Grigaliūnas, Artūras

Electrocardiogram (ECG) signal reflects electrical processes taking part in the human body. Various lead combinations and registering systems are designed in aim to reflect as much as possible of valuable diagnostical information at the same time minimizing representation of concurrent electrical processes in the human body. On the other hand, designers of the lead systems aim to minimize the number of leads used making the registering of the signals less obtrusive for the patient. Assessment of suitability of the registered signals and their diagnostic value remains challenging because of broad variety of the diagnostic applications where ECG signals are used. Anyway, the general purpose of ECG registering systems is to reflect three-dimensional changes of the electrical field in the body during excitation of heart muscle, so, spatial positioning of the leads is optimized for few particular diagnostic cases (e.g. 12- lead ECG). Classical approach of orthogonal Franc leads system could be considered as „Golden Standard” in ECG registering comprehensively reflecting all electrical activity of the heart. Multivariate analysis method Principal Component Analysis (PCA) [1] gives an optimal representation of variance in multidimensional data. Multilead ECG signal could be considered as multidimensional data, therefore the PCA will give optimal representation of variance in it, what actually is the valuable diagnostic information. In ideal case, if all registering systems are concerned as equally good, this optimal representation of variance in ECG signal should be the same, regardless to the particular lead system used. Various transforms are proposed to obtain it: for example, 12-lead signal from special 4-lead registered signals, or Frank orthogonal leads from 12-leads by using deep neural network [2,3]. Here we propose the method to assess the diagnostical value of ECG signal, registered by some “new lead system” (Neural Network based 3-lead reconstruction from 12-lead ECG), comparing PCA representation of its result to the one obtained from some „Golden Standard” system like 12-lead or Orthogonal Frank leads. We analysed 12-lead and orthogonal Frank lead ECG recordings from healthy and myocardial infarction patients from open-access PhysioNet PTB Diagnostic ECG Database [4]. The recordings in this Database are unique because the same multilead signal was register synchronically in 12-lead and Orthogonal Frank lead systems. We compared the optimal representation of electrical activity registered by both “standard” systems and “new lead system” – Neural Network transformed signal. The complexity of the signal (amount of carried information) could be reflected by minimal amount principal components needed for optimal its’ representation. We found that third principal component becomes important only in myocardial infarction cases (Fig.1, Table 1). The differences in coefficients of third principal component revealed limitations of Neural Network based signal transform. The presented case is an example how any newly designed ECG registering system could be evaluated in regard to the information carried by the registered signals.

The present study continued to investigate whether the effects of length misperception caused by cross-shaped (formed by two pairs of the oppositely oriented Müller-Lyer wings) contextual distractors can be explained by the combined manifestation of two different (i.e., the Müller-Lyer and filled-space) geometric illusions of extent. In psychophysical experiments, the luminance of one pair of wings was randomly changed, while the luminance of the other pair remained constant. Two different distractor orientations were used-when the wings with constant luminance formed the right side of the cross or the left side, otherwise. To separately evaluate the manifestation of the Müller-Lyer illusion under different luminance conditions, two distracting crosses of the same orientation were attached to the lateral stimulus terminators in the first series of experiments. In the following four series, a single distracting cross (with different orientation) was attached to one of the lateral stimulus terminators and various combinations of the constant and background luminance were used. To interpret the experimental data, we used the basic computational principles of previously developed quantitative models of hypothetical visual mechanisms underlying the emergence of the Müller-Lyer illusion and the filled-space illusion. It was shown that the results of theoretical calculations adequately approximate the experimental curves obtained for all modifications of stimuli, which strongly supports the suggestion that the joint manifestations of these two illusions can be considered among the main factors determining the features of the illusion investigated.

Background. Congenital heart defects are the important morbidity and mortality factors for several severe clinical conditions. This birth defect is detected and evaluated by experts using heart auscultation. However, despite the reported quite good agreement between the experts about the general diagnosis, there are no clearly described features or quantitative parameters which can be used for classification. Cardiologists are ‘looking for’ and subjectively grading the audibility of whooshing or swishing sounds heard during the heartbeat. The sought sounds indicate that blood is flowing abnormally across heart valves due to structural heart problems. Machine learning models trained with optimal feature sets should be capable of classifying phonocardiogram signals into ‘reflecting heart defect’ and ‘normal’ ones. By implementing such a classification model into mobile devices or cloud-based systems, we can realize rapid and robust screening of disorders of the mechanical function of the heart in the paediatric population could be realized. Aim. To elaborate optimal phonocardiogram feature set and classification model to detect disorders of the mechanical function of the heart. Methods. We used annotated phonocardiogram recordings of 1568 patients collected from paediatric population screening campaigns conducted in Northeast Brazil in July–August 2014 and June–July 2015, provided by Physionet’s open clinical database. Convolutional neural network elaboration strategy, usually used for image classification or recognition, was applied for the classification of 2-dimensional arrays of time-frequency estimates of signal recording excerpts, constructed by means of continuous wavelet transform and special data pre-processing. Results. 5-fold cross-validation of elaborated algorithm performance showed an accuracy of 0.777 ± 0.03. The accuracy of the algorithm tested with ‘never seen’ data was 0.671. The algorithm was evaluated in the Physionet 2022 challenge and ranked in 23rd place out of 44 participants by classification performance. At the same time, the algorithm required comparatively low computational resources. Conclusion. Due to comparatively low required computational resources and good classification accuracy, the elaborated algorithm is suitable for deployment into mobile diagnostic devices or cloud-based systems for paediatric populational screening.

