Cílem této práci je seznámit se z metodami softwarové detekce QRS komplexů v EKG signálech. Tato práce obsahuje popis elektrokardiografického signálu a hlavních kompon...
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Cílem této práci je seznámit se z metodami softwarové detekce QRS komplexů v EKG signálech. Tato práce obsahuje popis elektrokardiografického signálu a hlavních komponentů EKG. Popisuje základní metody detekci QRS. V práci jsou realizované tři metody detekce: pan-tompkinsův algoritmus, metoda založená na průchodu nulovou hladinou a metoda využívající adaptivní kvantovací práh. Metody byly realizované v prostředí MATLAB a testované na CSE databázi.
Emotion recognition using biological signals plays an essential role in studying psychological states. In many studies, the distinct effect of each physiological signal usually is ignored. By limiting the physiologica...
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Emotion recognition using biological signals plays an essential role in studying psychological states. In many studies, the distinct effect of each physiological signal usually is ignored. By limiting the physiological signals and just evaluating the ECG signals, the paper aims to study the dynamical behavior of the Poincare map in the form of the feature extraction from the generated time-series. For this purpose, the detection of emotional states in the two-dimensional model of emotion (Arousal-Valence) is utilized using the electrocardiogram signals recorded on the MAHNOB-HCl tagging database. So after signal processing, the waves of Q, R, S, and T are detected by processing the ECG signals using the pan-tompkins algorithm. The Poincare map is adopted for the RR, QT, and ST intervals, then five different time-series are extracted from this mapping, subsequently, various features are extracted from these different time-series in the time domain, frequency domain, time-frequency domain, and nonlinear domain analysis. Finally, the classification of emotional states is performed using three classifiers: KNN, SVM, and MLP. In this study, the extracted optimal features of the different time-series generated from Poincare map of the ST Intervals are achieved the best average accuracies of 82.17 % +/- 4.73 and 78.07 % +/- 3.59 in the arousal and valence model, respectively. The superiority of the obtained results from the extracted features of the five different time-series generated from ST-Intervals Poincare map expresses that in comparison with RR and QT Intervals, the ST Intervals are more affected by ANS response to the emotional stimuli. (C) 2020 Elsevier Ltd. All rights reserved.
Birth asphyxia is a potential cause of death that is also associated with acute and chronic morbidities. The traditional and immediate approach for monitoring birth asphyxia (i.e., arterial blood gas analysis) is high...
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Birth asphyxia is a potential cause of death that is also associated with acute and chronic morbidities. The traditional and immediate approach for monitoring birth asphyxia (i.e., arterial blood gas analysis) is highly invasive and intermittent. Additionally, alternative noninvasive approaches such as pulse oximeters can be problematic, due to the possibility of false and erroneous measurements. Therefore, further research is needed to explore alternative noninvasive and accurate monitoring methods for asphyxiated neonates. This study aims to investigate the prominent ECG features based on pH estimation that could potentially be used to explore the noninvasive, accurate, and continuous monitoring of asphyxiated neonates. The dataset used contained 274 segments of ECG and pH values recorded simultaneously. After preprocessing the data, principal component analysis and the pan-tompkins algorithm were used for each segment to determine the most significant ECG cycle and to compute the ECG features. Descriptive statistics were performed to describe the main properties of the processed dataset. A Kruskal-Wallis nonparametric test was then used to analyze differences between the asphyxiated and non-asphyxiated groups. Finally, a Dunn-& Scaron;id & aacute;k post hoc test was used for individual comparison among the mean ranks of all groups. The findings of this study showed that ECG features (T/QRS, T Amplitude, Tslope, Tslope/T, Tslope/|T|, HR, QT, and QTc) based on pH estimation differed significantly (p < 0.05) in asphyxiated neonates. All these key ECG features were also found to be significantly different between the two groups.
The novel coronavirus outbreak has affected over 177 million people in around 218 countries and Territories. Covid-19 is life-threatening for the elderly and for those with chronic health conditions. This has set out ...
