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1

Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D)2PCA and KELM

Zhang Tao, Chen Wanzhong, Chen Xiaojuan

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

279--297

EN

In order to achieve the accurate identifications of various electroencephalograms (EEGs) and electrocardiograms (ECGs), a unified framework of wavelet scattering transform (WST), bidirectional weighted two-directional two-dimensional principal component analysis (BW(2D)2PCA) and grey wolf optimization based kernel extreme learning machine (KELM) was put forward in this study. To extract more discriminating features in the WST domain, the BW(2D)2PCA was proposed based on original two-directional two-dimensional principal component analysis, by considering both the contribution of eigenvalue and the variation of two adjacent eigenvalues. Totally fifteen classification tasks of classifying normal vs interictal vs ictal EEGs, non-seizure vs seizure EEGs and normal vs congestive heart failure (CHF) ECGs were investigated. Applying patient non-specific strategy, the proposed scheme reported ACCs of no less than 99.300 ± 0.121 % for all the thirteen classification cases of Bonn dataset in classifying normal vs interictal vs ictal EEGs, MCC of 90.947 ± 0.128 % in distinguishing non-seizure vs seizure EEGs of CHB-MIT dataset, and MCC of 99.994 ± 0.001 % in identifying normal vs CHF ECGs of BBIH dataset. Experimental results indicate BW(2D)2PCA based framework outperforms (2D)2PCA based scheme, the high-performance results manifest the effectiveness of the proposed framework and our proposal is superior to most existing approaches.

2

A deformable CNN architecture for predicting clinical acceptability of ECG signal

Allam Jaya Prakash, Samantray Saunak, Sahoo Suraj Prakash, Ari Samit

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

335--351

EN

The degraded quality of the electrocardiogram (ECG) signals is the main source of false alarms in critical care units. Therefore, a preliminary analysis of the ECG signal is required to decide its clinical acceptability. In conventional techniques, different handcrafted features are extracted from the ECG signal based on signal quality indices (SQIs) to predict clinical acceptability. A one-dimensional deformable convolutional neural network (1DDCNN) is proposed in this work to extract features automatically, without manual interference, to detect the clinical acceptability of ECG signals efficiently. In order to create DCNN, the deformable convolution and pooling layers are merged into the regular convolutional neural network (CNN) architecture. In DCNN, the equidistant sampling locations of a regular CNN are replaced with adaptive sampling locations, which improves the network’s ability to learn based on the input. Deformable convolution layers concentrate more on significant segments of the ECG signals rather than giving equal attention to all segments. The proposed method is able to detect acceptable and unacceptable ECG signals with an accuracy of 99.50%, recall of 99.78%, specificity of 99.60%, precision of 99.47%, and F-score of 0.999. Experimental results show that the proposed method performs better than earlier state-of-the-art techniques.

3

A hybrid approach of a deep learning technique for real-time ECG beat detection

Patro Kiran Kumar, Prakash Allam Jaya, Samantray Saunak, Pławiak Joanna, Tadeusiewicz Ryszard, Pławiak Paweł

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 3

455--465

EN

This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bio-electrical signal that helps monitor the heart’s electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can damage the functioning of the heart. Initially, a modified Pan–Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an F1 score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.

4

Sieć sensoryczna do pomiaru wielkości biomedycznych człowieka

Nogły Rafał, Wnuk Marian

Elektronika : konstrukcje, technologie, zastosowania

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2022

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Vol. 63, Nr 2

13--17

PL

W artykule przedstawiono wybrane parametry życiowe człowieka wraz z urządzeniami umożliwiającymi ich pomiar. Następnie przedstawiono systemy przesyłania informacji wokół systemu badania parametrów życiowych człowieka. Zwrócono uwagę na potrzebę zastosowania wielu technologii w celu zapewnienia kompatybilności na wielu poziomach. W dalszej części przedstawione zostały przykładowe anteny które znajdą zastosowanie wokół sieci sensorycznej, po czym został przedstawiony projekt anteny tekstylnej mogącej zostać użytej na poziomie sensor-centrala wraz z charakterystykami opisującymi jej parametry.

EN

The paper presents selected human vital parameters along with devices that enable their measurement. Then, the information transmission systems around the human vital parameters study system are discussed. The need for multiple technologies to ensure compatibility at multiple levels has been highlighted. Further on, examples of antennas that will be used around the sensory network are presented, and then a design of a textile antenna that can be used at the sensor-control panel level was presented, along with the characteristics describing its parameters.

