Numerical algorithm for nonlinearity compensation of hardly constrained actuation for trajectory tracking control of deadzone-included dynamic systems.

This paper addresses the control of a nonlinear system affected by deadzone effects, using a constrained actuator. The system itself incorporates a second-order oscillatory dynamic actuator, with an unknown nonlinear input-output relationship. The proposed algorithm not only accommodates the deadzone constraints on control inputs but also considers the actuator's saturation limits in control input calculations.

Input-decoupled discrete-time sliding mode control algorithm for servo multi-field multi-armature DC machine.

The multivariable modeling of a servo actuating system consisting of multi-field multi-armature direct current (MFMADC) machine is extracted and a novel discrete time nonlinear algorithm is proposed for the corresponding system. The proposed control algorithm demonstrates robustness against modeling uncertainty and by utilizing its novel mathematical structure, decouples the dynamical interactions of the connected motors. The main contribution of this paper is the proposition of a new decoupling control algorithm that in which, the driving (commanding) voltages of the connected driving motors are extracted separately and independently using the Lyapunov principle in discrete time.

Real-time electrocardiogram P-QRS-T detection-delineation algorithm based on quality-supported analysis of characteristic templates.

The main objective of this study is to introduce a simple, low-latency, and accurate algorithm for real-time detection of P-QRS-T waves in the electrocardiogram (ECG) signal. In the proposed method, real-time signal preprocessing, which includes high frequency noise filtering and baseline wander reduction, is performed by applying discrete wavelet transform (DWT). A method based on signal first-order derivative and adaptive threshold adjustment is employed for real-time detection of the QRS complex.

Electrocardiogram signal quality assessment using an artificially reconstructed target lead.

In real applications, even the most accurate electrocardiogram (ECG) analysis algorithm, based on research databases, might breakdown completely if a quality measurement technique is not applied precisely before the analysis. The major concentration of this study is to describe and develop a reliable ECG signal quality assessment technique. The proposed algorithm includes three major stages: preprocessing, energy-concavity index (ECI) analysis and a correlation-based examination subroutine.

A correlation analysis-based detection and delineation of ECG characteristic events using template waveforms extracted by ensemble averaging of clustered heart cycles.

The goal of this study is to introduce a simple, standard and safe procedure to detect and to delineate P and T waves of the electrocardiogram (ECG) signal in real conditions. The proposed method consists of four major steps: (1) a secure QRS detection and delineation algorithm, (2) a pattern recognition algorithm designed for distinguishing various ECG clusters which take place between consecutive R-waves, (3) extracting template of the dominant events of each cluster waveform and (4) application of the correlation analysis in order to delineate automatically the P- and T-waves in noisy conditions. The performance characteristics of the proposed P and T detection-delineation algorithm are evaluated versus various ECG signals whose qualities are altered from the best to the worst cases based on the random-walk noise theory.

Noise/spike detection in phonocardiogram signal as a cyclic random process with non-stationary period interval.

The major aim of this study is to describe a unified procedure for detecting noisy segments and spikes in transduced signals with a cyclic but non-stationary periodic nature. According to this procedure, the cycles of the signal (onset and offset locations) are detected. Then, the cycles are clustered into a finite number of groups based on appropriate geometrical- and frequency-based time series.

An expert electrocardiogram quality evaluation algorithm based on signal mobility factors.

The major concentration of this study is to describe and to develop a new electrocardiogram (ECG) signal measurement binary quality assessment (accept-reject) technique. The proposed algorithm is composed of three major stages: pre-processing, signal mobility-based quality measurement and advanced post-evaluation. The pre-processing step includes baseline wander and high-frequency disturbances removal.

A unified procedure for detecting, quantifying, and validating electrocardiogram T-wave alternans.

The precision of T-wave alternans (TWA) quantification depends certainly upon the way we choose to align T-waves and to get feedbacks from the electrocardiogram (ECG) quality. Quantifying the ECG TWA based on assigning automatically the required number of T-waves along with applying a proper T-wave alignment approach is the purpose of this paper. The structure of the proposed method mainly consists of seven sections: preprocessing, ECG events detection-delineation, alignment of cycles, T-wave template extraction, T-wave delineation, T-wave left- and right-lobes synchronization, and T-wave alternans quantification-detection by getting feedback from ECG quality value.

Detection and boundary identification of phonocardiogram sounds using an expert frequency-energy based metric.

This paper presents a new method to detect and to delineate phonocardiogram (PCG) sounds. Toward this objective, after preprocessing the PCG signal, two windows were moved on the preprocessed signal, and in each analysis window, two frequency-and amplitude-based features were calculated from the excerpted segment. Then, a synthetic decision making basis was devised by combining these two features for being used as an efficient detection-delineation decision statistic, (DS).

Computerized quality assessment of phonocardiogram signal measurement-acquisition parameters.

The major focus of this study is to describe and develop a phonocardiogram (PCG) signal measurement binary quality assessment (accept-reject) technique. The proposed algorithm is composed of three major stages: preprocessing, numerical-based quality measurement and advanced measurement subroutines. The preprocessing step includes normalization, wavelet-based threshold denoising and baseline wander removal.

