizure detection and feedback control. The system is tested on mice with mesial temporal epilepsy, induced by lithium-pilocarpine injections. The hippocampus is photic stimulated through an optical fibre inserted using the stereotactic technique, and depth EEG from the hippocampus contralateral to the ...
Source: Biomedical Signal Processing and Control - March 16, 2019 Category: Biomedical Science Source Type: research

External lighting and sensing photoglottography: Characterization and MSePGG algorithm
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): A. Bouvet, A. Amelot, X. Pelorson, S. Maeda, A. Van HirtumAbstractContinuous observation of the time-varying glottal area lacks a direct, quantitative, non-invasive measurement method despite its relevance to study breathing, speech production, swallowing, etc. External photoglottography (ePGG) relies on external glottal transillumination and sensing, it is therefore suitable for non-invasive and continuous observation. Nevertheless, a formalized relationship between ePGG signal and glottal area is lacking. The current paper pro...
Source: Biomedical Signal Processing and Control - March 16, 2019 Category: Biomedical Science Source Type: research

P- and T-wave delineation in ECG signals using parametric mixture Gaussian and dynamic programming
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Achuth Rao MV, Prakhar Gupta, Prasanta Kumar GhoshAbstractDetection and tracking of the P- and T-waves are important issues in the analysis and interpretation of the ECG signals. This paper addresses the problem by using two mixture Gaussian function and the Dynamic programming. A key feature of the proposed algorithm is that it allows to incorporate the prior knowledge about the P/T wave location variations and robustness to errors in QRS detection. The proposed algorithm is evaluated on the annotated QT-database and compared a...
Source: Biomedical Signal Processing and Control - March 16, 2019 Category: Biomedical Science Source Type: research

Lossless electrocardiogram signal compression: A review of existing methods
ConclusionIt is hoped that this review will provide technology developers with invaluable insights, thus opening doors for wearables devices with clinically-relevant capabilities. (Source: Biomedical Signal Processing and Control)
Source: Biomedical Signal Processing and Control - March 16, 2019 Category: Biomedical Science Source Type: research

Benign and malignant classification of mammogram images based on deep learning
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Hua Li, Shasha Zhuang, Deng-ao Li, Jumin Zhao, Yanyun MaAbstractBreast cancer is one of the most common malignant tumors in women, which seriously affect women's physical and mental health and even threat to life. At present, mammography is an important criterion for doctors to diagnose breast cancer. However, due to the complex structure of mammogram images, it is relatively difficult for doctors to identify breast cancer features. At present, deep learning is the most mainstream image classification algorithm. Therefore, this ...
Source: Biomedical Signal Processing and Control - March 16, 2019 Category: Biomedical Science Source Type: research

Open-source Python software for analysis of 3D kinematics from quadrupedal animals
We present a Python software to address this need. It uses 2D coordinates from four cameras and DLT coefficients from the calibrated volume to generates 3D coordinates [1]. A method is presented to modify the knee and elbow joint positions in 3D. Then, kinematic features are extracted, and they are sorted in a time series format to plot a summary of a study. In addition, we generate videos from the tracked points, 3D reconstruction of the points, showing joint angles for eight joints, the location of animal on the belt, and the animal's speed on the belt. The software has been evaluated by eight trials to show the importan...
Source: Biomedical Signal Processing and Control - March 16, 2019 Category: Biomedical Science Source Type: research

Newborn jaundice determination by reflectance spectroscopy using multiple polynomial regression, neural network, and support vector regression
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Yunus Karamavuş, Mehmed ÖzkanAbstractDiffuse reflectance spectroscopy is a non-destructive method to obtain biochemical and physiological information by investigating the optical properties of skin. Transcutaneous bilirubin (TcB) measurement utilizes reflectance spectroscopy to determine the jaundice level in newborns. Although TcB measurement has some advantages over total serum bilirubin (TSB) measurement such as being non-invasive, noninfectious, painless, and instantaneous, the existing TcB devices cannot yet replace T...
Source: Biomedical Signal Processing and Control - March 8, 2019 Category: Biomedical Science Source Type: research

Semi-automated detection of polysomnographic REM sleep without atonia (RSWA) in REM sleep behavioral disorder
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Iva Milerska, Vaclav Kremen, Vaclav Gerla, Erik K. St Louis, Lenka LhotskaAbstractWe aimed at evaluating semi-automatic detection and quantification of polysomnographic REM sleep without atonia (RSWA). As basic requirements, we defined lower time demand, the possibility of comparison of several evaluations and ease of examination for neurologists. We focused on well-known primary processing of surface electromyographic signals and selected recordings that were free of technical artifacts that could compromise automated signal de...
Source: Biomedical Signal Processing and Control - March 8, 2019 Category: Biomedical Science Source Type: research

An algorithm for automatic detection of repeater F-waves and MUNE studies
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): N. Tuğrul Artuğ, N. Görkem Şirin, Emel Oğuz Akarsu, M. Baris Baslo, A. Emre ÖgeAbstractThe present study aims to develop an algorithm and software that automatically detects repeater F-waves which are very difficult to analyze when elicited as high number of recordings in motor unit number estimation studies. The main strategy of the study was to take the repeater F waves discriminated by the neurologist, from limited number of recordings, as the gold standard and to test the conformity of the results of the new au...
Source: Biomedical Signal Processing and Control - March 8, 2019 Category: Biomedical Science Source Type: research

A region of interest based approach for texture analysis of medical images in space–frequency domain
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Pyari Mohan Pradhan, Chun Hing Cheng, Joseph Ross MitchellAbstractThe two-dimensional S-transform (ST-2D) is a space–frequency representation (SFR) useful in image processing especially for texture analysis. The high computation time and inefficiency in computing the local spectrum at each pixel in an image motivated the authors to develop the two-dimensional fast time–frequency transform (FTFT-2D). However, in practice, the SFRs at individual pixels are seldom used for analysis. In most medical images, the spectral ...
Source: Biomedical Signal Processing and Control - March 8, 2019 Category: Biomedical Science Source Type: research

Cardiorespiratory profiling during simulated lunar mission using impedance pneumography
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Marcel Młyńczak, Agata Kołodziejczyk, Hubert Krysztofiak, Grzegorz Ambroszkiewicz, Marek Żyliński, Gerard CybulskiAbstractManned spaceflight requires research in diverse areas, including neuropsychology and human physiology. For these subjects, the Lunares Analog Research Station was established in Pila, Poland. It allows testing of crew members under space-like conditions. One experiment, Lunar Expedition I, was performed on a group of 6 analogue astronauts over 14 days. All were studied for their subjective perception of ...
Source: Biomedical Signal Processing and Control - March 8, 2019 Category: Biomedical Science Source Type: research

On the design of automatic voice condition analysis systems. Part I: Review of concepts and an insight to the state of the art
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): J.A. Gómez-García, L. Moro-Velázquez, J.I. Godino-LlorenteAbstractThis is the first of a two-part series devoted to review the current state of the art of automatic voice condition analysis systems. The goal of this paper is to provide to the scientific community and to newly comers to the field of automatic voice condition analysis a resource that presents introductory concepts, a categorisation of different aspects of voice pathology and a systematic literature review that describes the methodologies and m...
Source: Biomedical Signal Processing and Control - March 8, 2019 Category: Biomedical Science Source Type: research

Digital cardiotocography: What is the optimal sampling frequency?
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Sofia Romagnoli, Agnese Sbrollini, Luca Burattini, Ilaria Marcantoni, Micaela Morettini, Laura BurattiniAbstractCardiotocography (CTG) is the most popular prenatal diagnostic test for establishing fetal health and consists in simultaneous recording of fetal heart rate (FHR, bpm) and maternal uterine contraction (UC, mmHg) traces. Typically, FHR and UC traces are visually analyzed and interpreted by clinicians. Recently, software applications like CTG Analyzer have been developed to support visual CTG interpretation by making it ...
Source: Biomedical Signal Processing and Control - March 8, 2019 Category: Biomedical Science Source Type: research

Ultrasound tissue characterization based on the Lempel–Ziv complexity with application to breast lesion classification
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Tomasz Steifer, Marcin LewandowskiAbstractBuilding upon the recent successes in the application of information-theoretic concepts (e.g. Shannon entropy) in quantitative ultrasound, the authors propose a novel tissue characterization method based on the Lempel–Ziv complexity. In this procedure, standard ultrasound B-Mode images are mapped onto words over finite alphabets before the corresponding Lempel–Ziv complexity of ultrasound images is calculated. Such complexity metric may be used to differentiate between types ...
Source: Biomedical Signal Processing and Control - March 8, 2019 Category: Biomedical Science Source Type: research

Standard ECG lead I prospective estimation study from far-field bipolar leads on the left upper arm: A neural network approach
In this study, the feasibility of interpreting heart rhythms from far-field bipolar ECG arm-band lead recordings on the left-upper-arm (LUA), is evaluated in a clinical multichannel arm-ECG mapping database (N = 153 subjects) for the prospective development of long-term heart rhythm monitoring from comfortable arm wearable devices. A preliminary multivariable linear regression analysis on ECG chest Lead I from 10 selected far-field bipolar leads along the left arm, indicated that 3 of them in the LUA were relevant and worth evaluating in more detail from a heart rhythm information perspective.To derive a good and effective...
Source: Biomedical Signal Processing and Control - March 6, 2019 Category: Biomedical Science Source Type: research

