We present a novel wavelet-based ECG delineation method with robust classification of P wave and T wave. The work is aimed on an adaptation of the method to long-term experimental electrograms (EGs) measured on isolated rabbit heart and to evaluate the effect of global ischemia in experimental EGs on delineation performance. The algorithm was tested on a set of 263 rabbit EGs with established reference points and on human signals using standard Common Standards for Quantitative Electrocardiography Standard Database (CSEDB). On CSEDB, standard deviation (SD) of measured errors satisfies given criterions in each point and the results are comparable to other published works. In rabbit signals, our QRS detector reached sensitivity of 99.87% and positive predictivity of 99.89% despite an overlay of spectral components of QRS complex, P wave and power line noise. The algorithm shows great performance in suppressing J-point elevation and reached low overall error in both, QRS onset (SD = 2.8 ms) and QRS offset (SD = 4.3 ms) delineation. T wave offset is detected with acceptable error (SD = 12.9 ms) and sensitivity nearly 99%. Variance of the errors during global ischemia remains relatively stable, however more failures in detection of T wave and P wave occur. Due to differences in spectral and timing characteristics parameters of rabbit based algorithm have to be highly adaptable and set more precisely than in human ECG signals to reach acceptable performance.

• Klíčová slova
• ECG delineation, Electrogram, Ischemia, Isolated rabbit heart, Wave detection, Wavelet transform,
• MeSH
• algoritmy MeSH
• elektrokardiografie metody   MeSH
• ischemie patofyziologie   MeSH
• králíci MeSH
• lidé MeSH
• počítačové zpracování signálu * MeSH
• srdce patofyziologie   MeSH
• vlnková analýza * MeSH
• zvířata MeSH
• Check Tag
• králíci MeSH
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

• MeSH
• akční potenciály MeSH
• algoritmy MeSH
• časové faktory MeSH
• elektrokardiografie metody   MeSH
• fuzzy logika * MeSH
• lidé MeSH
• lingvistika * MeSH
• neuronové sítě * MeSH
• počítačové zpracování signálu MeSH
• vlnková analýza MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

OBJECTIVE: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal quality, small number of leads and short records, leading to incorrect recognition of pathological events. This paper introduces a novel approach to classification (normal/'N', atrial fibrillation/'A', other/'O', and noisy/'P') of short single-lead ECGs recorded by wearable devices. APPROACH: Various rhythm and morphology features are derived from the separate beats ('local' features) as well as the entire ECGs ('global' features) to represent short-term events and general trends respectively. Various types of atrial and ventricular activity, heart beats and, finally, ECG records are then recognised by a multi-level approach combining a support vector machine (SVM), decision tree and threshold-based rules. MAIN RESULTS: The proposed features are suitable for the recognition of 'A'. The method is robust due to the noise estimation involved. A combination of radial and linear SVMs ensures both high predictive performance and effective generalisation. Cost-sensitive learning, genetic algorithm feature selection and thresholding improve overall performance. The generalisation ability and reliability of this approach are high, as verified by cross-validation on a training set and by blind testing, with only a slight decrease of overall F1-measure, from 0.84 on training to 0.81 on the tested dataset. 'O' recognition seems to be the most difficult (test F1-measures: 0.90/'N', 0.81/'A' and 0.72/'O') due to high inter-patient variability and similarity with 'N'. SIGNIFICANCE: These study results contribute to multidisciplinary areas, focusing on creation of robust and reliable cardiac monitoring systems in order to improve diagnosis, reduce unnecessary time-consuming expert ECG scoring and, consequently, ensure timely and effective treatment.