5

ECG signals-based automated diagnosis of congestive heart failure using Deep CNN and LSTM architecture

Kusuma S., Jothi K. R.

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

247--257

EN

In humans, Congestive Heart Failure (CHF) refers to the chronic progressive condition that drastically influences the pumping potentiality of the heart muscle. This CHF has the possibility of increasing health expenditure, morbidity, mortality and minimized quality of life. In this context, Electrocardiogram (ECG) is considered as the simplest and a non-invasive diagnosis method that aids in detecting and demonstrating the realizable changes in CHF. However, diagnosing CHF based on manual exploration of ECG signals is frequently impacted by errors as duration and small amplitude of the signals either investigated separately or in the integration is determined to neither specific nor sensitive. At this juncture, the reliability and diagnostic objectivity of ECG signals during the CHF detection process may be enhanced through the inclusion of automated computer-aided system. In this paper, Deep CNN and LSTM Architecture (DCNN-LSTM)-based automated diagnosis system is proposed for detecting CHF using ECG signals. In specific, CNN is included for the purpose of extracting deep features and LSTM is used for attaining the objective of CHF detection using the extracted features. This proposed DCNN-LSTM is evolved with minimal pre-processing of ECG signals and does not involve any classification process or manual engineered features during diagnosis. The experimentation of the proposed DCNN-LSTM conducted using the real time ECG signals datasets confirmed an accuracy of 99.52, sensitivity of 99.31%, specificity of 99.28%, F-Score of 98.94% and AUC of 99.9%, respectively.

6

Capsule network assisted electrocardiogram classification model for smart healthcare

Jiao Ying, Qi Huamei, Wu Jia

Biocybernetics and Biomedical Engineering

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2022

|

Vol. 42, no. 2

543--555

EN

Improving the classification accuracy of electrocardiogram (ECG) signals is of great significance for diagnosing heart abnormalities and arrhythmias and preventing cardiovascular diseases (CVDs). The traditional classification method depends on medical experience to select and extract features artificially, lacks the generalization ability to deal with big medical data. The vital feature extraction ability of neural networks has become a hot topic to solve this problem. Based on this, the capsule network is applied to ECG signal classification in this paper. Based on the original network architecture, long short-term memory (LSTM) network and 1D convolutional neural network (CNN) are added as a parallel feature extraction layer to extract the spatial and temporal features of the ECG signal. In addition, the enhanced routing algorithm is proposed, which uses the prior probability of subcapsules as a weighting factor for routing algorithm classification to weaken the influence of noise capsules. The proposed model is superior to the existing state-of-the-art techniques when tested on the MIT-BIH arrhythmia database.

7

Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model

Rahul Jagdeep, Sharma Lakhan Dev

Biocybernetics and Biomedical Engineering

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2022

|

Vol. 42, no. 1

312--324

EN

Cardiovascular diseases (CVDs) are a group of heart and blood vessel ailments that can cause chest pain and trouble breathing, especially while active. However, some patients with heart disease have no symptoms and may benefit from screening. Electrocardiogram (ECG) measures electrical activity of the heart using sensors positioned on the skin over the chest, and it can be used for the timely detection of CVDs. This work presents a technique for classification among lethal CVDs like atrial fibrillation (Afib), ventricular fibrillation (Vfib), ventricular tachycardia (Vtec), and normal (N) beats. A novel combination of Stationary wavelet transforms (SWT) and a two-stage median filter with Savitzky–Golay (SG) filter were utilised for pre-processing of the ECG signal followed by segmentation and z-score normalisation process. Next, 1-D six-layers convolutional neural network (1- D CNN) was used for automated and reliable feature extraction. After that, bidirectional long short-term memory (Bi-LSTM) was used in the back end for classification of arrhythmias. The novelty of the present work is the use of 1-D CNN and Bi-LSTM architecture followed by relevant and effective pre-processing of the ECG signal makes this technique accurate and reliable. An accuracy of 99.41 % was achieved using 10-fold cross validation, which is superior to the existing state-of-art methods. Thus, this method presents a noble, accurate, and reliable method for classification of cardiac arrhythmia beats.