Classification of melanoma based on feature similarity measurement for codebook learning in the bag-of-features model
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Kai Hu, Xiaorui Niu, Si Liu, Yuan Zhang, Chunhong Cao, Fen Xiao, Wanchun Yang, Xieping GaoAbstractBag-of-features (BoF) model based melanoma classification methods can effectively assist dermatologists to diagnose skin diseases. Codebook learning is a key step in the BoF model and the k-means clustering algorithm is often used to learn a codebook. However, the cluster centers generated by k-means algorithm are irresistibly attracted to the denser regions. This produces a suboptimal codebook in which most of the clusters are loca...
Source: Biomedical Signal Processing and Control - March 6, 2019 Category: Biomedical Science Source Type: research

Recycling cardiogenic artifacts in impedance pneumography
ConclusionsThe dsSST is suitable for generating useful hemodynamic information from the cardiogenic artifact in a single-channel IP. We propose that the usefulness of the dsSST as a recycling tool extends to other biomedical sensors exhibiting cardiogenic artifacts. (Source: Biomedical Signal Processing and Control)
Source: Biomedical Signal Processing and Control - March 6, 2019 Category: Biomedical Science Source Type: research

Comparison of video-based methods for respiration rhythm measurement
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): M. Mateu-Mateus, F. Guede-Fernández, V. Ferrer-Mileo, M.A. García-González, J. Ramos-Castro, M. Fernández-ChimenoAbstractThe aim of this work is to characterize the differences in the respiratory rhythm obtained through three video based methods by comparing the obtained respiratory signals with the one obtained with the gold standard method in adult population. The analysed methods are an RGB camera, a depth camera and a thermal camera while the gold standard is an inductive thorax plethysmography sy...
Source: Biomedical Signal Processing and Control - February 28, 2019 Category: Biomedical Science Source Type: research

Automated Optic Disc region location from fundus images: Using local multi-level thresholding, best channel selection, and an Intensity Profile Model
ConclusionsThe obtained results show that the proposed method is robust and achieves the maximum detection rate in all four compared databases, which demonstrates its effectiveness and suitability to be integrated into a complete prescreening system for early diagnosis of retinal diseases. Use of promising OD region location reduces processing area in about 40%. (Source: Biomedical Signal Processing and Control)
Source: Biomedical Signal Processing and Control - February 28, 2019 Category: Biomedical Science Source Type: research

Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Anmin Gong, Jianping Liu, Ling Lua, Gengrui Wu, Changhao Jiang, Yunfa FuAbstractLong-term professional sport training may cause the brain functional network of high-level athletes to differ significantly from that of non-athletes. To test this hypothesis, electroencephalograms (EEGs) from 20 high-level shooting athletes and 20 age- and gender-matched non-athletes are collected in an eyes-closed resting state. The frequency spectrum was divided into four bands according to the individual alpha frequency of each participant: delta...
Source: Biomedical Signal Processing and Control - February 26, 2019 Category: Biomedical Science Source Type: research

Smoking detection based on regularity analysis of hand to mouth gestures
This study describes a novel method to detect smoking events by monitoring the regularity of hand gestures. Here, the regularity of hand gestures was estimated from a one axis accelerometer worn on the wrist of the dominant hand. To quantify the regularity score, this paper applied a novel approach of unbiased autocorrelation to process the temporal sequence of hand gestures. The comparison of regularity score of smoking events with other activities substantiated that hand-to-mouth gestures are highly regular during smoking events and have the potential to detect smoking from among a plethora of daily activities. This hypo...
Source: Biomedical Signal Processing and Control - February 23, 2019 Category: Biomedical Science Source Type: research

Inter-patient heartbeat classification based on region feature extraction and ensemble classifier
Publication date: May 2019Source: Biomedical Signal Processing and Control, Volume 51Author(s): Haotian Shi, Haoren Wang, Fei Zhang, Yixiang Huang, Liqun Zhao, Chengliang LiuAbstractThe electrocardiogram (ECG) is an important tool for detecting arrhythmia. To solve the limitations of visual inspection, computer-aided diagnosis appears and grows rapidly. Most of the reported researches for heartbeat classification were based on intra-patient dataset. Moreover, existing inter-patient researches were usually conducted for superclasses of arrhythmia. To classify specific types of arrhythmia, this study proposed an inter-patien...
Source: Biomedical Signal Processing and Control - February 23, 2019 Category: Biomedical Science Source Type: research