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Background Epilepsy is one of the most common neurological disorders associated with disruption of brain activity. In the classification and detection of epileptic seizures, electroencephalography (EEG) measurements, which record the electrical activities of the brain, are frequently used. Empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) are recently developed methods used to decompose non-stationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). Our main objective in this study is to present a hybrid IMF selection method combining four different approaches (energy, correlation, power spectral distance, and statistical significance measures), and investigate the effect of selected IMFs extracted by EMD and EEMD on the classification. We have applied the proposed IMF selection approach on the classification of EEG signals recorded from epilepsy patients who are under treatment at our collaborator hospital. Multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then IMF selection was performed. Finally, time- and spectral-domain, and nonlinear features are extracted and feature sets are created for the classification. Results The maximum classification accuracies obtained using various combinations of IMFs were 94.56%, 95.63%, 96.8%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression classifiers, respectively, by using EMD analysis; whereas, the EEMD approach has provided maximum classification accuracies of 96.06%, 97%, 97%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression, respectively. Classification performance with the same features obtained using direct EEG signals instead of the decomposed IMFs was worse than the aforementioned 2 approaches for every combination. Conclusion Simulation results demonstrate that the proposed IMF selection approach affects the classification results. Also, EEMD provides a robust method for feature extraction from EEG signals in order to classify pre-seizure and seizure segments.

Amyotrophic lateral sclerosis (ALS) disease, also known as motor neuron disease, is a disease resulting from loss of motor nerve cells in the spinal cord and brain stem region at central nervous system. Researchers can’t find the reason of ALS for certain, however there are a wide variety of risk factors in respect of this disease. This disease has more than one risk factor. Researchers believe that it is resulted from a virus which leads to abnormality in immune system, spoils the structure of DNA and functioning of enzyme system, exhibiting neurotoxic properties. The signals coming to a single arm or leg muscle from upper and lower motor neurons are highly determinative in diagnosis of the disease, although there is not a specific test for diagnosing the ALS disease for certain. Doctors still conduct many tests even though the main symptoms of ALS are the signals coming to muscles. The developments related to gait analysis are used an auxiliary factor in diagnosis and analysis of ALS diseases. In this study, gait signals from control individuals and ALS diseases have been recorded by means of sensors sensitive to the strength under the foot. These signals’ time-amplitude, amplitude spectrum, phase spectrum, average value and variance changes have been analysed. As a result of these inspections, differences of ALS signals from control signals have been identified.

Unscented Kalman Filter (UKF) (Julier & Uhlmann, 1997) was developed as an improvement of Extended Kalman Filter (EKF) (Grewal & Andrews, 2001) for discrete-time filtering of the nonlinear dynamic systems. Comparison between different statistical approaches on the state and parameter estimation of the dynamic systems revealed that the performance of UKF is superior to EKF in many Kalman Filter (KF) applications (Chow et al., 2007); (Xiong et al., 2006); (Wan & Merwe, 2001); (Kandepu et al., 2008). Nonlinear dynamic systems with uncertain observations were often appeared in, for instance, communication systems (Wan & Merwe, 2001), medical systems (Polak & Mroczka, 2006) and machine learning (Chen, 2003). Medical systems, described by stochastic difference equations with measurement models including nonlinear and non-Gaussian components, are good candidates for the UKF analysis. Although there are many medical signal applications of Kalman Filters (KF); (Vauhkonen et al., 1998) and EKF (Avendano et al., 2006), some medical diagnostic and therapeutic measures are processed by UKF from indirect sensor measurements including statistical brain signal analysis to study cognitive brain functions by Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) (Brochwell et al., 2007), ECG model-based denoising (Sameni et al., 2007), medical image processing (Ijaz et al., 2008), and evoke potential analysis in the neuroscience. These works demonstrated that UKF can be considered as an effective framework for medical signal analysing, modelling and filtering. Also, it was shown that UKF is a promising alternative in a variety of applications’ domains including state and parameter estimation simultaneously which is dual estimation. Respiratory mechanics is the dynamic relationship between appropriate pressures and flows in the respiratory system and assessment of it is an important problem in the diagnosis and monitoring of respiratory disorders, especially of Chronic Obstructive Pulmonary Disease (COPD). The primarily goal on the determination of the respiratory mechanics is the computation, or estimation, of the respiratory parameters non-invasively, continuously, effectively and without any patient cooperation. Direct approach to this problem is the measurement of the mechanics by the lung catheter or the alveolar capsule (Bates & Lutchen, 2005). However, these direct measurement methods are invasive and not suitable for continuous monitoring. On the other hand, the studies revealed that analysis of pressure O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

The emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed to communicate brain signals with external systems and make predictions over emotional states. This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, Dominance, and Liking emotional states. Hence, a novel approach is proposed for emotion recognition with time series of multi-channel EEG signals from a Database for Emotion Analysis and Using Physiological Signals (DEAP). We propose a new approach to emotional state estimation utilizing CNN-based classification of multi-spectral topology images obtained from EEG signals. In contrast to most of the EEG-based approaches that eliminate spatial information of EEG signals, converting EEG signals into a sequence of multi-spectral topology images, temporal, spectral, and spatial information of EEG signals are preserved. The deep recurrent convolutional network is trained to learn important representations from a sequence of three-channel topographical images. We have achieved test accuracy of 90.62% for negative and positive Valence, 86.13% for high and low Arousal, 88.48% for high and low Dominance, and finally 86.23% for like–unlike. The evaluations of this method on emotion recognition problem revealed significant improvements in the classification accuracy when compared with other studies using deep neural networks (DNNs) and one-dimensional CNNs.

Obsesif Kompulsif Bozuklular (OKB) başka psikiyatrik belirtilerin de eşlik edebildiği nöropsikiyatrik bir hastalıktır. OKB için başlıca klinik değerlendirme kriteri, Yale-Brown Obsesyon Kompulsiyon Ölçeği (YBOKÖ) olsa da bunun yanı sıra, Hamilton Depresyon Değerlendirme Ölçeği (HDDÖ), Beck Anksiyete Ölçeği (BAÖ)de kullanılmaktadır. Bu çalışma da OKB değerlendirme ölçekleri ile EEG senkronizasyonu arasında ki ilişki incelenmiştir. EEG senkronizasyonunu belirlemek için Global Alan Senkronizasyonu yöntemi ile hesaplanan GAS indisi kullanılmıştır. 30 adet OKB hastasına ait GAS indisleri ile bu hastalara ait OKB Klinik Değerlendirme Ölçekleri, arasındaki ilişki Spearman-rho korelasyon yöntemi ile incelenmiştir. Analiz sonuçlarına göre alfa 1 frekans bandında ki GAS değerleri ile BAÖ arasında negatif ilişki bulunurken, teta, alfa 1 ve alfa 2 bantlarındaki GAS değerleri ile HDDÖ arasında, negatif ilişki tespit edilmiştir. Bu sonuçlar, OKB hastalarında anksiyete ve depresyon belirtilerinin senkronizasyon bozukluğuna bağlı olarak fonksiyonel bağlılıkta azalışa neden olduğunu göstermektedir. OKB’ye eşlik eden anksiyete ve depresyon şiddetindeki artışın, bilişsel işlem düzeyinde düşüşe neden olduğu görülmüştür.

Emotions play a significant role in daily life by encouraging the individual in the survival, decision making, guessing, and communication processes. Through emotions can be explained with the activation of anatomical structures in certain regions of brain with nervous system the emotions can be understood by electroencephalogram (EEG) signals. In order to recognize emotions, the signal processing techniques were applied to recorded signals using 32-channels EEG device from the subjects during listening audios. The Self-Assessment Manikin (SAM) form was filled by 23 subjects to evaluate their feelings based on three emotion states and recorded their answers by designed Graphical User Interface (GUI) monitored in front of the subjects. In signal processing stage, the EEG signals were segmented into segmented files by cutting stimulus intervals from recorded signal and decomposed to Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD) method. Then, most meaningful IMFs has been selected by analyzing Power Spectral Density (PSD) to extract statistical and entropy-based features and then, classification algorithm has been applied to obtain feature vector to categorize states of emotion consisting of valence, arousal, dominance dimensions. It is aimed to find most useful selected IMFs, most active channels related to emotion, best suitable features for each dimension of emotion. Finally, the percentage of performance accuracy has been calculated and the best accuracy of 81.74% is found in channels ranged in frontal lobe (1–12) for valence state, 72.15% in channel TP7 for arousal state, 74.57% in channels ranged in frontal lobe (1–12) for dominance state by combining different features.

© 2019 IEEE.Arrhythmia is irregular changes of normal heart rhythm and effective manual identifying of them require a lot of time and depends on experience of clinicians. This paper proposes deep learning-based novel 2-D convolutional neural network (CNN) approach for accurate classification of five different arrhythmia types. The performance of the proposed architecture is tested on Electrocardiogram (ECG) signals that are taken from MIT-BIH arrhythmia benchmark database. ECG signals was segmented into heartbeats and each of the heartbeats was converted into 2-D grayscale images as an input data for CNN structure. The accuracy of the proposed architecture was found as 97.42% on the training results revealed that the proposed 2-D CNN architecture with transformed 2-D ECG images can achieve highest accuracy without any preprocessing and feature extraction and feature selection stages for ECG signals.

Indocyanine green (ICG) provides an advantage in the imaging of deep tumors as it can reach deeper location without being absorbed in the upper layers of biological tissues in the wavelengths, which named “therapeutic window” in the tissue engineering. Unfortunately, rapid elimination and short-term stability in aqueous media limited its use as a fluorescence probe for the early detection of cancerous tissue. In this study, stabilization of ICG was performed by encapsulating ICG molecules into the biodegradable polymer composited with poly(l-lactic acid) and poly(?-caprolactone) via a simple one-step multiaxial electrospinning method. Different types of coaxial and triaxial structure groups were performed and compared with single polymer only groups. Confocal microscopy was used to image the encapsulated ICG (1 mg/mL) within electrospun nanofibers and in vitro ICG uptake by MIA PaCa-2 pancreatic cancer cells. Stability of encapsulated ICG is demonstrated by the in vitro sustainable release profile in PBS (pH = 4 and 7) up to 21 days. These results suggest the potential of the ability of internalization and accommodation of ICG into the pancreatic cell cytoplasm from in vitro implanted ICG-encapsulated multiaxial nanofiber mats. ICG-encapsulated multilayer nanofibers may be promising for the local sustained delivery system to eliminate loss of dosage caused by direct injection of ICG-loaded nanoparticles in systemic administration. © 2019 Wiley Periodicals, Inc.

© 2018 IEEE.One of the main causes of sudden deaths is heart disease. Early detection and treatment of cardiac arrhythmias prevent the problem from reaching sudden deaths. The purpose of this study is to develop an arrhythmia detection algorithm based on Empirical Mode Decomposition (EMD). This algorithm consists of four steps: Preprocessing, Empirical Mode Decomposition, feature extraction and classification. Six arrhythmia types were used for differentiate normal and arrhythmic signals obtained from the MIT-BIH Arrhythmia database. These are normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), paced beat and atrial premature beats (APB). Three different classifiers were used to classify ECG signals. The method achieves better result with accuracy of 87% using linear discriminant analysis (LDA) classifier for detection of normal and arrhythmic signals.

Denoising of broadband non--stationary signals is a challenging problem in communication systems. In this paper, we introduce a time-varying filter algorithm based on the discrete linear chirp transform (DLCT), which provides local signal decomposition in terms of linear chirps. The method relies on the ability of the DLCT for providing a sparse representation to a wide class of broadband signals. The performance of the proposed algorithm is compared with the discrete fractional Fourier transform (DFrFT) filtering algorithm. Simulation results show that the DLCT algorithm provides better performance than the DFrFT algorithm and consequently achieves high quality filtering., 6 pages, conference paper

WOS: 000419020400015, In this paper, subcarrier intensity modulation (SIM) is investigated for multiple-input multiple-output (MIMO) visible light communication (VLC) systems. A new modulation scheme called DC-aid SIM (DCA-SIM) is proposed for the spatial modulation (SM) transmission plan. Then, DCA-SIM is extended for multiple subcarrier case which is called DC-aid Multiple Subcarrier Modulation (DCA-MSM). Bit error rate (BER) performances of the considered system are analyzed for different MIMO schemes. The power efficiencies of DCA-SIM and DCA-MSM are shown in correlated MIMO VLC channels. The upper bound BER performances of the proposed models are obtained analytically for PSK and QAM modulation types in order to validate the simulation results. Additionally, the effect of power imbalance method on the performance of SIM is studied and remarkable power gains are obtained compared to the non-power imbalanced cases. In this work, Pulse amplitude modulation (PAM) and MSM-Index are used as benchmarks for single carrier and multiple carrier cases, respectively. And the results show that the proposed schemes outperform PAM and MSM-Index for considered single carrier and multiple carrier communication scenarios. (C) 2017 Elsevier B.V. All rights reserved.

