Влияние СД 2 типа на возникновение пароксизмов тахи- и брадикардии

Целью работы явилось изучение особенностей возникновения пароксизмов тахи- брадикардии у больных СД 2 типа. Было обследовано 36 больных СД 2 типа. Всем обследуемым было проведено эхокардиографическое исследование по стандартной методике и холтеровское мониторирование ЭКГ, а также исследование биохимических показателей. При анализе полученных результатов было выявлено, что эпизоды синусовой тахикардии выявляются у 100% больных СД 2 типа; эпизоды синусовой брадикардии выявляются у 75% больных, при этом, уменьшение частоты выявления эпизодов тахи- и брадикардии у больных СД 2 типа коррелирует со степенью декомпенсации диабета и выраженностью ст руктурно-функциональных изменений сердца

Evaluating and comparing performance of feature combinations of heart rate variability measures for cardiac rhythm classification

Abstract Automatic classification of cardiac arrhythmias using heart rate variability (HRV) analysis has been an important research topic in recent years. Explorations reveal that various HRV feature combinations can provide highly accurate models for some rhythm disorders. However, the proposed feature combinations lack a direct and carefully designed comparison. The goal of this work is to assess the various HRV feature combinations in classification of cardiac arrhythmias. In this setting, a total of 56 known HRV features are grouped in eight feature combinations. We evaluate and compare the combinations on a difficult problem of automatic classification between nine types of cardiac rhythms using three classification algorithms: support vector machines, AdaBoosted C4.5, and random forest. The effect of analyzed segment length on classification accuracy is also examined. The results demonstrate that there are three combinations that stand out the most, with total classification accuracy of roughly 85% on time segments of 20 seconds duration. A simple combination of time domain features is shown to be comparable to the more informed combinations, with only 1-4% worse results on average than the three best ones. Random forest and AdaBoosted C4.5 are shown to be comparably accurate, while support vector machines was less accurate (4-5%) on this problem. We conclude that the nonlinear features exhibit only a minor influence on the overall accuracy in discerning different arrhythmias. The analysis also shows that reasonably accurate arrhythmia classification lies in the range of 10 to 40 seconds, with a peak at 20 seconds, and a significant drop after 40 seconds

Kalp hızı değişkenliğinin spektral kestirim metotları kullanılarak analizi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu çalışmada, bir kardiyak aritmi tipi olan süpraventriküler aritmi için KHD işaretinin spektral analizleri gerçekleştirilmiştir. MIT-BIH Supraventricular Arrhytmia (SVA) veri tabanında bulunan 78 hastadan alınmış yarım saatlik EKG verileri, kaydırmalı pencere ortalama filtresinden geçirilerek ektopikleri yok edilmiş sonra yatay ekseni vuru tanım alanından zaman tanım alanına çevrilmiştir. İnterpolasyon ve yeniden örnekleme işlemlerinden geçirilen KHD verileri spektral kestirim metotlarından Periodogram, Welch periodogram, Yule-Walker ve Burg metotları ile analiz edilmiştir. Analiz sonuçlarında elde edilen güç spektral yoğunluklarının grafikleri alçak frekans (AF) (0,04?0,15 Hz) ve yüksek frekans (YF) (0,15?0,4 Hz) bölgesini kapsayacak şekilde çizdirilmiş ve sonuçlar karşılaştırılmıştır. Ayrıca AF ve YF bölgelerinde elde edilen toplam güçler ile sempatovagal denge (AF/YF) tablolar halinde listelenip sonuçlar değerlendirilmiştir.Heart Rate Variability (HRV) is an important physiological signal for classification of cardiac arrhythmias and used for analysis of fluctuation of sympatho-vagal balance in autonomic Nervous System.In this study, spectral analysis of HRV signal is realized for Supraventricular Arrhythmia being a type of cardiac arrhythmia. At first, Electrocardiogram (ECG) half-hours records in MIT-BIH Supraventricular arrhythmia database obtained from 78 patients are converted to HRV signals and then their ectopics are rejected using sliding window average filter and horizontal axis is converted to time domain from beat number domain. HRV data passed from interpolation and resampling processes are analyzed using Periodogram, Welch Periodogram, Yule-Walker and Burg methods. Power spectral density graphics are drawn as including Low Frequency (LF) (0.04-0.15 Hz) and High Frequency (HF) (0.15-0.4 Hz) zones and these results are compared. And also, total power on LF, HF zones and sympathovagal balance ratio (LF/HF Ratio) are listed on tables and evaluated

