scholarly journals Automatic detection of Atrial Fibrillation

2021 ◽  
Author(s):  
◽  
Greg Hayes
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Health Sector ◽  
Wearable Device ◽  
P Wave ◽  
Support Vector ◽  
Wave Form ◽  
Discrete Wavelet ◽  
Rr Interval ◽  
The Individual

<p>Atrial Fibrillation is an abnormal arrhythmia of the heart and is a growingconcern in the health sector affecting 1% of the population. The incidenceof atrial fibrillation increases with age and has been found to be more detri-mental to long term cardiac health than previously thought. Sufferers arefive times more likely to experience a stroke than others. Often, atrial fib-rillation is asymptomatic and is frequently discovered only when a patient visits a hospital for other reasons. The detection of paroxysmal atrial fib-rillation can be difficult. Holter monitors are used to record the ECG overlong periods of time, but the resulting recording still needs to be analysed.This can be a time consuming task and one prone to errors. If a miniature,low-power, wearable device could be designed to detect and record whena heart experiences atrial fibrillation, then health professionals would havemore timely information to carry out better, more cost effective courses of treatment. This thesis presents progress towards development of such a device. Atrial fibrillation is characterised by random RR interval, missing Pwave and presence of atrial activity. The detection of the P wave and atrialactivity can be unreliable due to low signal levels and differences in wave-form morphology between subjects. The random RR interval appears tobe a more reliable method of detection. By analysing the ECG signal inboth the frequency and time domains, feature sets can be extracted for thedetection process. In this research, the Discrete Wavelet Transform is used to generate several sub-bands for analysing wave form morphology, and anumber of RR interval metrics are created for analysing the rhythm. All features are further processed and presented to a support vector machine classification stage for the ultimate detection of atrial fibrillation. Forty eight files from the MITDB database of the PhysioNet online ECG reposi-tory were downloaded and processed to form separate training and test-ing data sets. Overall classification accuracy for normal sinus rhythm was93% sensitivity and 95% specificity, and for atrial fibrillation, 95% sensitiv-ity and 93% specificity. These results were found to be sensitive to the ECG morphology of the individual subjects. This means that the system either needs to be trained on a greater number of ECG morphologies or perhaps trained on the morphology of the individual under investigation. Putting this issue aside, the research to date shows that it is reasonable to expect a small, low powered, wearable device, to be capable of automatically detecting whena heart experiences atrial fibrillation.</p>

scholarly journals Automatic detection of Atrial Fibrillation

2021 ◽  
Author(s):  
◽  
Greg Hayes
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Health Sector ◽  
Wearable Device ◽  
P Wave ◽  
Support Vector ◽  
Wave Form ◽  
Discrete Wavelet ◽  
Rr Interval ◽  
The Individual

<p>Atrial Fibrillation is an abnormal arrhythmia of the heart and is a growingconcern in the health sector affecting 1% of the population. The incidenceof atrial fibrillation increases with age and has been found to be more detri-mental to long term cardiac health than previously thought. Sufferers arefive times more likely to experience a stroke than others. Often, atrial fib-rillation is asymptomatic and is frequently discovered only when a patient visits a hospital for other reasons. The detection of paroxysmal atrial fib-rillation can be difficult. Holter monitors are used to record the ECG overlong periods of time, but the resulting recording still needs to be analysed.This can be a time consuming task and one prone to errors. If a miniature,low-power, wearable device could be designed to detect and record whena heart experiences atrial fibrillation, then health professionals would havemore timely information to carry out better, more cost effective courses of treatment. This thesis presents progress towards development of such a device. Atrial fibrillation is characterised by random RR interval, missing Pwave and presence of atrial activity. The detection of the P wave and atrialactivity can be unreliable due to low signal levels and differences in wave-form morphology between subjects. The random RR interval appears tobe a more reliable method of detection. By analysing the ECG signal inboth the frequency and time domains, feature sets can be extracted for thedetection process. In this research, the Discrete Wavelet Transform is used to generate several sub-bands for analysing wave form morphology, and anumber of RR interval metrics are created for analysing the rhythm. All features are further processed and presented to a support vector machine classification stage for the ultimate detection of atrial fibrillation. Forty eight files from the MITDB database of the PhysioNet online ECG reposi-tory were downloaded and processed to form separate training and test-ing data sets. Overall classification accuracy for normal sinus rhythm was93% sensitivity and 95% specificity, and for atrial fibrillation, 95% sensitiv-ity and 93% specificity. These results were found to be sensitive to the ECG morphology of the individual subjects. This means that the system either needs to be trained on a greater number of ECG morphologies or perhaps trained on the morphology of the individual under investigation. Putting this issue aside, the research to date shows that it is reasonable to expect a small, low powered, wearable device, to be capable of automatically detecting whena heart experiences atrial fibrillation.</p>

scholarly journals Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning

2021 ◽  
Author(s):  
Yunendah Nur Fuadah ◽  
Ki Moo Lim
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Congestive Heart Failure ◽  
Feature Extraction ◽  
Selection Process ◽  
Normal Sinus Rhythm ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Cardiovascular Disorders ◽  
Ecg Signals

