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1

Comparison of multiband filtering, empirical mode decomposition and short-time fourier transform used to extract physiological components from long-term heart rate variability

Adamczyk Krzysztof, Polak Adam G.

Metrology and Measurement Systems

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2021

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Vol. 28, nr 4

643--660

EN

Heart rate is constantly changing under the influence of many control signals, as manifested by heart rate variability (HRV). HRV is a nonstationary, irregularly sampled signal, the spectrum of which reveals distinct bands of high, low, very low and ultra-low frequencies (HF, LF, VLF, ULF). VLF and ULF components are the least understood, and their analysis requires HRV records lasting many hours. Moreover, there are still no well-established methods for the reliable extraction of these components. The aim of this work was to select, implement and compare methods which can solve this problem. The performance of multiband filtering (MBF), empirical mode decomposition and the short-time Fourier transform was tested, using synthetic HRV as the ground truth for methods evaluation as well as real data of three patients selected from 25 polysomnographic records with a clear HF component in their spectrograms. The study provided new insights into the components of long-term HRV, including the character of its amplitude and frequency modulation obtained with the Hilbert transform. In addition, the reliability of the extracted HF, LF, VLF and ULF waveforms was demonstrated, and MBF turned out to be the most accurate method, though the signal is strongly nonstationary. The possibility of isolating such waveforms is of great importance both in physiology and pathophysiology, as well as in the automation of medical diagnostics based on HRV.

2

Heart rate variability based assessment of cognitive workload in smart operators

Digiesi Salvatore, Manghisi Vito M., Facchini Francesco, Klose Elisa M., Foglia Mario M., Mummolo Carlotta

Management and Production Engineering Review

|

2020

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Vol. 11, No. 3

56--64

EN

The study on cognitive workload is a field of research of high interest in the digital society. The implementation of ‘Industry 4.0’ paradigm asks the smart operators in the digital factory to accomplish more ‘cognitive-oriented’ than ‘physical-oriented’ tasks. The Authors propose an analytical model in the information theory framework to estimate the cognitive workload of operators. In the model, subjective and physiological measures are adopted to measure the work load. The former refers to NASA-TLX test expressing subjective perceived work load. The latter adopts Heart Rate Variability (HRV) of individuals as an objective indirect measure of the work load. Subjective and physiological measures have been obtained by experiments on a sample subjects. Subjects were asked to accomplish standardized tasks with different cognitive loads according to the ‘n-back’ test procedure defined in literature. Results obtained showed potentialities and limits of the analytical model proposed as well as of the experimental subjective and physiological measures adopted. Research findings pave the way for future developments.

3

Non-contact video-based remote photoplethysmography for human stress detection

Nikolaiev Sergii, Telenyk Sergii, Tymoshenko Yury

Journal of Automation Mobile Robotics and Intelligent Systems

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2020

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Vol. 14, No. 2

63--73

EN

This paper presents the experimental results for stress index calculation using developed by the authors information technology for non-contact remote human heart rate variability (HRV) retrieval in various conditions from video stream using common wide spread web cameras with minimal frame resolution of 640x480 pixels at average frame rate of 25 frames per second. The developed system architecture based on remote photoplethysmography (rPPG) technology is overviewed including description of all its main components and processes involved in converting video stream of frames into valuable rPPG signal. Also, algorithm of RR-peaks detection and RR-intervals retrieval is described. It is capable to detect 99.3% of heart contractions from raw rPPG signal. The usecases of measuring stress index in a wide variety of situations starting with car and tractor drivers at work research and finishing with students passing exams are presented and analyzed in detail. The results of the experiments have shown that the rPPG system is capable of retrieving stress level that is in accordance with the feelings of experiments’ participants.

