Fractional dynamical model for the generation of ECG like signals from filtered coupled Van-der Pol oscillators

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.In this paper, an incommensurate fractional order (FO) model has been proposed to generate ECG like waveforms. Earlier investigation of ECG like waveform generation is based on two identical Van-der Pol (VdP) family of oscillators, which are coupled by time delays and gains. In this paper, we suitably modify the three state equations corresponding to the nonlinear cross-product of states, time delay coupling of the two oscillators and low-pass filtering, using the concept of fractional derivatives. Our results show that a wide variety of ECG like waveforms can be simulated from the proposed generalized models, characterizing heart conditions under different physiological conditions. Such generalization of the modelling of ECG waveforms may be useful to understand the physiological process behind ECG signal generation in normal and abnormal heart conditions. Along with the proposed FO models, an optimization based approach is also presented to estimate the VdP oscillator parameters for representing a realistic ECG like signal.The work presented in this paper was supported by the E.U. ARTEMIS Joint Undertaking under the Cyclic and Person-Centric Health Management: Integrated appRoach for hOme, mobile and clinical eNvironments – (CHIRON) Project, Grant Agreement # 2009-1-100228

Control Strategy for Anaesthetic Drug Dosage with Interaction Among Human Physiological Organs Using Optimal Fractional Order PID Controller

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In this paper, an efficient control strategy for physiological interaction based anaesthetic drug infusion model is explored using the fractional order (FO) proportional integral derivative (PID) controllers. The dynamic model is composed of several human organs by considering the brain response to the anaesthetic drug as output and the drug infusion rate as the control input. Particle Swarm Optimisation (PSO) is employed to obtain the optimal set of parameters for PID/FOPID controller structures. With the proposed FOPID control scheme much less amount of drug-infusion system can be designed to attain a specific anaesthetic target and also shows high robustness for +/-50% parametric uncertainty in the patient's brain model

Artifact reduction in multichannel pervasive EEG using hybrid WPT-ICA and WPT-EMD signal decomposition techniques

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In order to reduce the muscle artifacts in multi-channel pervasive Electroencephalogram (EEG) signals, we here propose and compare two hybrid algorithms by combining the concept of wavelet packet transform (WPT), empirical mode decomposition (EMD) and Independent Component Analysis (ICA). The signal cleaning performances of WPT-EMD and WPT-ICA algorithms have been compared using a signal-to-noise ratio (SNR)-like criterion for artifacts. The algorithms have been tested on multiple trials of four different artifact cases viz. eye-blinking and muscle artifacts including left and right hand movement and head-shaking.This work was supported by FP7 EU funded MICHELANGELO project, Grant Agreement #288241

Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordDegree of phase synchronization between different Electroencephalogram (EEG) channels is known to be the manifestation of the underlying mechanism of information coupling between different brain regions. In this paper, we apply a continuous wavelet transform (CWT) based analysis technique on EEG data, captured during face perception tasks, to explore the temporal evolution of phase synchronization, from the onset of a stimulus. Our explorations show that there exists a small set (typically 3-5) of unique synchronized patterns or synchrostates, each of which are stable of the order of milliseconds. Particularly, in the beta (β) band, which has been reported to be associated with visual processing task, the number of such stable states has been found to be three consistently. During processing of the stimulus, the switching between these states occurs abruptly but the switching characteristic follows a well-behaved and repeatable sequence. This is observed in a single subject analysis as well as a multiple-subject group-analysis in adults during face perception. We also show that although these patterns remain topographically similar for the general category of face perception task, the sequence of their occurrence and their temporal stability varies markedly between different face perception scenarios (stimuli) indicating toward different dynamical characteristics for information processing, which is stimulus-specific in nature. Subsequently, we translated these stable states into brain complex networks and derived informative network measures for characterizing the degree of segregated processing and information integration in those synchrostates, leading to a new methodology for characterizing information processing in human brain. The proposed methodology of modeling the functional brain connectivity through the synchrostates may be viewed as a new way of quantitative characterization of the cognitive ability of the subject, stimuli and information integration/segregation capability.The work presented in this paper was supported by FP7 EU funded MICHELANGELO project, Grant Agreement #288241. Website: www.michelangelo-project.eu/

CORDIC Framework for Quaternion-based Joint Angle Computation to Classify Arm Movements

We present a novel architecture for arm movement classification based on kinematic properties (joint angle and position), computed from MARG sensors, using a quaternion-based gradient-descent method and a 2-link model of the upper limb. The design based on Coordinate Rotation Digital Computer framework was validated on stroke survivors and healthy subjects performing three elementary arm movements (reach and retrieve, lift arm, rotate arm), involved in `making-a-cup-of-tea' an archetypal daily activity, achieved an overall accuracy of 78% and 85% respectively. The design coded in System Verilog, was synthesized using STMicroelectronics 130 nm technology, occupies 340K NAND2 equivalent area and consumes 292 nW @ 150 Hz, besides being functionally verified up to 25 MHz making it suitable for real-time high speed operations. The orientation, arm position and the joint angle, are computed on-the-fly, with the classification performed at the end of movement duration

CORDIC Framework for Quaternion-based Joint Angle Computation to Classify Arm Movements

We present a novel architecture for arm movement classification based on kinematic properties (joint angle and position), computed from MARG sensors, using a quaternion-based gradient-descent method and a 2-link model of the upper limb. The design based on Coordinate Rotation Digital Computer framework was validated on stroke survivors and healthy subjects performing three elementary arm movements (reach and retrieve, lift arm, rotate arm), involved in `making-a-cup-of-tea' an archetypal daily activity, achieved an overall accuracy of 78% and 85% respectively. The design coded in System Verilog, was synthesized using STMicroelectronics 130 nm technology, occupies 340K NAND2 equivalent area and consumes 292 nW @ 150 Hz, besides being functionally verified up to 25 MHz making it suitable for real-time high speed operations. The orientation, arm position and the joint angle, are computed on-the-fly, with the classification performed at the end of movement duration

A statistical index for early diagnosis of ventricular arrhythmia from the trend analysis of ECG phase-portraits

This is the author accepted manuscript. The final version is available from IOP Publishing via the DOI in this record.In this paper, we propose a novel statistical index for the early diagnosis of ventricular arrhythmia (VA) using the time delay phase-space reconstruction (PSR) technique, from the electrocardiogram (ECG) signal. Patients with two classes of fatal VA-with preceding ventricular premature beats (VPBs) and with no VPBs-have been analysed using extensive simulations. Three subclasses of VA with VPBs viz. ventricular tachycardia (VT), ventricular fibrillation (VF) and VT followed by VF are analyzed using the proposed technique. Measures of descriptive statistics like mean (µ), standard deviation (σ), coefficient of variation (CV = σ/µ), skewness (γ) and kurtosis (β) in phase-space diagrams are studied for a sliding window of 10 beats of the ECG signal using the box-counting technique. Subsequently, a hybrid prediction index which is composed of a weighted sum of CV and kurtosis has been proposed for predicting the impending arrhythmia before its actual occurrence. The early diagnosis involves crossing the upper bound of a hybrid index which is capable of predicting an impending arrhythmia 356 ECG beats, on average (with 192 beats standard deviation) before its onset when tested with 32 VA patients (both with and without VPBs). The early diagnosis result is also verified using a leave one out cross-validation (LOOCV) scheme with 96.88% sensitivity, 100% specificity and 98.44% accuracy.This work was supported by the E.U. ARTEMIS Joint Undertaking under the Cyclic and person-centric Health management: Integrated appRoach for hOme, mobile and clinical eNvironments—(CHIRON) Project, Grant Agreement # 2009-1-100228