Non-linear dynamical analysis of biosignals

Biosignals are physiological signals that are recorded from various parts of the body. Some of the major biosignals are electromyograms (EMG), electroencephalograms (EEG) and electrocardiograms (ECG). These signals are of great clinical and diagnostic importance, and are analysed to understand their behaviour and to extract maximum information from them. However, they tend to be random and unpredictable in nature (non-linear). Conventional linear methods of analysis are insufficient. Hence, analysis using non-linear and dynamical system theory, chaos theory and fractal dimensions, is proving to be very beneficial. In this project, ECG signals are of interest. Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be fatal or cause irreparable damage to the heart when sustained over long periods of time. Hence the ability to identify arrhythmias from ECG recordings is of importance for clinical diagnosis and treatment and also for understanding the electrophysiological mechanism of arrhythmias. To achieve this aim, algorithms were developed with the help of MATLAB® software. The classical logic of correlation was used in the development of algorithms to place signals into the various categories of cardiac arrhythmias. A sample set of 35 known ECG signals were obtained from the Physionet website for testing purposes. Later, 5 unknown ECG signals were used to determine the efficiency of the algorithms. A peak detection algorithm was written to detect the QRS complex. This complex is the most prominent waveform within an ECG signal and its shape, duration and time of occurrence provides valuable information about the current state of the heart. The peak detection algorithm gave excellent results with very good accuracy for all the downloaded ECG signals, and was developed using classical linear techniques. Later, a peak detection algorithm using the discrete wavelet transform (DWT) was implemented. This code was developed using nonlinear techniques and was amenable for implementation. Also, the time required for execution was reduced, making this code ideal for real-time processing. Finally, algorithms were developed to calculate the Kolmogorov complexity and Lyapunov exponent, which are nonlinear descriptors and enable the randomness and chaotic nature of ECG signals to be estimated. These measures of randomness and chaotic nature enable us to apply correct interrogative methods to the signal to extract maximum information. The codes developed gave fair results. It was possible to differentiate between normal ECGs and ECGs with ventricular fibrillation. The results show that the Kolmogorov complexity measure increases with an increase in pathology, approximately 12.90 for normal ECGs and increasing to 13.87 to 14.39 for ECGs with ventricular fibrillation and ventricular tachycardia. Similar results were obtained for Lyapunov exponent measures with a notable difference between normal ECG (0 – 0.0095) and ECG with ventricular fibrillation (0.1114 – 0.1799). However, it was difficult to differentiate between different types of arrhythmias.Biosignals are physiological signals that are recorded from various parts of the body. Some of the major biosignals are electromyograms (EMG), electroencephalograms (EEG) and electrocardiograms (ECG). These signals are of great clinical and diagnostic importance, and are analysed to understand their behaviour and to extract maximum information from them. However, they tend to be random and unpredictable in nature (non-linear). Conventional linear methods of analysis are insufficient. Hence, analysis using non-linear and dynamical system theory, chaos theory and fractal dimensions, is proving to be very beneficial. In this project, ECG signals are of interest. Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be fatal or cause irreparable damage to the heart when sustained over long periods of time. Hence the ability to identify arrhythmias from ECG recordings is of importance for clinical diagnosis and treatment and also for understanding the electrophysiological mechanism of arrhythmias. To achieve this aim, algorithms were developed with the help of MATLAB® software. The classical logic of correlation was used in the development of algorithms to place signals into the various categories of cardiac arrhythmias. A sample set of 35 known ECG signals were obtained from the Physionet website for testing purposes. Later, 5 unknown ECG signals were used to determine the efficiency of the algorithms. A peak detection algorithm was written to detect the QRS complex. This complex is the most prominent waveform within an ECG signal and its shape, duration and time of occurrence provides valuable information about the current state of the heart. The peak detection algorithm gave excellent results with very good accuracy for all the downloaded ECG signals, and was developed using classical linear techniques. Later, a peak detection algorithm using the discrete wavelet transform (DWT) was implemented. This code was developed using nonlinear techniques and was amenable for implementation. Also, the time required for execution was reduced, making this code ideal for real-time processing. Finally, algorithms were developed to calculate the Kolmogorov complexity and Lyapunov exponent, which are nonlinear descriptors and enable the randomness and chaotic nature of ECG signals to be estimated. These measures of randomness and chaotic nature enable us to apply correct interrogative methods to the signal to extract maximum information. The codes developed gave fair results. It was possible to differentiate between normal ECGs and ECGs with ventricular fibrillation. The results show that the Kolmogorov complexity measure increases with an increase in pathology, approximately 12.90 for normal ECGs and increasing to 13.87 to 14.39 for ECGs with ventricular fibrillation and ventricular tachycardia. Similar results were obtained for Lyapunov exponent measures with a notable difference between normal ECG (0 – 0.0095) and ECG with ventricular fibrillation (0.1114 – 0.1799). However, it was difficult to differentiate between different types of arrhythmias