• MeSH
• elektrokardiografie přístrojové vybavení   metody   MeSH
• fibrilace síní diagnóza   MeSH
• lidé MeSH
• nositelná elektronika * MeSH
• reprodukovatelnost výsledků MeSH
• rozhodovací stromy MeSH
• support vector machine * MeSH
• určení tepové frekvence přístrojové vybavení   metody   MeSH
• víceúrovňová analýza MeSH
• vlnková analýza MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• práce podpořená grantem MeSH

Complex fractionated atrial electrograms (CFAEs) may represent the electrophysiological substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF ablation strategies. A novel algorithm for automated description of atrial electrograms (A-EGMs) fractionation based on wavelet transform and several statistical pattern recognition methods was proposed and new methodology of A-EGM processing was designed and tested. The algorithms for A-EGM classification were developed using normal density based classifiers, linear and high degree polynomial classifiers, nearest mean scaled classifiers, nonlinear classifiers, neural networks and j48. All classifiers were compared and tested using a representative set of 1.5 s A-EGMs (n = 68) ranked by 3 independent experts 100% coincidentialy into 4 classes of fractionation: 1 - organized atrial activity; 2 - mild; 3 - intermediate; 4 - high degree of fractionation. Feature extraction and well performing classification algorithms tested here showed maximal error of 15% and mean classification error across all implemented classifiers 9%, and the best mean classification error 5.9% (nearest mean classifier), and classification error of highly fractionated A-EGMs of approximately 9%.

• MeSH
• algoritmy MeSH
• automatizace MeSH
• elektrofyziologie metody   MeSH
• elektrokardiografie metody   MeSH
• endokard patofyziologie   MeSH
• fibrilace síní diagnóza   patofyziologie   MeSH
• lidé MeSH
• neuronové sítě MeSH
• neurony patologie   MeSH
• normální rozdělení MeSH
• počítačové zpracování signálu MeSH
• statistické modely MeSH
• výpočetní biologie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• srovnávací studie MeSH

Complex fractionated atrial electrograms (CFAEs) may represent the electrophysiological substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify sites of CFAEs is crucial for the development of AF ablation strategies. A novel algorithm for automated description of fractionation of atrial electrograms (A-EGMs) based on the wavelet transform has been proposed. The algorithm was developed and validated using a representative set of 1.5 s A-EGM (n = 113) ranked by three experts into four categories: 1-organized atrial activity; 2-mild; 3-intermediate; 4-high degree of fractionation. A tight relationship between a fractionation index and expert classification of A-EGMs (Spearman correlation rho = 0.87) was documented with a sensitivity of 82% and specificity of 90% for the identification of highly fractionated A-EGMs. This operator-independent description of A-EGM complexity may be easily incorporated into mapping systems to facilitate CFAE identification and to guide AF substrate ablation.

• MeSH
• algoritmy * MeSH
• automatizace MeSH
• elektrokardiografie statistika a číselné údaje   MeSH
• fibrilace síní patofyziologie   MeSH
• interpretace statistických dat MeSH
• katetrizační ablace MeSH
• lidé středního věku MeSH
• lidé MeSH
• pilotní projekty MeSH
• počítačové zpracování signálu MeSH
• senioři MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The paper presents evaluation of the proposed phonocardiography (PCG) measurement system designed primarily for heartbeat detection to estimate heart rate (HR). Typically, HR estimation is performed using electrocardiography (ECG) or pulse wave as one of the fundamental diagnostic methodologies for assessing cardiac function. The system includes novel both sensory part and data processing procedure, which is based on signal preprocessing using Wavelet Transform (WT) and Shannon energy computation and heart sounds classification using K-means. Due to the lack of standardization in the placement of PCG sensors, the study focuses on evaluating the signal quality obtained from 7 different sensor locations on the subject's chest and investigates which locations are most suitable for recording heart sounds. The suitability of sensor localization was examined in 27 subjects by detecting the first two heart sounds (S1, S2). The HR detection sensitivity related to reference ECG from all sensor positions reached values over 88.9 and 77.4% in detection of S1 and S2, respectively. The placement in the middle of sternum showed the higher signal quality with median of the proper S1 and S2 detection sensitivity of 98.5 and 97.5%, respectively.

• MeSH
• elektrokardiografie MeSH
• fonokardiografie MeSH
• lidé MeSH
• srdeční frekvence MeSH
• srdeční ozvy * MeSH
• sternum MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH