Artificial neural network for atrial fibrillation identification in portable devices

none6siAtrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1%–93.0%), 90.2% (CI: 86.2%–94.3%) and 90.8% (CI: 88.1%–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.openMarinucci D.; Sbrollini A.; Marcantoni I.; Morettini M.; Swenne C.A.; Burattini L.Marinucci, D.; Sbrollini, A.; Marcantoni, I.; Morettini, M.; Swenne, C. A.; Burattini, L

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.

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD); and most existing algorithms are usually designed for the diagnosis (i.e.; feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper; we utilized the MIT-BIH AF Database (AFDB); which is composed of data from normal people and patients with AF and onset characteristics; and the AFPDB database (i.e.; PAF Prediction Challenge Database); which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF); and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction; we regarded diagnosis and prediction as two classification problems; adopted the traditional support vector machine (SVM) algorithm; and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process; the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases; the sensitivity; specificity; and accuracy measures were 99.2% and 99.2%; 99.2% and 93.3%; and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases; respectively. Moreover; the sensitivity; specificity; and accuracy were 94.2%; 79.7%; and 87.0%; respectively; when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels

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

Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection

Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely differed when applying on the independent test datasets. This situation is more significant for AF detection from dynamic ECGs. In this study, we explored two potential training strategies to address the over-fitting problem in AF detection. The first one is to use the Fast Fourier transform (FFT) and Hanning-window-based filter to suppress the influence from individual difference. Another is to train the model on the wearable ECG data to improve the robustness of model. Wearable ECG data from 29 patients with arrhythmia were collected for at least 24 h. To verify the effectiveness of the training strategies, a Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN)-based model was proposed and tested. We tested the model on the independent wearable ECG data set, as well as the MIT-BIH Atrial Fibrillation database and PhysioNet/Computing in Cardiology Challenge 2017 database. The model achieved 96.23%, 95.44%, and 95.28% accuracy rates on the three databases, respectively. Pertaining to the comparison of the accuracy rates on each training set, the accuracy of the model trained in conjunction with the proposed training strategies only reduced by 2%, while the accuracy of the model trained without the training strategies decreased by approximately 15%. Therefore, the proposed training strategies serve as effective mechanisms for devising a robust AF detector and significantly enhanced the detection accuracy rates of the resulting deep networks

Noninvasive Dynamic Characterization of Swallowing Kinematics and Impairments in High Resolution Cervical Auscultation via Deep Learning

Swallowing is a complex sensorimotor activity by which food and liquids are transferred from the oral cavity to the stomach. Swallowing requires the coordination between multiple subsystems which makes it subject to impairment secondary to a variety of medical or surgically related conditions. Dysphagia refers to any swallowing disorder and is common in patients with head and neck cancer and neurological conditions such as stroke. Dysphagia affects nearly 9 million adults and causes death for more than 60,000 yearly in the US. In this research, we utilize advanced signal processing techniques with sensor technology and deep learning methods to develop a noninvasive and widely available tool for the evaluation and diagnosis of swallowing problems. We investigate the use of modern spectral estimation methods in addition to convolutional recurrent neural networks to demarcate and localize the important swallowing physiological events that contribute to airway protection solely based on signals collected from non-invasive sensors attached to the anterior neck. These events include the full swallowing activity, upper esophageal sphincter opening duration and maximal opening diameter, and aspiration. We believe that combining sensor technology and state of the art deep learning architectures specialized in time series analysis, will help achieve great advances for dysphagia detection and management in terms of non-invasiveness, portability, and availability. Like never before, such advances will enable patients to get continuous feedback about their swallowing out of standard clinical care setting which will extremely facilitate their daily activities and enhance the quality of their lives

Cardiovascular Disorder Detection with a PSO-Optimized Bi-LSTM Recurrent Neural Network Model

The medical community is facing ever-increasing difficulties in identifying and treating cardiovascular diseases. The World Health Organization (WHO) reports that despite the availability of numerous high-priced medical remedies for persons with heart problems, CVDs continue to be the main cause of mortality globally, accounting for over 21 million deaths annually. When cardiovascular diseases are identified and treated early on, they cause far fewer deaths. Deep learning models have facilitated automated diagnostic methods for early detection of these diseases. Cardiovascular diseases often present insidious symptoms that are difficult to identify in a timely manner. Prompt diagnosis of individuals with CVD and related conditions, such as high blood pressure or high cholesterol, is crucial to initiate appropriate treatment. Recurrent neural networks (RNNs) with gated recurrent units (GRUs) have recently emerged as a more advanced variant, capable of surpassing Long Short-Term Memory (LSTM) models in several applications. When compared to LSTMs, GRUs have the advantages of faster calculation and less memory usage. When it comes to CVD prediction, the bio-inspired Particle Swarm Optimization (PSO) algorithm provides a straightforward method of getting the best possible outcomes with minimal effort. This stochastic optimization method requires neither the gradient nor any differentiated form of the objective function and emulates the behaviour and intelligence of swarms. PSO employs a swarm of agents, called particles, that navigate the search space to find the best prediction type.This study primarily focuses on predicting cardiovascular diseases using effective feature selection and classification methods. For CVD forecasting, we offer a GRU model built on recurrent neural networks and optimized with particle swarms (RNN-GRU-PSO). We find that the proposed model significantly outperforms the state-of-the-art models (98.2% accuracy in predicting cardiovascular diseases) in a head-to-head comparison