Parametric modelling of cardiac system multiple measurement signals: an open-source computer framework for performance evaluation of ECG, PCG and ABP event detectors.

The major focus of this study is to present a performance accuracy assessment framework based on mathematical modelling of cardiac system multiple measurement signals. Three mathematical algebraic subroutines with simple structural functions for synthetic generation of the synchronously triggered electrocardiogram (ECG), phonocardiogram (PCG) and arterial blood pressure (ABP) signals are described. In the case of ECG signals, normal and abnormal PQRST cycles in complicated conditions such as fascicular ventricular tachycardia, rate dependent conduction block and acute Q-wave infarctions of inferior and anterolateral walls can be simulated.

Discrete wavelet-aided delineation of PCG signal events via analysis of an area curve length-based decision statistic.

The aim of this study is to describe a robust unified framework for segmentation of the phonocardiogram (PCG) signal sounds based on the false-alarm probability (FAP) bounded segmentation of a properly calculated detection measure. To this end, first the original PCG signal is appropriately pre-processed and then, a fixed sample size sliding window is moved on the pre-processed signal. In each slid, the area under the excerpted segment is multiplied by its curve-length to generate the Area Curve Length (ACL) metric to be used as the segmentation decision statistic (DS).

Optimal delineation of ambulatory holter ECG events via false-alarm bounded segmentation of a wavelet-based principal components analyzed decision statistic.

The aim of this study is to develop and describe a new ambulatory holter electrocardiogram (ECG) events detection-delineation algorithm with the major focus on the bounded false-alarm probability (FAP) segmentation of an information-optimized decision statistic. After implementation of appropriate preprocessing methods to the discrete wavelet transform (DWT) of the original ECG data, a uniform length sliding window is applied to the obtained signal and in each slid, six feature vectors namely as summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second order differentiation, curve length, area and variance of the excerpted segment are calculated to construct a newly proposed principal components analyzed geometric index (PCAGI) by application of a linear orthonormal projection. In the next step, the α-level Neyman-Pearson classifier (which is a FAP controlled tester) is implemented to detect and delineate QRS complexes.

Finding events of electrocardiogram and arterial blood pressure signals via discrete wavelet transform with modified scales.

A robust electrocardiogram (ECG) wave detection-delineation algorithm that can be applied to all ECG leads is developed in this study on the basis of discrete wavelet transform (DWT). By applying a new simple approach to a selected scale obtained from DWT, this method is capable of detecting the QRS complex, P-wave, and T-wave as well as determining parameters such as start time, end time, and wave sign (upward or downward). In the proposed method, the selected scale is processed by a sliding rectangular window of length n and the curve length in each window is multiplied by the area under the absolute value of the curve.

Segmentation of holter ECG waves via analysis of a discrete wavelet-derived multiple skewness-kurtosis based metric.

In this study, a simple mathematical-statistical based metric called Multiple Higher Order Moments (MHOM) is introduced enabling the electrocardiogram (ECG) detection-delineation algorithm to yield acceptable results in the cases of ambulatory holter ECG including strong noise, motion artifacts, and severe arrhythmia(s). In the MHOM measure, important geometric characteristics such as maximum value to minimum value ratio, area, extent of smoothness or being impulsive and distribution skewness degree (asymmetry), occult. In the proposed method, first three leads of high resolution 24-h holter data are extracted and preprocessed using Discrete Wavelet Transform (DWT).

A methodology for prediction of acute hypotensive episodes in ICU via a risk scoring model including analysis of ST-segment variations.

The aim of this study is to detect Acute Hypotensive Episodes (AHE) and Mean Arterial Pressure Dropping Regimes (MAPDRs) using ECG signal and Arterial Blood Pressure waveforms. To meet this end, the QRS complexes and end-systolic end-diastolic pulses are first extracted using two innovative Modified Hilbert Transform-Based algorithms namely as ECGMHT and BPMHT. A new smoothing algorithm is next developed based on piecewise polynomial fitting to smooth the fast fluctuations observed in RR-tachogram, systolic blood pressure (SBP) and diastolic blood pressure (DBP) trends.

Parallel processing of ECG and blood pressure waveforms for detection of acute hypotensive episodes: a simulation study using a risk scoring model.

The aim of this study is to detect acute hypotensive episodes (AHE) and mean arterial pressure dropping regimes (MAPDRs) using electrocardiographic (ECG) signals and arterial blood pressure waveforms. To meet this end, the QRS complexes and end-systolic end-diastolic pulses are first extracted using two innovative modified Hilbert transform-based algorithms, namely ECGMHT and BPMHT. The resulting systolic and diastolic blood pressure pulses are then used to calculate the MAP trend.

A robust wavelet-based multi-lead Electrocardiogram delineation algorithm.

A robust multi-lead ECG wave detection-delineation algorithm is developed in this study on the basis of discrete wavelet transform (DWT). By applying a new simple approach to a selected scale obtained from DWT, this method is capable of detecting QRS complex, P-wave and T-wave as well as determining parameters such as start time, end time, and wave sign (upward or downward). First, a window with a specific length is slid sample to sample on the selected scale and the curve length in each window is multiplied by the area under the absolute value of the curve.