Effectiveness of a handheld remote ECG monitor

This present study deals with designing a real-time remote handheld ECG monitoring system and evaluating its potential usefulness in early detection of heart conduction problems. The raw ECG recordings were sent by the handheld monitor (client) to a remote server, which performed an on-line ECG analysis and sent the results back to the client. Real-time feedback provided to the client included display of ECG, results of ECG analysis and alarms (if required). The objective of this work was to determine its effectiveness in real-time identification of particular pattern preceding ventricular fibrillation. The remote server identified the occurrence of QRS complex and premature ventricular contractions and monitored ECG for ventricular tachycardia and variations in heart rate variability indices. The sensitivity and specificity of the QRS detection to ECG recordings from MIT-Arrhythmia database were 99.34% and 99.31%, respectively. Similarly these parameters of the premature ventricular contraction detection were 87.5% and 91.67%, respectively. The time between alarm and the onset of ventricular fibrillation was measured on ECG recordings where premature ventricular contractions were found to lead to ventricular fibrillation. The remote monitor was able to successfully identify the onset on ventricular fibrillation. Early detection could contribute to better response to an emergency intervention. HRV indices sensitive to the differences between normal and subjects with congestive heart failure were monitored in real-time. They were heart rate, statistical index RMSSD, total spectral power, high frequency power and the ratio of low frequency to high frequency power (LFP:HFP). The effectiveness of HRV indices was tested on an ECG recording of a sleep study subject, who experienced cardiac arrhythmia. Cyclic changes observed in total spectral power prior to onset of cardiac arrhythmia could be attributed to REM sleep cycles. No other conclusive change in HRV indices was observed. The monitor's usefulness in predicting long-term prognosis of post-MI subjects was tested on ECG recordings from two subjects made immediately after conclusion of cardiac arrhythmia and during a follow-up visit. Both showed higher RMSSD, total spectral power and LFP:HFP ratio. Personalizing the monitor for each patient further improves its accuracy in measurement of various parameters