4

Heart rate variability features from nonlinear cardiac dynamics in identification of diabetes using artificial neural network and support vector machine

Aggarwal Yogender, Das Joyani, Mazumder Papiya Mitra, Kumar Rohit, Sinha Rakesh Kumar

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1002--1009

EN

Diabetes mellitus (DM) is a multifactorial disease characterized by hyperglycemia. The type 1 and type 2 DM are two different conditions with insulin deficiency and insulin resistance, respectively. It may cause atherosclerosis, stroke, myocardial infarction and other relevant complications. It also features neurological degeneration with autonomic dysfunction to meet metabolic demand. The autonomic balance controls the physiological variables that exhibit nonlinear dynamics. Thus, in current work, nonlinear heart rate variability (HRV) parameters in prognosis of diabetes using artificial neural network (ANN) and support vector machine (SVM) have been demonstrated. The digital lead-I electrocardiogram (ECG) was recorded from male Wister rats of 10–12 week of age and 200 ± 20 gm of weight from control (n = 5) as well as from Streptozotocin induced diabetic rats (n = 5). A total of 526 datasets were computed from the recorded ECG data for evaluating thirteen nonlinear HRV parameters and used for training and testing of ANN. Using these parameters as inputs, the classification accuracy of 86.3% was obtained with an ANN architecture (13:7:1) at learning rate of 0.01. While relatively better accuracy of 90.5% was observed with SVM to differentiate the diabetic and control subjects. The obtained results suggested that nonlinear HRV parameters show distinct changes due to diabetes and hence along with machine learning tools, these can be used for development of noninvasive low-cost real-time prognostic system in predicting diabetes using machine learning techniques.

5

Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals

Sharma Rishi Raj, Kumar Ashish, Pachori Ram Bilas, Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 2

312--327

EN

Congestive heart failure (CHF) is a cardiac abnormality in which heart is not able to pump sufficient blood to meet the requirement of all the parts of the body. This study aims to diagnose the CHF accurately using heart rate variability (HRV) signals. The HRV signals are non-stationary and nonlinear in nature. We have used eigenvalue decomposition of Hankel matrix (EVDHM) method to analyze the HRV signals. The lowest frequency component (LFC) and the highest frequency component (HFC) are extracted from the eigenvalue decomposed components of HRV signals. After that, the mean and standard deviation in time domain, mean frequency calculated from Fourier-Bessel series expansion, k-nearest neighbor (k-NN) entropy, and correntropy features are evaluated from the decomposed components. The ranked features based on t-value are fed to least-squares support vector machine (LS-SVM) classifier with radial basis function (RBF) kernel for automated diagnosis of CHF HRV signals. The study is performed on three normal datasets and two CHF datasets. Our proposed system has yielded an accuracy of 93.33%, sensitivity of 91.41%, and specificity of 94.90% using 500 HRV samples. The automated toolkit can aid cardiac physicians in the accurate diagnosis of CHF patients to confirm their findings with our system. Hence, it will help to provide timely treatment for CHF patients and save life.

6

A novel multi-class approach for early-stage prediction of sudden cardiac death

Devi Reeta, Tyagi Hitender Kumar, Kumar Dinesh

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 3

586--598

EN

Sudden cardiac death (SCD) is a complex issue that may occur in population groups with either known or unknown cardiovascular disease (CVD). Given the complex nature of SCD, the discovery of a suitable biomarker will prove essential in identifying individuals at risk of SCD, while discriminating it from patients with other cardiac pathologies as well as healthy individuals. Thus, this study aimed to develop an efficient approach to support a better comprehension of heart rate variability (HRV) as a predictive biomarker to identify SCD patients at an early stage. The present study proposed a novel multi-class classification approach using signal processing methods of HRV to predict SCD 10 min before its occurrence. The developed algorithm was qualitatively and quantitatively analyzed in terms of discriminating SCD patients from patients of heart failure and normal people. A total of 51 HRV signals of all three classes obtained from PhysioBank were processed to extract 32 features in each subject. The optimal feature selection was performed by a hybrid approach of sequential feature selection-random under sampling boosting algorithms. Multi-class classifiers, namely decision tree, support vector machine, and k-nearest neighbors were used for classification. An average classification accuracy of SCD prediction 10 min before occurrence was obtained as 83.33%. Therefore, this study suggests a new efficient approach for the early-stage prediction of SCD that is considerably different from that reported in the literature to date. However, to generalize the findings, the algorithm needs to be tested for a larger population group.

7

Ocena zmienności rytmu serca pod wpływem muzyki na podstawie sygnału fotopletyzmograficznego

Wilk B., Post P.

Przegląd Elektrotechniczny

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2018

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R. 94, nr 8

150--153

PL

W artykule opisano algorytm opracowany do wyznaczania zmienności rytmu serca (tzw. sygnału HRV) na podstawie wartości chwilowych okresu sygnału PPG, który reprezentuje falę tętna obwodowego. Sygnał PPG został zarejestrowany podczas oddziaływania muzyki. Do wydzielenia składowych sygnału HRV (tj. fluktuacji i nieliniowego trendu) zastosowano dyskretną transformatę falkową. Do oceny wpływu muzyki na częstość pracy serca przyjęto parametry opisujące zarówno zmienność fluktuacji rytmu serca, jak i wolnozmiennego trendu.

EN

In this article, the algorithm developed for determination of HRV based on the PPG signal representing the peripheral pulse wave was described. The PPG signal was recorded under the influence of music. The components of HRV signal (i.e. a nonlinear trend and fluctuations) were extracted by using the DWT. The parameters representing variability of the HRV fluctuations as well as trend were applied to assessment of HRV.

8

Automatic detection of heart diseases based on ECG signal analysis

Delarue Louis, Drouard Pierre-Amaud, Hourdé Pierre, Joulia Jean, Renault Victorieu, Kotas Rafał

International Journal of Microelectronics and Computer Science

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2018

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Vol. 9, nr 4

133--141

EN

The authors provide overview of techniques used in ECG signal analysis and present their implementation in order to detect heart diseases (arrhythmias). This paper presents different means to study the ECG signals to develop automatic detection of heart diseases based on artificial intelligence.

9

Parachuting training improves autonomic control of the heart in novice parachute jumpers

Mazurek K., Koprowska N., Gajewski J., Zmijewski P., Skibniewski F., Różanowski K.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

181--189

EN

This study aimed to investigate the acute effect of skydiving and the chronic effect of parachute jump training on the cardiac response in novice and trained parachuters. The study included 11 experienced skydivers (expert group), aged 35.9 ± 7.2 years, and 12 students (novice group), aged 27.9 ± 7.2 years. Participants underwent 10-unit training in accelerated freefall (AFF) from an altitude of 4000 m. In experts, the highest HR was noted during the phase of opening of the parachute and during the landing phase, and in pre-training novices during the phase of exit from the plane and the descent by parachute. Mean standard deviation of NN intervals (SDNN) was higher in experts than pre-training novices. In novices, post-training values of SDNN, root mean square of successive differences (RMSSD), and the low/high frequency oscillation ratio (LF/HF) were higher, and HF and LF were lower, than pre-training values. In experts the values of SDNN, RMSSD, LF, HF, and total power spectrum (TP) were significantly higher and LF/HF significantly lower than in pre-training novices. Novice compared to experienced skydivers are characterized by higher modulation of the sympathetic, and lower modulation of the parasympathetic autonomic nervous system (ANS). Chronic effects of 10-unit AFF training are characterized by decreased modulation of the sympathetic nervous system, increased total power spectrum of HRV, and increased activity of the parasympathetic nervous system. The changes in ANS modulation suggest that parachute training leads to a reduction of the stress response and improves autonomic control of cardiovascular function in novice skydivers.