8

A high-performance arrhythmic heartbeat classification using ensemble learning method and PSD based feature extraction approach

Yakut Önder, Bolat Emine Doğru

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

667--680

EN

Health problems, directly or indirectly caused by cardiac arrhythmias, may threaten life. The analysis of electrocardiogram (ECG) signals is an important diagnostic tool for assessing cardiac function in clinical research and disease diagnosis. Until today various Soft Computing methods and techniques have been proposed for the analysis of ECG signals. In this study, a new Ensemble Learning based method is proposed that automatically classifies the arrhythmic heartbeats of ECG signal according to the category-based and patient-based evaluation plan. A two-stage median filter was used to remove the baseline wander from the ECG signal. The locations of fiducial points of the ECG signal were determined using the developed QRS complex detection method. Within the scope of this study, four different feature extraction methods were utilized. A new feature extraction technique based on the Power Spectral Density has been proposed. Hybrid sub-feature sets were constructed using a Wrapper-based feature selection algorithm. A new method based on Ensemble Learning (EL) has been proposed by using a stacking algorithm. Multi-layer Perceptron (MLP) and Random Forest (RF) as base learners and Linear Regression (LR) as meta learner were utilized. Average performance values for the category-based arrhythmic heartbeat classification of the proposed new method based on Ensemble Learning; accuracy was 99,88%, sensitivity was 99,08%, specificity was 99,94% and positive predictivity (+P) was 99,08%. Average performance values for patient-based arrhythmic heartbeat classification were 99,72% accuracy, 99,30% sensitivity, 99,83% specificity and 99,30% positive predictivity (+P). Thus, it is concluded that the proposed method has higher performance results than similar studies in the literature.

9

A complete system for an automated ECG diagnosis

Wójcik Dariusz, Woś Michał, Kłosowski Grzegorz, Rymarczyk Tomasz

Przegląd Elektrotechniczny

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2021

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R. 97, nr 1

162--165

EN

We present a very simple LSTM neural network capable of categorizing heart diseases from the ECG signal. With the use of the ECG simulator we ware able to obtain a large data-set of ECG signal for different diseases that was used for neural network training and validation.

PL

W artykule prezentujemy bardzo prostą sieć LSTM zdolną do rozpoznawania jednostek chorobowych przy chorobach serca. Dodatkowo pokazujemy w jaki sposób stworzyliśmy bazę danych sygnałów pomiarowych użytych do nauki i walidacji sieci neuronowej przy użyciu symulatora EKG.

10

A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network

Kołodziej Marcin, Majkowski Andrzej, Tarnowski Paweł, Rak Remigiusz Jan, Rysz Andrzej

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 3

art. no. e136921

EN

Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.

11

An improved cardiac arrhythmia classification using an RR interval-based approach

Rahul Jagdeep, Sora Marpe, Sharma Lakhan Dev, Bohat Vijay Kumar

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

656--666

EN

Accurate and early detection of cardiac arrhythmia present in an electrocardiogram (ECG) can prevent many premature deaths. Cardiac arrhythmia arises due to the improper conduction of electrical impulses throughout the heart. In this paper, we propose an improved RR interval-based cardiac arrhythmia classification approach. The Discrete Wavelet Transform (DWT) and median filters were used to remove high-frequency noise and baseline wander from the raw ECG. Next, the processed ECG was segmented after the determination of the QRS region. We extracted the primary feature RR interval and other statistical features from the beats to classify the Normal, Premature Ventricular Contraction (PVC), and Premature Atrial Contraction (PAC). The K-Nearest Neighbour (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naı¨ve Bayes (NB), and Random Forest (RF) classifier were utilised for classification. Overall performance of SVM with Gaussian kernel achieved Se % = 99.28, Sp % = 99.63, +P % = 99.28, and Acc % = 99.51, which is better than the other classifiers used in this method. The obtained results of the proposed method are significantly better and more accurate.

12

A comprehensive survey on low-cost ECG acquisition systems: Advances on design specifications, challenges and future directionA comprehensive survey on low-cost ECG acquisition systems: Advances on design specifications, challenges and future direction

Faruk Nasir, Abdulkarim Abubakar, Emmanuel Ifada, Folawiyo Yusuf Y., Adewole Kayode S., Mojeed Hammed A., Oloyede Abdukareem A., Olawoyin Lukman A., Sikiru Ismaeel A., Nehemiah Musa, Ya’u Gital Abdulsalam, Chiroma Haruna, Ogunmodede James A., Almutairi Mubarak, Katibi Ibraheem A.