Abstract: Physionet, web-based archive of biomedical and physiological signals, and Computing in cardiology conferense have organized annual cardiological origin data analysis challanges for more than 20 years. The aim of the challanges is to encourage participants to develop algorithmic approaches tackling clinically interesting questions that remain unsolved. Challenge 2022 was focused on heart murmur (an extra, unusual sound in heartbeat caused by abnormal blood flow over heart valves) detection from phonocardiogram recordings. Participants were asked to design and implement a working, open-source algorithm that, based only on the provided recordings and routine demographic data, can determine whether any murmurs are audible from a patient’s recordings. The lack of early diagnoses of these conditions represents a major public health problem, especially in underprivileged countries with high birth rates. Our approach to the problem consisted of continuous wavelet transform (CWT) technique to form features from the given data set and convolutional neural network (CNN) to classify patients to “murmur-absent” or “murmur-present”. A total of 87 teams submitted 779 algorithms during the course of the Challenge, including 81 teams with 167 successful entries during the unofficial phase and 63 teams with 306 successful entries during the official phase. Only 40 teams had a successful entry for the murmur detection task on the test set for the murmur detection task and met the other Challenge criteria for ranking. Our teams proposed algorithm was evaluated on the hidden test set and received a weighted accuracy score of 0.671 (ranked 23th out of 40 teams). The highest weighted accuracy metric score was received by team “HearHeart” 0.780. Proposed algorithms can lower healthcare costs and increase the accessibility of cardiac screening and care for patients with abnormal cardiac function in low-resourced environments.

Mokomoji knyga „Optiniai tyrimo metodai“ skirta biomedicinos krypties LSMU ir KTU studentams, studijuojantiems medicinos fizikos, biologinės fizikos, sveikatos fizikos ir fizikos dalykus. Knygoje aprašomi optiniai reiškiniai ir jiems būdingi dėsniai, kuriais pagrįsti medžiagų ir tirpalų analizės, gydymo ir diagnostikos metodai. Jų supratimas padės kelti profesionalumo ir kūrybiškumo lygį medicinos profesinėje veikloje. Knygą sudaro šeši skyriai, skirti tiesiaeigio šviesos spindulių sklidimo ir jų eigos lęšiuose reiškiniams, šviesos lūžiui, šviesos poliarizacijai, šviesos spinduliavimo, sugerties, sklaidos, difrakcijos, atspindžio ir pralaidumo reiškiniams, visiškojo vidaus atspindžio reiškiniui, kvantinei šviesos prigimčiai. Kiekvieno skyriaus gale pateikiami klausimai ir uždaviniai, kurių sprendimas padės įtvirtinti įgytas žinias.

In this study, we applied a mathematical model to explore the mechanism and factors leading to phase separation and the formation of branching structures with nanocolumns extending from larger clusters formed on the substrate of a grown film. The mathematical model simulated the growth of a thin film over time by using partial differential equations, including the processes of adsorption, phase separation, and diffusion due to the curvature of the thin film surface. The modeling results revealed the possible mechanism that could lead to the formation of the described branching structures. That mechanism can be divided into two main steps. The first step is the growth of a relatively large cluster (of a component that makes up the branching phase) on the substrate during the initial growth stages. The second step is the division process of that large cluster into smaller clusters in the later growth stages. The model parameters influencing the growth conditions that lead to the formation mechanism of the branching structures were determined, and their influences on the phase structure were analyzed.