10

Modeling the 2D space of emotions based on the poincare plot of heart rate variability signal

Moharreri S., Dabanloo N. J., Maghooli K.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

794--809

EN

Emotions mean accepting, understanding, and recognizing something with one's senses. The physiological signals generated from the internal organs of the body can objectively and realistically reflect changes in real-time human emotions and monitor the state of the body. In this study, the two-dimensional space-based emotion model was introduced on the basis of Poincare's two-dimensional plot of the signal of heart rate variability. Four main colors of psychology, blue, red, green, and yellow were used as a stimulant of emotion, and the ECG signals from 70 female students were recorded. Using extracted features of Poincare plot and heart rate asymmetry, two tree based models estimated the levels of arousal and valence with 0.05 mean square errors, determined an appropriate estimation of these two parameters of emotion. In the next stage of the study, four different emotions mean pleasure, anger, joy, and sadness, were classified using IF-THEN rules with the accuracy of 95.71%. The results show the color red is associated with more excitement and anger, while green has small anxiety. So, this system provides a measure for numerical comparison of mental states and makes it possible to model emotions for interacting with the computer and control mental states independently of the pharmaceutical methods.

11

Early detection of sudden cardiac death using nonlinear analysis of heart rate variability

Khazaei M., Raeisi K., Goshvarpour A., Ahmadzadeh M.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

931--940

EN

Background and objective: Sudden cardiac death (SCD) is one of the most widespread reasons for death around the world. A precise and early prediction of SCD can improve the chance of survival by administering cardiopulmonary resuscitation (CPR). Hence, there is a vital need for an SCD prediction system. Methods: In this work, a novel and efficient algorithm for automated detection of SCD six minutes before its onset is proposed. This algorithm uses features based on the nonlinear modeling of heart rate variability (HRV). In fact, after the extraction of the HRV signals, increment entropy and recurrence quantification analysis-based features are extracted. The one-way ANOVA is applied for the dimension reduction of feature space—this results in lower computational cost. Finally, the distinguishing features are fed to classifiers such as the decision tree, K-nearest neighbor, naive Bayes, and the support vector machine. Results: By using the decision tree classifier we have achieved SCD detection six minutes before its onset with an accuracy, specificity, and sensitivity of 95%. These results demonstrate the superiority of the presented algorithm compared to the existing ones in performance. Conclusions: This study shows that a combination of features based on the nonlinear modeling of HRV, such as laminarity (based on recurrence quantification analysis), and increment entropy leads to early detection of SCD. Choosing the decision tree improves the performance of the algorithm. The results could help in the development of a tool that would allow the detection of cardiac arrest six minutes before its onset.

12

Heart rate variability (HRV) during virtual reality immersion

Malińska M., Zużewicz K., Bugajska J., Grabowski A.

International Journal of Occupational Safety and Ergonomics

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2015

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Vol. 21, No. 1

47--54

EN

The goal of the study was assessment of the hour-long training involving handling virtual environment (sVR) and watching a stereoscopic 3D movie on the mechanisms of autonomic heart rate (HR) regulation among the subjects who were not predisposed to motion sickness. In order to exclude predispositions to motion sickness, all the participants (n=19) underwent a Coriolis test. During an exposure to 3D and sVR the ECG signal was continuously recorded using the Holter method. For the twelve consecutive 5-min epochs of ECG signal, the analysis of heart rate variability (HRV) in time and frequency domains was conducted. After 30 min from the beginning of the training in handling the virtual workstation a significant increase in LF spectral power was noted. The values of the sympathovagal LF/HF index while sVR indicated a significant increase in sympathetic predominance in four time intervals, namely between the 5th and the 10th minute, between the 15th and the 20th minute, between the 35th and 40th minute and between the 55th and the 60th minute of exposure.

13

Real-time estimation of the spectral parameters of Heart Rate Variability

Kudrynski K., Strumillo P.