Kalp hızı değişkenliğinin dalgacık dönüşümü ve yapay sinir ağları kullanılarak analizi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Dalgacık dönüşümü biyomedikal işaretler gibi durağan olmayan işaretlerin analizinde kullanılan en önemli metotlardan biridir. Kalp hızı değişkenliği (KHD) işaretleri, içerisinde bulunan süreksizlikler ve çok küçük frekans aralıkları ile dalgacık dönüşümüne en uygun adaylardan biridir.Bu çalışma, literatürde çok nadir kullanılan ventriküler taşiaritmi veri tabanı üzerinde gerçekleştirilen KHD analizlerini kapsar. Bu kapsam ektopikli ve ektopiksiz KHD'lerin ayrık dalgacık dönüşümü (ADD) kullanılarak analizi ve SD'nin hesaplanması, ADD ve dalgacık paket dönüşümü (DPD) ile frekans bantlarındaki enerji değerlerinin tespiti ve sonuçların karşılaştırmalı analizi, DPD ile ADD uygulaması sırasında meydana gelen frekans bant kaymalarının ortadan kaldırılarak frekans bantlarının literatüre uygun hale getirilmesi, ADD ve yapay sinir ağları (YSA) ile SD `nin otomatik tespiti, DPD ve YSA kullanımı ile alt bantlardaki baskın bileşenlerin belirlenerek frekans bantlarının daraltılması, literatürde nadir bahsedilen ÇAF bandının analizi, ve tüm elde edilen sonuçların ventriküler taşikardi ve ventriküler fibrilasyon açısından ayrıntılı olarak değerlendirilmesini içine alır.KHD 'lerin DPD ile analizi, ÇAF bölgesinin değerlendirilmesi, SD'nin otomatik tespiti ve baskın frekans alt-bantlarının belirlenmesiyle ilgili, yapılan ilk çalışma olma özelliğini taşıyan bu tez, elde ettiği sonuçlar, önerdiği güncel metotlar ile VT ve VF için ortaya koyduğu değerlendirmelerle literatürdeki çok önemli bir eksikliği ortadan kaldırmaktadır.Wavelet Transform that is used for analyses of non-stationary signals as biomedical signals is one of the most important methods. Heart Rate Variability (HRV) signals having discontinuities and very small frequency ranges are one of the most appropriate for Wavelet Transform.This study contains HRV analyses which are applied on ventricular tachyarrhythmia database that isn?t analyzed as detailed in the literature. This scope consists of HRV analyses with ectopic and without ectopic using Discrete Wavelet Transform (DWT), the determination of Sympathovagal Balance (SB), the detection of frequency bands energy values and compare of its results using DWT and Wavelet Packet Transform (WPT), to optimize the frequency band shifts in DWT using WPT, the automatic detection of SB using DWT and Artificial Neural Networks (ANN), the identification of domination sub-bands using WPT and ANN, analysis of the Very Low Frequency (VLF) band that is defined occasionally in the literature, and the evaluation of all of the obtained results in the Ventricular Tachyarrhythmia database.This thesis is the first study including specifications that HRV analysis with WP, interpretation of VLF band, automatic detection of SB and identification of dominant frequency sub-bands. Obtained results, proposed actual methods and evaluation of Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) resolves an important drawback

Automatic Analysis of Heart Rate Variability Signals

Táto dizertačná práca sa venuje variabilite srdcového rytmu a metódam jej stanovenia. Predovšetkým sa zameriava na nelineárne metódy a obzvlášť na Poincarého graf. Najprv sa venuje princípu a podstate vzniku variability srdcového rytmu, potom spôsobom jej znázornenia, metódam jej analýzy lineárnym aj nelineárnym a fyziologickým a patologickým vplyvom na zmeny variability srdcového rytmu. Obzvlášť je tu kladený dôraz na metabolický syndróm. V ďalšej časti práce sú porovnávané a vyhodnocované rôzne spôsoby vyjadrenia variability srdcového rytmu a ďalej sú testované vybrané metódy analýzy variability srdcového rytmu na unikátnych dátach pacientov s metabolickým syndrómom a zdravých osôb poskytnutých Ústavem přístrojové techniky (ÚPT) AV ČR. Predovšetkým sú použité Poincarého graf a jeho parametre SD1 a SD2, bežne používané parametre časovej domény a frekvenčnej domény, parametre stanovujúce entropiu signálu a Lyapunovov exponent. SD1 a SD2, ktoré kombinujú výhody metód časovej a frekvenčnej analýzy, dokážu úspešne rozlišovať medzi pacientmi s metabolickým syndrómom a zdravými osobami.This dissertation thesis is dedicated to the heart rate variability and methods of its evaluation. It mainly focuses on nonlinear methods and especially on the Poincaré plot. First it deals with the principle and nature of the heart rate variability, then the ways of its representation, linear and also nonlinear methods of its analysis and physiological and pathophysiological influence on heart rate variability changes. In particular, there is emphasis on the metabolic syndrome. In the next section of the thesis there are compared and evaluated different ways of representation of the heart rate variability and further are tested selected methods of heart rate variability analysis on unique data from patients with the metabolic syndrome and healthy subjects provided by the Institute of Scientific Instruments, Academy of Sciences of Czech Republic. In particular, they are used the Poincaré plot and its parameters SD1 and SD2, commonly used time domain and frequency domain parameters, parameters evaluating signal entropy and the Lyapunov exponent. SD1 and SD2 combining the advantages of time and frequency domain methods of heart rate variability analysis distinguish successfully between patients with the metabolic syndrome and healthy subjects.