Biocybernetics and Biomedical Engineering

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2021

|

Vol. 41, no. 2

474--502

EN

Availability of low-cost, reliable, and portable Electrocardiography (ECG) devices is still very important in the medical world today. Despite the tremendous technological advancement, Cardiovascular Diseases (CVDs) remain a serious health burden claiming millions of lives on an annual basis globally. This is more prevalent in Low and Middle-Income Countries (LMICs) where there are huge financial instability and lack of critical infrastructure and support services for the health care system. Efforts aimed at reducing the prevalence of CVDs are confounded by late diagnosis, frequently, caused by lack of access to or nonavailability of basic diagnostic modalities such as the ECG. Hence effective mitigation of the effect of CVDs in LMICs depend on the development of such devices at low-cost with reliability, accuracy and energy efficiency. This paper therefore, was developed to understand the state of the art of low-cost ECG acquisition systems with respect to design features and system capabilities for different use cases. In addition, different design options and taxonomies of available low-cost ECG devices, case studies reports of efficacy tests have been provided. The paper proposes a generalised ECG framework and provides implementation challenges and open research directions that should be considered when developing such devices for proper management of CVDs.

13

Klasyfikacja sygnału EKG przy użyciu konwolucyjnych sieci neuronowych

Ogryczak Maria

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej

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2020

|

Nr 71

51--54

PL

Podniesienie jakości i zautomatyzowanie procesu diagnozy jest istotnym elementem rozwoju medycyny i samokontroli stanu zdrowia pacjentów. Od dłuższego czasu istnieją i są stosowane różne metody analizy i klasyfikacji sygnału EKG, jednak nie zawsze ich dokładność jest zadowalająca. Największym problemem jest trudność rozpoznania istniejącej nieprawidłowości, w przypadku gdy jej reprezentacja jest podobna do prawidłowej pracy serca np. przedwczesny skurcz komorowy. W ostatnich latach obserwujemy dynamiczny rozwój nowego narzędzia z rodziny metod sztucznej inteligencji - głębokich sieci neuronowych. Cechuje je duża selektywność klasyfikacji nawet najbardziej skomplikowanych sygnałów w postaci szeregów czasowych czy obrazów, często na podstawie cech niezauważalnych dla ludzkiego oka. W niniejszym artykule przedstawiono sposób analizy zarejestrowanego sygnału elektrycznej czynności mięśnia sercowego (EKG), na podstawie pojedynczego, wyodrębnionego cyklu pracy serca. Celem badania było zdiagnozowanie sześciu różnych typów ewolucji mogących świadczyć o występowaniu arytmii. Badania przeprowadzono z wykorzystaniem ogólnodostępnej bazy danych MIT-BIH Arrhythmia Database. W celu podniesienia jakości ekstrakcji cech analizowanego sygnału, dokonano jego dekompozycji czasowo-przestrzennej przy wykorzystaniu transformacji falkowej. W rezultacie uzyskano zadowalające wyniki klasyfikacji: dokładność 92,4% i swoistość (zdolność do wykrycia braku cechy) 96,5%. Osiągnięte wyniki potwierdzają skuteczność systemu automatycznej klasyfikacji cyklów pracy serca, mogącego wspomóc lekarzy w procesie żmudnej analizy dużej liczby zarejestrowanych danych.

EN

Automation and improvement of diagnostic process is a vital element of medicine development and patient’s condition self-control. For a long time different ECG signal classification methods exist and are successfully applied, nevertheless their accuracy is not always satisfying enough. The lack of identification of an existing abnormality, which is very similar to a normal heartbeat is the biggest issue - for example premature ventricular contraction. Over the past few years there was a rapid development of an artificial intelligence tool - deep neural networks. They characterise by a high classification ability even the most complicated patterns in the form of time series or images, often based on features unnoticeable for human eye. In this paper the approach to electrocardiography (ECG) analysis was presented, taking into consideration a single heartbeat. The aim of this research was diagnosis of six different types of beat that may indicate arrhythmia occurrence. The study were performed on the public database MIT-BIH Arrhythmia Database. In order to enhance feature extraction quality of the analysed signal the time-space decomposition was made using wavelet transform. The satisfying performance with 92.4% accuracy and 96.5% specificity were accomplished. The achieved results may be used to develop an automatic heartbeat classification system that would significantly contribute medicians in the arduous process of data analysis.