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The novel coronavirus outbreak has affected over 177 million people in around 218 countries and Territories. Covid-19 is life-threatening for the elderly and for those with chronic health conditions. This has set out a state of panic where people have the need to constantly monitor their health. The objective of this paper is to review the various sensor components, filtering algorithms, classification techniques, existing methods and technologies that play an important role in developing a safe remote health monitoring device. Comparison of sensor devices is done to understand the features of available products in the market. An extensive survey of ECG monitoring, lung acoustic signals and how COVID-19 affects this vital signature, thermal sensors, and pulse oximeters are presented. The need for a device in this situation to cater to the health issues is necessary.
Nowadays, automatic recognition algorithm is being frequently utilized to extract the information concerning cardiac abnormalities. In this study, a fully automatic novel method based on the continuous wavelet transfo...
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Nowadays, automatic recognition algorithm is being frequently utilized to extract the information concerning cardiac abnormalities. In this study, a fully automatic novel method based on the continuous wavelet transform (CWT) was developed for QT intervals in various ECG signals. Especially, the determination of T-wave end is the paramount problem to be solved. The developed method was performed to find the beginning of QRS complexes and the end of T-wave. The proposed algorithm was tested on MIT-BIH-NSR database given by QT database, then, it yielded the scores 15.17 milliseconds and root-mean-square error of 17.19 milliseconds at silver standard, 19.22 milliseconds and 20.22 milliseconds at gold standard, respectively. In conclusion, the proposed algorithm is a fully automatic method to attain a high performance in the calculation of QT intervals at various ECG signals.
This paper describes a study on combined algorithms used for classification of QRS Complex in ECG signals. The proposed algorithm/detector uses Hillbert transform on a Wavelet base for the pre-processing stage. Both H...
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ISBN:
(纸本)9781467316644
This paper describes a study on combined algorithms used for classification of QRS Complex in ECG signals. The proposed algorithm/detector uses Hillbert transform on a Wavelet base for the pre-processing stage. Both Hillbert transform and Wavelet base known to be superior in reducing unwanted noise resembles in ECG signal such as baseline wander and muscle noise. In addition, the pan-tompkins algorithm was employed as the QRS peak detection. A testing against MIT-BIH Arrhythmias Database results in a reliable detection error rate (DER) of 98.7%. It can be concluded that, the proposed method offers significant noise reduction in pre-processing stage and still produce a reliable results even with the contaminated ECG signal.
When using the available m-health systems, ECG data for a small duration is recorded and sent to a server for processing and arrhythmia detection. Since arrhythmia occurrence is not so frequent in early stages, a need...
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ISBN:
(纸本)9781467383264
When using the available m-health systems, ECG data for a small duration is recorded and sent to a server for processing and arrhythmia detection. Since arrhythmia occurrence is not so frequent in early stages, a need is felt to develop a real time and continuous arrhythmia monitoring system on the phone itself. This paper provides a novel approach to detect QRS complexes from a high fidelity ECG data obtained from B.E.A.T. hardware for arrhythmia monitoring in real time. Our approach referred to as m-QRS uses continuous wavelet transform at its kernel and its efficiency is compared to that of pan-tompkins's which is a standard QRS detection algorithm widely used for arrhythmia detection. It was found that our algorithm uses lesser computation time when compared to pan-tompkins and was found to be mobile friendly. This provides an opportunity to develop further algorithms to perform continuous and real-time arrhythmia monitoring on affordable smartphones without internet dependability.
The epidemic of diabetes, obesity and unhealthy lifestyles have highly contributed to increasing number of patients with heart problems. Wearable fitness trackers are not accurate enough in heart problem detection and...
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The epidemic of diabetes, obesity and unhealthy lifestyles have highly contributed to increasing number of patients with heart problems. Wearable fitness trackers are not accurate enough in heart problem detection and the current software-based algorithms, when implemented in devices like smartwatches are not efficient in terms of hardware resource utilization and computational speed. To address these limitations, this paper proposes an automated heartbeat classifying hardware chip-design which can be placed in any kind of wearable device for real-time cardiac monitoring that would help to ensure early diagnosis of any kind of cardiac abnormality. The algorithm burnt on the hardware is a modification of the pan-tompkins beat-detection algorithm to which a novel classifier algorithm is added. It exhibits high computational speed with an accuracy of 99.65% in extremely noisy situations, when applied on the MIT/BIH arrhythmia database. The hardware utilization on the SPARTAN-6 FPGA for the presented design is just 32% allowing space for much more multi-tasking and upgrading to be done when implemented on a wearable device as an ASIC.
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