The heart auscultation signal contains strong beats representing cardiac valve closures and the murmur sounds (if present). These key-components of the signal differ in time and frequency, therefore continuous wavelet transform (CWT) was proposed for features formation. The result of CWT of randomly taken excerpt of the signal is two-dimensional array. It contains bold areas of high value estimates representing the strong beats and some areas of moderate values representing murmur sounds in case they are present. Strong beat representations in these arrays give the time marks for the eventual representations of sought murmur sounds. Therefore, we did not do the signal segmentation, but we calculate CWT results of sliding-overlapping windows along the whole signal instead. For final analysis we use CWT-results per recording, having lowest, non-zero entropy. Therefore, we get rid of noisy or corrupted signal parts. The convolutional neural network does the final classification. We used the same convolutional neural network and CWT features to classify patient’s clinical outcomes. Algorithm was tested on the George B. Moody PhysioNet Challenge 2022 hidden test set. “LSMU” team’s murmur classifier received a weighted accuracy score of 0.671 (ranked 23th out of 40 teams) and Challenge cost score of 15402 (ranked 35th out of 39 teams). Introduction Cardiac auscultation is the simplest and most cost effective method of screening for a large number of heart disorders, including arrhythmia and valve disease. The method could be effectively used even for late postoperative diagnostics after valve replacements [1]. However, heart sounds are difficult to identify and analyze because significant events are closely spaced or even overlapped in time, and their frequency content is at the lower end of the audible frequency range [2]. Experienced cardiologists classify heart auscultation signals with great agreement between each other, at the same time facing difficulties to precise verbally the key features they use. Therefore on average, only 20% of medical interns can effectively detect heart conditions using auscultation [3]. Machine learning algorithms trained on experts annotated data could be a valuable tool for clinical decision support increasing reliability of cardiac diagnostics. The George B. Moody PhysioNet Challenge 2022 [4] invited participants to identify murmurs and clinical outcomes using heart sound recordings collected from multiple auscultation locations. We propose here the convolutional neural network approach for heart murmur detection and clinical outcome prediction using wavelet transform based features extracted from auscultation signals. […].

Mokomoji knyga "Mechanika. Svyravimai, bangos, skysčių tekėjimas" yra skirta medicinos, veterinarijos, sveikatos mokslų, odontologijos ir farmacijos specialybių studentams, studijuojantiems medicinos fizikos, biologinės fizikos, sveikatos fizikos ir taikomosios fizikos dalykus. Knygoje aprašomi mechaninių svyravimų, bangų sklidimo vientisose aplinkose, skysčių tekėjimo reiškiniai, atskleidžiami jiems būdingi dėsniai ir jų svarba gyvo organizmo veikloje ir biomedicinos praktikoje. Sėkmingas šio kurso įsisavinimas padės studentams ne tik pagilinti ir išplėsti supratimą apie minėtuosius reiškinius ir dėsnius, sujungti juos į vieningą sistemą, bet ir rasti jųš vietą studijuojamų specialybių žinių ir metodų sistemoje. Įgytos žinios padės kūrybiškai taikyti įvairius analizės, gydymo ir diagnostikos metodus, taigi, bus naudingos profesinėje veikloje. Žvaigždutėmis pažymėti skyreliai yra skirit papildomam skaitymui ir akiračio plėtimui.

The chapter reviews results of investigation of the cholinergic modulation of excitatory synaptic transmission in the frog tectum gained in Laboratory of Neurophysiology, Lithuanian University of Health Sciences. Experiments were done in vivo on common grass frogs Rana temporaria. A certain level of the endogenous acetylcholine exists in the frog tectum at normal physiological conditions due to a persistent activity of cholinergic nuclei of the frog brain, primarily the nucleus isthmi. We have demonstrated that this background acetylcholine activates presynaptic nicotinic receptors, causing the steady potentiation of the retinotectal synaptic transmission up to 1.7 times. We have called this type of nicotinic potentiation tonic nicotinic potentiation and corresponding presynaptic receptors - tonic nicotinic receptors. Results of the experiments have demonstrated that not only glumtamate as a main mediator but also acetylcholine as a co-mediator release from the retinotectal synapses during firing of the retinotectal fiber. This co-released scetylcholine adds to background acetylcholine increasing the extracellular concentration of the acetylcholine. [...].

Mokomojoje knygoje aprašyti gyvojo organizmo skleidžiamo biosignalai, jų registravimo būdai, elektrinės audinių stimuliacijos pagrindai, kintamosios srovės tekėjimo elektrine grandine dėsningumai. Aptariamas bioelektrinių signalų panaudojimas ligoms diagnozuoti ir gydyti. Aprašyti laboratoriniai darbai, padėsiantys giliau suprasti minėtus reiškinius. mokomoji knyga skirta biomedicinos ir sveikatos mokslų studentams, studijuojantiems medicinos, biologinės fizikos, sveikatos fizikos ir taikomosios fizikos dalykus.