14

Electrical activity with ECG analysis for Body Surface Potential Mapping

Rymarczyk Tomasz

Przegląd Elektrotechniczny

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2020

|

R. 96, nr 10

144--147

EN

The article presents tests of electrical activity with ECG analysis for mapping body surface potential. Diagnostic tests involve placing available standard electrodes on the patient's body over specific anatomical skin areas. The main idea of the solution is to combine body surface potential mapping with electric impedance tomography imaging. This solution can provide a greater amount of medical data for analysis, whereby a larger number of cardiopulmonary disorders can be detected using specialized algorithms.

PL

Artykuł przedstawia badaia aktywności elektrycznej z analizą EKG do mapowania potencjału powierzchni ciała. Testy diagnostyczne polegają one na umieszczeniu dostępnych standardowych elektrod na ciele pacjenta na ściśle określonych anatomicznych obszarach skóry. Główną ideą rozwiązania jest połączenie mapowania potencjału powierzchni ciała z elektrycznym obrazowaniem tomografii impedancyjnej. Takie rozwiązanie może dostarczyć większą ilość danych medycznych do analizym gdzieza pomocą specjalistycznych algorytmów można będzie wykrywać większą ilość zaburzeń sercowo-płucnych.

15

Electrocardiogram generation software for testing of parameter extraction algorithms

Maciejewski Marcin, Maciejewska Barbara, Karpiński Robert, Krakowski Przemysław

Applied Computer Science

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2020

|

Vol. 16, no 4

37--47

EN

Fast and automated ECG diagnosis is of great benefit for treatment of cardi-ovascular and other conditions. The algorithms used to extract parameters need to be precise, robust and efficient. Appropriate training and testing methods for such algorithms need to be implemented for optimal results. This paper presents a software solution for computer ECG generation and a simplified concept of testing process. All the parameters of the resulting generated signal can be tweaked and set properly. Such software can also be beneficial for training and educational use.

16

The North Sea Bicycle Race ECG project : time-domain analysis

Długosz D., Eftestøl T., Królak A., Wiktorski T., Ørn S.

Journal of Automation Mobile Robotics and Intelligent Systems

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2018

|

Vol. 12, No. 1

23--32

EN

Analysis of electrocardiogram and heart rate provides useful information about health condition of a patient. The North Sea Bicycle Race is an annual cycling competition in Norway. Examination of ECG recordings collected from participants of this race may allow defining and evaluating the relationship between physical endurance exercises and heart electrophysiology. Parameters reflecting potentially alarming deviations are to be identified in this study. This paper presents results of a time-domain analysis of ECG data collected in 2014, implementing K-Means clustering. A double stage analysis strategy, aimed at producing hierarchical clusters, is proposed. The first phase allows rough separation of data. Second stage is applied to reveal internal structure of the majority clusters. In both steps, discrepancies driving the separation could stem from three sources. Firstly, they could be signs of abnormalities in electrical activity of the heart. Secondly, they may allow discriminating between natural groups of participants – according to sex, age, physical fitness. Finally, some deviations could result from faults in data extraction, therefore serving in evaluation of the parameters. The clusters were defined predominantly by combinations of features: heartbeat signals correlation, P-wave shape, and RR intervals; none of the features alone was discriminative for all the clusters.

17

Criteria for evaluation, methodology and analysis of electrocardiographic signal results from experimental studies in preventive control of clinically healthy people

Manukova A., Grozeva M., Marinov M.

Elektronika : konstrukcje, technologie, zastosowania

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2018

|

Vol. 59, nr 1

40--44

EN

The article presents criteria for evaluation, methodology and analysis of the results of electrocardiographic signals in the preventive control of clinically healthy people after emotional and extreme driving. These criteria combine new approaches to building an electronic system in the processing of individual medical information and specific methods and visualize the results in the real life of healthy people. Often a sudden heart attack occurs in the workplace, in the vehicle while driving or during physical or emotional activities. An important task is to preserve the health of the working people. Criteria are developed for evaluation of electrocardiographic signals in the analysis of the results of clinically healthy people. This allows the development of a methodology for experimental field and laboratory investigations for the analysis of ECG signals and also increases the reliability of the diagnostics of cardiac diseases in the construction of a preventive control system. The developed AMEG_SIM program for modeling and simulating ECG signals and the software program AMEG_AN developed enables analysis and evaluation of ECG signals for clinically health people and compares to cardiac diseases for prevention purposes.