Biocybernetics and Biomedical Engineering

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2015

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Vol. 35, no. 4

304--316

EN

Spectral Heart Rate Variability (HRV) parameters, LF (low frequency) and HF (high frequency), have an important role in interpreting slower and faster heart rate modulations. An online analysis method of HRV spectral parameters based on the modified Hilbert–Huang Transform (HHT) is proposed in the paper. A number of novel methods have been put forward to meet the demand of causal pre-processing of interbeat time intervals (IBI) series prior to application of HHT. Also in the real-time implementation of the HHT which is the combination of the Empirical Mode Decomposition and Hilbert spectral analysis an original extrapolation method of intrinsic mode function related to LF and HF spectral parameters was applied. The proposed algorithm allows temporal estimation of HRV spectral parameters in real-time with delays being reduced up to 60% with respect to the Short Time Fourier Transform (STFT) analysis. Such reduction in analysis delay can have an important significance in a number of cardiologic invasive procedures, e.g. in cardio-resynchronisation therapy (CRT).

14

Analysis of heart rate variability as a predictor of mortality in cardiovascular patients of intensive care unit

Moridani M. K., Setarehdan S. K., Nasrabadi A. M., Hajinasrollah E.

Biocybernetics and Biomedical Engineering

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2015

|

Vol. 35, no. 4

217--226

EN

Objective: Dynamic changes of heart rate variability (HRV) reflect autonomic dysfunction in cardiac disease. Some studies suggest the role of HRV in predicting intensive care unit (ICU) mortality. The main object of this study was analyzing the HRV to design an algorithm to predict mortality risk. Methods: We evaluated 80 cardiovascular ICU patients (45 males and 45 females), ranging from 45 to 70 years. Common time and frequency domain analysis, non-linear Poincaré plot and recurrence quantification analysis (RQA) were used to study the HRV in two episodes. The episodes include 8–4 h before death, and 4 h before death to death. Independent sample t-test was used as statistical analysis. Results: Statistical analysis indicates that frequency domain and Poincaré parameters such as LF/HF and SD2/SD1 show changes in transition to death episode (p < 0.05). Moreover, Lmean, vmax and RT measures showed meaningful changes (p < 0.01) in closer segments to the death. Conclusions: Analysis of physiological variables shows that there are significant differences in RQA measures in episodes close to death. These changes can be interpreted as more stability and determinism behavior of HRV in episodes close to death. RQA parameters can be used together with HRV parameters for description and prediction of mortality risk in ICU patients.

15

Detekcja błędów segmentacji sygnału fotopletyzmograficznego

Bołtrukiewicz M.

Pomiary Automatyka Kontrola

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2014

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R. 60, nr 8

541--543

PL

Tematyka pracy związana jest z analizą zmienności rytmu serca, a dotyczy w szczególności detekcji błędów powstających podczas segmentacji procedury wyznaczającej zbiór okresów przebiegu. W artykule omówiono i zilustrowano podstawowe przyczyny błędów segmentacji. Zaproponowano dwa algorytmy detekcyjne wykorzystujące statystyczne przedziały tolerancji, które następnie przetestowano i oceniono przy użyciu posiadanego zbioru 5-minutowych przebiegów sygnału fotopletyzmograficznego.

EN

The paper concerns the detection of segmentation errors in a photoplethysmographic signal (PPG). In the paper, the causes of segmentation errors are considered. The technical causes are presented in Figs. 1 and 2 while the biological causes are shown in Fig. 3. Two algorithms of detection of errors are proposed. Both algorithms use statistical tolerance ranges, which are described by Eq. 1. The principles of operation of these algorithms are given in Eqs. 2 and 3. In the study the efficiency of these algorithms was evaluated using the factor of errors defined by Eq. 4. For both algorithms the sensitivity (SE), specificity (SP) and positive prediction value (PPV) and negative prediction value (NPV) were calculated, too. In the experiments real photoplethysmographic signals were analyzed. Time duration of each signal was equal to 5 min. The coefficients of errors obtained for both algorithms are presented in Fig. 4. The comparison of the sensitivity and the positive prediction value is shown in Fig. 5. The causes of differences between the obtained values of the coefficients are considered. The possibility of improvement of SE and PPV is also analyzed.