This paper presents a simple and fast approach to find a minimum sampling frequency for multi-band signals. Instead of neighbor and boundary conditions, constraints on the sampling frequency were derived by using the geometric app­roach. Reformulation of the minimum sampling determination problem by using geometric approach enables to represent the problem as a basic inequality problem. Recursive algorithm was proposed to solve the constraints on the minimum sampling frequency. The proposed method was verified through numerical simulations in terms of the minimum sampling frequency and the computational efficiency by using 2-band and 3-band signals. Although the results illustrated the valid minimum sampling frequencies for the multi-band signals, due to the increase in the number of iterations, optimization approaches were recommended in the solution of the constraints on the minimum sampling frequency.

0000-0003-4236-3646 0000-0001-8894-5794 PubMed: 26078774 WOS:000355461600001 This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity. Istanbul UniversityIstanbul University [YADOP-6987, 36196, 38262, 42330, 35830] This work was supported by the Istanbul University Scientific Research Projects, Project numbers YADOP-6987, 36196, 38262, 42330, and 35830.

0000-0003-4236-3646 0000-0001-8894-5794 WOS:000356355100019 The Doppler effect is the preferred technique in fetal heart rate (FHR) monitoring devices. The main objective of the recent studies on the Doppler FHR has been to improve the accuracy. On the other hand, a reliable fetal heart simulator becomes essential for testing Doppler FHR monitoring devices. The motivation of this study is to design a reliable system that will be used to test Doppler FHR monitors. This device generates a similar Doppler frequency shift of fetal cardiac activity including the heart's wall and valve motions. The components of this system are basically a signal generator using a microcontroller and a modified relay. The relay is the most important and studied component that affects the Doppler frequency shift. Thus, the relay's contact distance and length as well as its closing and opening velocities have been modified to produce an appropriate Doppler shift to fetal cardiac motion. This device has been tested and its reliability has been proved. Research Fund of Istanbul UniversityIstanbul University [UDP-35119, UDP-25231] This work was partially supported by the Research Fund of Istanbul University, Project Numbers UDP-35119 and UDP-25231.

Hand hygiene (HH) compliance is known as the most significant factor to reduce the transmission of infection to patients in health care institutions. Thus, monitoring hand hygiene compliance has become an important tool to control the infection transmission. Most of the currently used HH monitoring devices and systems use battery powered badges or wristbands to control users. In this study, a user friendly, passive RF-ID based electronic system with monitoring capabilities was introduced. The system includes a wearable, passive RF-ID wristband, wall mounted dispenser and a software to measure the HH compliance rate. The technology laying behind the HH station is introduced and the advantages and drawbacks of the system is discussed.

Brain-computer interfaces (BCIs) provide a way to monitor and treat neurological diseases. An important application of BCIs is the monitoring and treatment of epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, symptomatic of abnormal, excessive or synchronous neuronal activity in the brain. BCIs contain an array of sensors that gather and transmit data under the constrains of low-power and minimal data transmission. Asynchronous sigma delta modulators (ASDMs) are considered an alternative to synchronous analog to digital conversion. ASDMs are non-linear feedback systems that enable time-encoding of analog signals, from which the signal can be reconstructed. An efficient reconstruction of time-encoded signals can be achieved using a prolate spheroidal waveform (PSW) projection. PSWs have finite time support and maximum energy concentration within a given bandwidth. The ASDM time-encoding is related to the duty-cycle modulation and demodulation, which shows that sampling is non-uniform. For transmission of data from BCI, we propose a modified orthogonal frequency division multiplexing (OFDM) technique using chirp modulation. Our method generalizes the chirp modulation of binary streams with non-uniform symbol duration. Our theoretical results relate to recent continuous-time digital signal processing and compressive sampling theories.