PL

W artykule przedstawiono kryteria oceny, metodologii i analizy wyników sygnałów elektrokardiograficznych w profilaktyce klinicznej osób zdrowych po emocjonalnej i ekstremalnej jeździe. Kryteria te łączą nowe podejścia do budowy systemu elektronicznego w przetwarzaniu indywidualnych informacji medycznych i konkretnych metod oraz wizualizują wyniki w prawdziwym życiu zdrowych ludzi. Często nagły atak serca występuje w miejscu pracy, w pojeździe podczas prowadzenia pojazdu lub podczas aktywności fizycznej lub emocjonalnej. Ważnym zadaniem jest zachowanie zdrowia ludzi pracy. Opracowano kryteria oceny sygnałów elektrokardiograficznych w analizie wyników klinicznie zdrowych osób. Pozwala to na opracowanie metodologii eksperymentalnych badań terenowych i laboratoryjnych do analizy sygnałów EKG, a także zwiększa niezawodność diagnostyki chorób serca w konstrukcji systemu kontroli prewencyjnej. Opracowany program AMEG_SIM do modelowania i symulacji sygnałów EKG oraz opracowany program AMEG_AN umożliwia analizę i ocenę sygnałów EKG dla osób z problemami zdrowotnymi i porównanie z chorobami serca w celach profilaktycznych.

18

Development of Underwear with Integrated 12 Channel ECG for Men and Women

Trummer S., Ehrmann A., Büsgen A.

Autex Research Journal

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2017

|

Vol. 17, no. 4

344--349

EN

Cardiovascular diseases are the most frequent cause of death worldwide. Cases of cardiac arrest can often be attributed to undetected cardiac arrhythmia. Detecting rare episodes of arrhythmia necessitates long-term ECG measurements along days or weeks. However, due to the relatively small number of electrodes used for these ECGs, abnormal episodes can still go unrecognized. This article thus describes the development of underwear with ten inbuilt textile ECG electrodes, allowing for the measurement of long-term 12-lead ECG. As against the constructs of other research groups, the position of electrodes offers the same detection directions as the common 12-lead ECG equipment in hospitals or medical practices. Long-term tests have shown the suitability of the sensory underwear variants for men and women to detect reliable ECG signals without disturbing the patients’ comfort.

19

Experimental identification of a mathematical model of human operator working under mental stress

Kopyt A., Zużewicz K., Bartuzi P.

Acta of Bioengineering and Biomechanics

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2017

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Vol. 19, no. 3

177--185

EN

In engineering the human is considered as one of the system elements. In most studies, his/her model remains unchanged due to the external factors. The present study shows that a relation between the mental stress and human dynamics cannot be neglected. The dynamic characteristics of the operator model change due to external stimuli, i.e., mental stress. The aim of this study was to present identification of a mathematical human model and measurement methodology of the mental stress level. To determine the level of human response to external stimuli, the electrocardiography (ECG) and electromyography (EMG) methods were applied. The results showed difference in model parameters that cannot be neglected during the modeling of the human operator. The present study points to the need of developing simplified human models, taking into account external stimuli that have direct impact on his/her effectiveness. Some interdisplinary investigation provide may benefits combining part of the automation and ergonomics research areas.

20

Investigations of real conditions properties of the biomedical electrodes with integrated electrode contact quality monitoring system for recording electrocardiographic signals

Marszalek Z., Gawedzki W.

Przegląd Elektrotechniczny

|

2016

|

R. 92, nr 11

55--58

EN

The results of investigations of the metrological properties of the system of 4 biomedical electrodes with the continuous control of the contact quality, are presented in the hereby paper. Investigations were performed under real conditions of measuring the electrocardiographic signals of humans, at the application of the typical ECG instrumentation. Due to the performed experiments the conformity of the electrocardiographic signals obtained by means of the standard gel electrodes glued to the body and the ones obtained by means of the electrodes with continuous control of the contact quality, was confirmed.

PL

W artykule przedstawiono wyniki badań właściwości metrologicznych układu 4 elektrod do pomiaru sygnałów elektrokardiograficznych człowieka z ciągłą kontrolą stanu kontaktu, przy wykorzystaniu typowej aparatury EKG. Potwierdzono zgodność zapisów sygnałów EKG wykonanych za pomocą standardowych elektrod żelowych przyklejanych do ciała oraz przy zastosowaniu elektrod z ciągłą kontrolą stanu kontaktu.