16

A comparative study of the effects of hyperbaric oxygenation on heart rate variability using wavelet analysis and fast Fourier transform

Juvet T., Abdelhadi S.

Polish Hyperbaric Research

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2013

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nr 4(45)

19--36

EN

Heart rate variability (HRV) is a recognized and reliable parameter of the autonomous nervous system’s effects on the heart. Hyperbaric oxygenation (HBO) has been shown to increase HRV and decrease heart rate suggesting increased vagal tone. Several methods have been developed to analyze HRV, including time-domain, frequency-domain and wavelet time/frequency analysis. To compare the use of these methods in analyzing HRV over shorter periods of time, electrocardiogram recordings were made of 6 subjects under normal resting conditions, followed by breathing 100% O2 at 253 kPa (2.5 ATA) of pressure. HRV was analyzed over two and ten minute periods using both fast Fourier transform and wavelet analysis. Results showed that wavelet and FFT analyses have similar diagnostic features, with continuous wavelet analysis appearing more suitable in detecting changes in HRV over shorter time periods.

PL

Zmienność rytmu zatokowego (HRV) jest sprawdzonym i niezawodnym parametrem mierzącym wpływ autonomicznego układu nerwowego na pracę serca. Badania wykazały, że hiperbaryczna terapia tlenowa (HBO) powoduje zwiększenie HRV oraz redukcję w częstotliwości akcji serca, sugerując tym samym zwiększenie napięcia nerwu błędnego. W celu dokonania analizy HRV wykorzystano kilka metod, w tym analizę czasową, analizę w dziedzinie częstotliwości oraz czasowo-częstotliwościową analizę fali elementarnej. W celu porównania skuteczności tych metod w krótkookresowej analizie HRV, sześciu pacjentów poddano badaniu przy użyciu elektrokardiogramu w warunkach spoczynku, a następnie zlecono im oddychanie 100% O2 pod ciśnieniem 253 kPa (2,5 ATA). HRV analizowano w okresach dwu- i dziesięciominutowych przy użyciu szybkiej transformaty Fouriera (STF) i analizy fali elementarnej. Wyniki wykazały podobieństwa cech diagnostycznych obu metod, przy czym stwierdzono, iż nieprzerwana analiza fali elementarnej osiąga lepsze wyniki w wykrywaniu zmian w HRV w krótszych przedziałach czasowych.

17

Heart rate variability assessment with rational-dilation wavelet transform

Buchner T., Grzyb G., Krzesiński P.

Bio-Algorithms and Med-Systems

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2013

|

Vol. 9, no. 1

29--35

EN

Wavelet transform on a rational dilation is proposed as a method of assessment of spectral power in low and high frequency (LF and HF, respectively) bands for heart rate variability (HRV) analysis. One of the unique properties of this method is a possibility to align the band limits of certain scales with the limits of ranges LF and HF used in HRV analysis. The method parameters are optimized for use in the context of HRV analysis. Suitable examples are tilt test recordings analyzed using the optimized rational-dilation wavelet transform method.

18

Wpływ wolnozmiennego pola magnetycznego na parametry zmienności rytmu zatokowego i uśrednionego EKG wysokiego wzmocnienia u pacjentów z cukrzycą typu 2 i nadciśnieniem tętniczym

Cieślar G., Gmyrek J., Małyszek-Tumidajewicz J., Jagodziński L., Sieroń A.

Przegląd Elektrotechniczny

|

2012

|

R. 88, nr 12b

117-120

PL

W pracy oceniano oddziaływanie wolnozmiennych pól magnetycznych stosowanych w formie magnetoterapii na zachowanie się parametrów zmienności rytmu zatokowego oraz parametrów uśrednionego EKG wysokiego wzmocnienia u 32 pacjentów z cukrzycą typu 2 i nadciśnieniem tętniczym. W trakcie cyklu 15 codziennych zabiegów magnetoterapii obserwowano przywrócenie równowagi układu wegetatywnego poprzez obniżenie nadmiernej aktywności współczulnej oraz redukcję ryzyka powstania późnych potencjałów komorowych, co wskazuje na przydatność magnetoterapii jako metody wspomagającej leczenie farmakologiczne tych chorych.

