Обзор методов автоматической диагностики сердечной аритмии для принятия решений о необходимости проведения дефибрилляции

Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. The fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that may respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals on the existence of a pattern of such arrhythmias is a risky and time-consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, systems of the computer classification of arrhythmias with the function of making a decision on the necessity of electric cardioversion with the parameters of a high-voltage pulse calculated adaptively for each patient are widely used for the automatic diagnosis of such conditions. This paper discusses methods of analyzing the electrocardiographic signal taken from the electrodes of an external automatic or semi-automatic defibrillator in order to make a decision on the necessity for defibrillation, which are applicable in the embedded software of automatic and semiautomatic external defibrillators. The paper includes an overview of applicable filtering techniques as well as subsequent algorithms for extracting, classifying and compressing features for the ECG signal.  Lipchak D. A., Chupov A. A. Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review. Ural Radio Engineering Journal. 2021;5(4):380–409. (In Russ.) DOI: 10.15826/ urej.2021.5.4.004. Фибрилляция желудочков сердца считается наиболее часто встречающейся причиной внезапной остановки сердца. Такая фибрилляция и часто предшествующая ей желудочковая тахикардия – это ритмы сердца, которые могут реагировать на экстренную электрошоковую терапию и вернуться к нормальному синусовому ритму при ранней диагностике после остановки сердца с восстановлением адекватной насосной функции сердца. Однако ручная проверка сигналов ЭКГ на наличие паттерна такой аритмии является сложной аналитической задачей, требующей немедленного принятия решения в стрессовой ситуации, практически невыполнимой в отсутствие квалифицированного медицинского специалиста. Поэтому для автоматической диагностики острых состояний широкое применение получили системы компьютерной классификации аритмий с функцией принятия решения о необходимости проведения электрокардиотерапии с параметрами высоковольтного импульса, вычисленного адаптивно для каждого пациента. В данной работе рассмотрены методы анализа электрокардиографического сигнала, снимаемого с электродов наружного автоматического или полуавтоматического дефибриллятора, с целью принятия решения о необходимости оказания дефибрилляции, применимые во встроенном программном обеспечении автоматических и полуавтоматических внешних дефибрилляторов. Работа включает обзор применимых методов фильтрации, а также последующих алгоритмов извлечения, классификации и сжатия характерных признаков для сигнала ЭКГ.  Липчак Д. А., Чупов А. А. Обзор методов автоматической диагностики сердечной аритмии для принятия решений о необходимости проведения дефибрилляции. Ural Radio Engineering Journal. 2021;5(4):380–409. DOI: 10.15826/urej.2021.5.4.004.

Facial emotion recognition using min-max similarity classifier

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods

Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review

Поступила: 19.12.2021. Принята в печать: 18.01.2022.Received: 19.12.2021. Accepted: 18.01.2022.Фибрилляция желудочков сердца считается наиболее часто встречающейся причиной внезапной остановки сердца. Такая фибрилляция и часто предшествующая ей желудочковая тахикардия – это ритмы сердца, которые могут реагировать на экстренную электрошоковую терапию и вернуться к нормальному синусовому ритму при ранней диагностике после остановки сердца с восстановлением адекватной насосной функции сердца. Однако ручная проверка сигналов ЭКГ на наличие паттерна такой аритмии является сложной аналитической задачей, требующей немедленного принятия решения в стрессовой ситуации, практически невыполнимой в отсутствие квалифицированного медицинского специалиста. Поэтому для автоматической диагностики острых состояний широкое применение получили системы компьютерной классификации аритмий с функцией принятия решения о необходимости проведения электрокардиотерапии с параметрами высоковольтного импульса, вычисленного адаптивно для каждого пациента. В данной работе рассмотрены методы анализа электрокардиографического сигнала, снимаемого с электродов наружного автоматического или полуавтоматического дефибриллятора, с целью принятия решения о необходимости оказания дефибрилляции, применимые во встроенном программном обеспечении автоматических и полуавтоматических внешних дефибрилляторов. Работа включает обзор применимых методов фильтрации, а также последующих алгоритмов извлечения, классификации и сжатия характерных признаков для сигнала ЭКГ.Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. The fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that may respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals on the existence of a pattern of such arrhythmias is a risky and time-consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, systems of the computer classification of arrhythmias with the function of making a decision on the necessity of electric cardioversion with the parameters of a high-voltage pulse calculated adaptively for each patient are widely used for the automatic diagnosis of such conditions. This paper discusses methods of analyzing the electrocardiographic signal taken from the electrodes of an external automatic or semi-automatic defibrillator in order to make a decision on the necessity for defibrillation, which are applicable in the embedded software of automatic and semiautomatic external defibrillators. The paper includes an overview of applicable filtering techniques as well as subsequent algorithms for extracting, classifying and compressing features for the ECG signal

Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review

Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. Ventricular fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that can respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals for the presence of a pattern of such arrhythmias is a risky and time- consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, for the automatic diagnosis of such conditions, systems for the computer classification of arrhythmias to decide on the need for electric cardioversion with the parameters of a high-voltage pulse, calculated adaptively for each patient, are widely used. This paper discusses methods for analyzing the electrocardiographic signal taken from external automatic or semi-automatic defibrillator electrodes to decide the need for defibrillation, which is applicable in the embedded software of automatic, semi-automatic external defibrillators. The paper includes an overview of applicable filtering techniques and subsequent algorithms for extracting, classifying, and compressing features for the ECG signal. Both advantages and disadvantages are discussed for the studied algorithms. © 2022 IEEE.Russian Foundation for Basic Research, РФФИ, (20-37-90037)The reported study is funded by RFBR according to research project No. 20-37-90037

Identification of serum microRNAs as potential biomarkers in Pompe disease

Altres ajuts: This study was supported by a grant from Sanofi-Genzyme (GZ-2015-11342) to Dr. Gallardo and has been registered in Clinicaltrials.gov (identifier NCT03045042).This study was supported by a grant from Sanofi-Genzyme (GZ-2015-11342) to Dr. Gallardo and has been registered in Clinicaltrials.gov (identifier NCT03045042).To analyze the microRNA profile in serum of patients with Adult Onset Pompe disease (AOPD). We analyzed the expression of 185 microRNAs in serum of 15 AOPD patients and five controls using microRNA PCR Panels. The expression levels of microRNAs that were deregulated were further studied in 35 AOPD patients and 10 controls using Real-Time PCR. Additionally, the skeletal muscle expression of microRNAs which showed significant increase levels in serum samples was also studied. Correlations between microRNA serum levels and muscle function test, spirometry, and quantitative muscle MRI were performed (these data correspond to the study NCT01914536 at ClinicalTrials.gov). We identified 14 microRNAs that showed different expression levels in serum samples of AOPD patients compared to controls. We validated these results in a larger cohort of patients and we found increased levels of three microRNAs, the so called dystromirs: miR-1-3p, miR-133a-3p, and miR-206. These microRNAs are involved in muscle regeneration and the expression of these was increased in patients' muscle biopsies. Significant correlations between microRNA levels and muscle function test were found. Serum expression levels of dystromirs may represent additional biomarkers for the follow-up of AOPD patients

Data mining analyses for precision medicine in acromegaly: a proof of concept

Predicting which acromegaly patients could benefit from somatostatin receptor ligands (SRL) is a must for personalized medicine. Although many biomarkers linked to SRL response have been identified, there is no consensus criterion on how to assign this pharmacologic treatment according to biomarker levels. Our aim is to provide better predictive tools for an accurate acromegaly patient stratification regarding the ability to respond to SRL. We took advantage of a multicenter study of 71 acromegaly patients and we used advanced mathematical modelling to predict SRL response combining molecular and clinical information. Different models of patient stratification were obtained, with a much higher accuracy when the studied cohort is fragmented according to relevant clinical characteristics. Considering all the models, a patient stratification based on the extrasellar growth of the tumor, sex, age and the expression of E-cadherin, GHRL, IN1-GHRL, DRD2, SSTR5 and PEBP1 is proposed, with accuracies that stand between 71 to 95%. In conclusion, the use of data mining could be very useful for implementation of personalized medicine in acromegaly through an interdisciplinary work between computer science, mathematics, biology and medicine. This new methodology opens a door to more precise and personalized medicine for acromegaly patients