1,031 research outputs found
ΠΠ±Π·ΠΎΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎ Π½Π΅ΠΎΠ±Ρ ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΠΈ
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.Β Β 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):380β409. (In Russ.) DOI: 10.15826/ urej.2021.5.4.004.Β Π€ΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΡ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠΎΠ² ΡΠ΅ΡΠ΄ΡΠ° ΡΡΠΈΡΠ°Π΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΠΎ Π²ΡΡΡΠ΅ΡΠ°ΡΡΠ΅ΠΉΡΡ ΠΏΡΠΈΡΠΈΠ½ΠΎΠΉ Π²Π½Π΅Π·Π°ΠΏΠ½ΠΎΠΉ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΡΠ΅ΡΠ΄ΡΠ°. Π’Π°ΠΊΠ°Ρ ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΡ ΠΈ ΡΠ°ΡΡΠΎ ΠΏΡΠ΅Π΄ΡΠ΅ΡΡΠ²ΡΡΡΠ°Ρ Π΅ΠΉ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠΎΠ²Π°Ρ ΡΠ°Ρ
ΠΈΠΊΠ°ΡΠ΄ΠΈΡ β ΡΡΠΎ ΡΠΈΡΠΌΡ ΡΠ΅ΡΠ΄ΡΠ°, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ ΡΠ΅Π°Π³ΠΈΡΠΎΠ²Π°ΡΡ Π½Π° ΡΠΊΡΡΡΠ΅Π½Π½ΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠΎΠΊΠΎΠ²ΡΡ ΡΠ΅ΡΠ°ΠΏΠΈΡ ΠΈ Π²Π΅ΡΠ½ΡΡΡΡΡ ΠΊ Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΠΎΠΌΡ ΡΠΈΠ½ΡΡΠΎΠ²ΠΎΠΌΡ ΡΠΈΡΠΌΡ ΠΏΡΠΈ ΡΠ°Π½Π½Π΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΏΠΎΡΠ»Π΅ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΡΠ΅ΡΠ΄ΡΠ° Ρ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΠΌ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎΠΉ Π½Π°ΡΠΎΡΠ½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΡΠ΅ΡΠ΄ΡΠ°. ΠΠ΄Π½Π°ΠΊΠΎ ΡΡΡΠ½Π°Ρ ΠΏΡΠΎΠ²Π΅ΡΠΊΠ° ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΠΠ Π½Π° Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΏΠ°ΡΡΠ΅ΡΠ½Π° ΡΠ°ΠΊΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π°Π΄Π°ΡΠ΅ΠΉ, ΡΡΠ΅Π±ΡΡΡΠ΅ΠΉ Π½Π΅ΠΌΠ΅Π΄Π»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π² ΡΡΡΠ΅ΡΡΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ, ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅Π²ΡΠΏΠΎΠ»Π½ΠΈΠΌΠΎΠΉ Π² ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ ΠΊΠ²Π°Π»ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠ³ΠΎ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ°. ΠΠΎΡΡΠΎΠΌΡ Π΄Π»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΎΡΡΡΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡΡΠΈΠ»ΠΈ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π°ΡΠΈΡΠΌΠΈΠΉ Ρ ΡΡΠ½ΠΊΡΠΈΠ΅ΠΉ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΡΠ΅ΡΠ°ΠΏΠΈΠΈ Ρ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ Π²ΡΡΠΎΠΊΠΎΠ²ΠΎΠ»ΡΡΠ½ΠΎΠ³ΠΎ ΠΈΠΌΠΏΡΠ»ΡΡΠ°, Π²ΡΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΠΎ Π΄Π»Ρ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°, ΡΠ½ΠΈΠΌΠ°Π΅ΠΌΠΎΠ³ΠΎ Ρ ΡΠ»Π΅ΠΊΡΡΠΎΠ΄ΠΎΠ² Π½Π°ΡΡΠΆΠ½ΠΎΠ³ΠΎ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΠ»ΠΈ ΠΏΠΎΠ»ΡΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΎΡΠ°, Ρ ΡΠ΅Π»ΡΡ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΎΠΊΠ°Π·Π°Π½ΠΈΡ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΠΈ, ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡΠ΅ Π²ΠΎ Π²ΡΡΡΠΎΠ΅Π½Π½ΠΎΠΌ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΌ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΠΏΠΎΠ»ΡΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²Π½Π΅ΡΠ½ΠΈΡ
Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΎΡΠΎΠ². Π Π°Π±ΠΎΡΠ° Π²ΠΊΠ»ΡΡΠ°Π΅Ρ ΠΎΠ±Π·ΠΎΡ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠΈΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ, ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈ ΡΠΆΠ°ΡΠΈΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π»Ρ ΡΠΈΠ³Π½Π°Π»Π° ΠΠΠ.Β Β ΠΠΈΠΏΡΠ°ΠΊ Π. Π., Π§ΡΠΏΠΎΠ² Π. Π. ΠΠ±Π·ΠΎΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΠΈ. Ural Radio Engineering Journal. 2021;5(4):380β409. DOI: 10.15826/urej.2021.5.4.004.