EN

In the study the impact of extremely-low-frequency variable magnetic fields applied as magnetotherapy on parameters of heart rate variability and signal averaged electrocardiography was estimated in 32 patients with diabetes type 2 and arterial hypertension. During a cycle of 15 daily magnetotherapy procedures, the restoration of vegetative system balance due to decreasing of excessive sympathetic activity and reduction of risk of creation of late ventricular potentials was observed, that indicates potential usefulness of magnetotherapy as a method assisting pharmacological treatment in those patients.

19

Prediction of fatigue and sleep onset using HRV analysis

Szypulska M., Piotrowski Z.

Elektronika : konstrukcje, technologie, zastosowania

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2012

|

Vol. 53, nr 12

16-19

EN

In the paper objective syndromes associated with sleep onset and fatigue based on the analysis of heart rate variability (HRV) have been presented. Temporal and frequency parameters have been given particular attention. An algorithm for detection of the moment of sleep onset and fatigue has been described. It is based on the determination of the LF/HF ratio on the basis of an RR tachogram and assigning its value to three basic states: activity, drowsiness and sleep. Results of experiments conducted on people without dysfunctions in electrocardiogram (ECG) waveforms have been presented and discussed.

PL

W artykule przedstawiono obiektywne syndromy towarzyszące zasypianiu i zmęczeniu na podstawie analizy Zmienności Rytmu Serca (HRV). Wyszczególniono parametry analizy czasowej i częstotliwościowej. Opisano algorytm detekcji momentu zasypiania i zmęczenia bazujący na wyznaczeniu wartości współczynnika LF/HF na podstawie tachogramu RR i przypisaniu jego wartości do trzech podstawowych stanów: aktywności, senności oraz snu. Przedstawiono i omówiono wyniki eksperymentów przeprowadzonych na osobach bez dysfunkcji przebiegów elektrokardiogramu (EKG).

20

Zastosowanie wariancji Allana do badania zmienności rytmu serca

Bołtrukiewicz M.

Pomiary Automatyka Kontrola

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2011

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R. 57, nr 12

1486-1488

PL

Zmienność rytmu serca jest powszechnie występującym zjawiskiem fizjologicznym. Do jego parametryzacji wykorzystuje się typowe wielkości statystyczne, do których należy wartość średnia i odchylenie standardowe. W pracy zaproponowano wykorzystanie do jej oceny wariancji Allana, która jest użyteczna w ocenie jakości oscylatorów elektronicznych. Zaprezentowano adaptację metody obliczeniowej wariancji Allana, którą dostosowano do specyfiki badanego sygnału oraz wyniki uzyskane dla trzech 10-minutowych przebiegów akcji serca.

EN

The heart rate variability - HRV, is a general physiological phenomenon. The analysis of HRV is a simple and noninvasive clinical examination of heart and autonomic nervous system. In the time domain the periods of electrocardiographic signal (ECG) or photoplethysmographic signal (PPG) are detected. Next, typical statistical parameters such as e.g. the average value, the standard deviation of these periods are calculated. The Allan variance is a recommended measure of instability of oscillators. The human heart is an oscillator too, so the Allan variance has been proposed to evaluate its rhythm variability. The procedure of calculation of the Allan variance has been modified, because the fundamental frequency of the heart rhythm is unknown. This modification is described by Eqs. 1 - 5, and shown in Fig. 1. In the experiments the PPG signal has been used. The histograms of the heart rhythm periods are presented in Fig. 2. The obtained values of the heart rhythm Allan variance are shown in Fig. 3. The obtained results enable identification of the kind of a noise component occurring in the heart rhythm.