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This study investigates the potential of a set of ECG morphological features for person verification/identification. The measurements are done over 145 pairs of ECG recordings from healthy subjects, acquired 5 years apart (T1, T2 = T1+5 years). Time, amplitude, area and slope descriptors of the QRS-T pattern are analysed in 4 ECG leads, forming quasi-orthogonal lead system (II&III, V1, V5). The correspondence between feature values in T1 and T2 is verified via factor analysis by principal components extraction method; correlation analysis applied over the measurements in T1 and T2; synthesis of regression equations for prediction of features’ values in T2 based on T1 measurements; and cluster analysis for assessment of the correspondence between measured and predicted feature values. Thus, 11 amplitude descriptors of the QRS complex are highlighted as stable, i.e. keeping their strong correlation (≥0.7) within a certain factor, weak correlation (

Electrocardiographic (ECG) and electromyographic (EMG) signals are inevitably and simultaneously recorded from the same electrodes and are respectively useful signal and noise in electrocardiography, and vice versa in electromyography. The frequency domains of the two signals overlap, making it difficult to filter the noise without distortion of the useful signal. An original ‘dynamic method’ for separation of the two signals was created. In a series of publications that began in 1999 with filtering of EMG noise from ECG signal, we have described the method and have made a number of improvements such as noise analysis and automatic on/off triggering in presence/absence of noise, online application, and tuning the parameters, to fulfill the last filtering recommendations of the American Heart Association. No matter if the Dynamic procedure is to be used in electrocardiography or in electromyography, the method contains the following: (i) Evaluation of the frequency bands of the ECG signal; (ii) filtering (suppression) of the EMG signal by dynamic change of the size of the filtering window for maximal preservation of the morphology of the ECG waves. The cutoff frequency is individual for any signal sample and varies from 13 Hz at the linear segments of the ECG signal, trough 25 Hz for the T-waves of high amplitude, and up to 400 Hz for the QRS-complexes; (iii) EMG signal separation by subtraction of the filtered ECG signal from ECG + EMG initial signal. With the current review, we are attempting to summarize all done over the years on the Dynamic procedure. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is associated with significant risk of heart failure and consequent death. Its episodic appearance, the wide variety of arrhythmias exhibiting irregular AF-like RR intervals and noises accompanying the ECG acquisition, impede the reliable AF detection. Therefore, the Computing in Cardiology Challenge 2017 organizers encourage the development of methods for classification of short, single-lead ECG as AF, normal sinus rhythm (NSR), other rhythm (OR), or noisy signal (NOISE). This study presents a set of 118 time and frequency domain feature including descriptors of the RR and PP intervals; QRS and P-wave amplitudes; ECG behavior within the TQ intervals, deviation of the TQ and PQRST segments from their first principle component analysis vector; dominant frequency; regularity index, width and area of the power spectrum estimated for the ECG signal with eliminated QRS complexes. Three classification techniques have been applied over the 118 ECG features – linear discriminant analysis (LDA), classification tree (CT) and neural network (NN) approach. The scores over a test subset are: (i) FNSR = 0.81; FAF = 0.61; FOR = 0.53, F1 = 0.65 for CT, which is the most simple model; (ii) FNSR = 0.82; FAF = 0.62; FOR = 0.53, F1 = 0.66 for LDA, which is the model with the most reproducible accuracy results; (iii) FNSR = 0.86; FAF = 0.74; FOR = 0.57, F1 = 0.72 for NN, which is the most accurate model. [ABSTRACT FROM AUTHOR]

Introduces an important class of endeavors of fuzzy modeling. Objective of fuzzy descriptive models; Use of the language of fuzzy sets that are viewed as generic models of information granules; Development of an information granule; Provision of algorithm of constructing membership functions of the information granules.