Machine learning techniques for arrhythmic risk stratification: a review of the literature
Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to cliniciansβ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice
Personal heart monitoring and rehabilitation system using smart phones
This paper discusses a personalized heart monitoring system using smart phones and wireless (bio) sensors. Based on several scenarios we present the functionality of a prototype we have built. The application is capable of monitoring the health of high risk cardiac patients. The smart phone application analyses in real-time sensor and environmental data and can automatically alert the ambulance and pre assigned caregivers when a heart patient is in danger. It also transmits sensor data to a healthcare centre for remote monitoring by a nurse or cardiologist. The system can be personalized and rehabilitation programs can monitor the progress of a patient. Rehabilitation programs can be used to give advice (e.g. exercise more) or to reassure the patient. Β© 2006 IEEE
Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia
Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.This study was supported by the Ministerio de EconomΓa, Industria y Competitividad, Gobierno de EspaΓ±a (ES) (TEC-2015-64678-R) to UI and EA and by Euskal Herriko Unibertsitatea (ES) (GIU17/031) to UI and EA. The funders, Tecnalia Research and Innovation and Banco Bilbao Vizcaya Argentaria (BBVA), provided support in the form of salaries for authors AP, AA, FAA, CF, EG, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the author contributions section
Classification techniques for arrhythmia patterns using convolutional neural networks and Internet of Things (IoT) devices
The rise of Telemedicine has revolutionized how patients are being treated, leading to several advantages such as enhanced health analysis tools, accessible remote healthcare, basic diagnostic of health parameters, etc. The advent of the Internet of Things (IoT), Artificial Intelligence (AI) and their incorporation into Telemedicine extends the potential of health benefits of Telemedicine even further. Therefore, the synergy between AI, IoT, and Telemedicine creates diverse innovative scenarios for integrating cyber-physical systems into medical health to provide remote monitoring and interactive assistance to patients. Data from World Health Organization reports that 7.4 million people died because of Atrial Fibrillation (AF), recognizing the most common arrhythmia associated with human heart rate. Causes like unhealthy diet, smoking, poor resources to go to the doctor and based on research studies, about 12 and 17.9 million of people will be suffering the AF in the USA and Europe, in 2050 and 2060, respectively. The AF as a cardiovascular disease is becoming an important public health issue to tackle. By using a systematic approach, this paper reviews recent contributions related to the acquisition of heart beats, arrhythmia detection, IoT, and visualization. In particular, by analysing the most closely related papers on Convolutional Neural Network (CNN) and IoT devices in heart disease diagnostics, we present a summary of the main research gaps with suggested directions for future research
Design of an artificial neural network and feature extraction to identify arrhythmias from ECG
This paper presents a design of an artificial neural network (ANN) and feature extraction methods to identify two types of arrhythmias in datasets obtained through electrocardiography (ECG) signals, namely arrhythmia dataset (AD) and supraventricular arrhythmia dataset (SAD). No special ANN toolkit was used; instead, each neuron and necessary calculus were modeled and individually programmed. Thus, four temporal-based features are used: heart rate (HR), R-peaks root mean square (R-RMS), RR-peaks variance (RR-VAR), and QSR-complex standard deviation (QSR-SD). The network architecture presents four neurons in the input layer, eight in hidden layer and an output layer with two neurons. The proposed classification method uses the MIT-BIH Dataset (Massachusetts Institute of Technology-Beth Israel Hospital) for training, validation and execution or test phases. Preliminary results show the high efficiency of the proposed ANN design and its classification method, reaching accuracies between 98.76% and 98.91%, when in the identification of NSRD and arrhythmic ECG; and accuracies of 86.37% (AD) and 76.35% (SAD), when analyzing only classifications between both arrhythmias.info:eu-repo/semantics/acceptedVersio
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. Β© 2022 IEEE.Russian Foundation for Basic Research, Π Π€Π€Π, (20-37-90037)The reported study is funded by RFBR according to research project No. 20-37-90037
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