Abstract Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are the major causes of mortality worldwide. The diagnosis of cardiovascular disorders is heavily reliant on electrocardiogram (ECG) signals. Therefore, extracting significant features from ECG signals is the most challenging aspect to represent each condition of the ECG signals. Earlier studies have claimed that the Hjorth descriptor is assigned as a simple feature extraction algorithm that has the capability of class separation among AF, CHF, and normal sinus rhythm (NSR) conditions. However, owing to noise interference, certain features do not represent the characteristics of the ECG signals. This study addressed this important gap by applying a discrete wavelet transform (DWT) prior to applying the Hjorth descriptor as a feature extraction method. Furthermore, the feature selection process and optimization of various classifier algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), and artificial neural network (ANN), were investigated to provide the best system performance. This study obtained accuracies of 95 %, 92 %, and 95 % for the k-NN, SVM, and ANN classifiers, respectively. The results demonstrated that the optimization of the classifier algorithm could improve the classification accuracy of AF, CHF, and NSR conditions, compared to earlier studies.

scholarly journals An Atrial Fibrillation with Normal Limits using Signal Processing- A Review

2018 ◽  
Vol 7 (2.24) ◽  
pp. 453
Author(s):  
S. Sathish ◽  
K Mohanasundaram
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
P Wave ◽  
Wave Duration ◽  
Normal Limits ◽  
Normal Sinus ◽  
P Wave Duration ◽  
Standard Lead ◽  
Wave Morphology

Atrial fibrillation is an irregular heartbeat (arrhythmia) that can lead to the stroke, blood clots, heart failure and other heart related complications. This causes the symptoms like rapid and irregular heartbeat, fluttering, shortness of breath etc. In India for every around 4000 people eight of them are suffering from Atrial Fibrillation. P-wave Morphology.  Abnormality of P-wave (Atrial ECG components) seen during sinus rhythm are associated with Atrial fibrillation. P-wave duration is the best predictor of preoperative atrial fibrillation. but the small amplitudes of atrial ECG and its gradual increase from isometric line create difficulties in defining the onset of P wave in the Standard Lead Limb system (SLL).Studies shows that prolonged P-wave have duration in patients (PAF) In this Study, a Modified Lead Limb (MLL) which solves the practical difficulties in analyzing the P-ta interval for both in healthy subjects and Atrial Fibrillation patients. P-Ta wave interval and P-wave duration can be estimated with following proposed steps which is applicable for both filtered and unfiltered atrial ECG components which follows as the clinical database trials. For the same the p-wave fibrillated signals that escalates the diagnosis follows by providing minimal energy to recurrent into a normal sinus rhythm.  

scholarly journals Detection of Brief Episodes of Atrial Fibrillation Based on Electrocardiomatrix and Convolutional Neural Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Ricardo Salinas-Martínez ◽  
Johannes de Bie ◽  
Nicoletta Marzocchi ◽  
Frida Sandberg
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Thrombus Formation ◽  
Normal Sinus Rhythm ◽  
Morphological Characteristics ◽  
P Wave ◽  
Learning Approaches ◽  
Normal Sinus

Background: Brief episodes of atrial fibrillation (AF) may evolve into longer AF episodes increasing the chances of thrombus formation, stroke, and death. Classical methods for AF detection investigate rhythm irregularity or P-wave absence in the ECG, while deep learning approaches profit from the availability of annotated ECG databases to learn discriminatory features linked to different diagnosis. However, some deep learning approaches do not provide analysis of the features used for classification. This paper introduces a convolutional neural network (CNN) approach for automatic detection of brief AF episodes based on electrocardiomatrix-images (ECM-images) aiming to link deep learning to features with clinical meaning.Materials and Methods: The CNN is trained using two databases: the Long-Term Atrial Fibrillation and the MIT-BIH Normal Sinus Rhythm, and tested on three databases: the MIT-BIH Atrial Fibrillation, the MIT-BIH Arrhythmia, and the Monzino-AF. Detection of AF is done using a sliding window of 10 beats plus 3 s. Performance is quantified using both standard classification metrics and the EC57 standard for arrhythmia detection. Layer-wise relevance propagation analysis was applied to link the decisions made by the CNN to clinical characteristics in the ECG.Results: For all three testing databases, episode sensitivity was greater than 80.22, 89.66, and 97.45% for AF episodes shorter than 15, 30 s, and for all episodes, respectively.Conclusions: Rhythm and morphological characteristics of the electrocardiogram can be learned by a CNN from ECM-images for the detection of brief episodes of AF.

219A machine learning classification algorithm to detect patients with paroxysmal atrial fibrillation during sinus rhythm

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
D Tachmatzidis ◽  
D Filos ◽  
I Chouvarda ◽  
D Mouselimis ◽  
A Tsarouchas ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
P Wave ◽  
Support Vector ◽  
Svm Classifier ◽  
P Waves ◽  
Machine Learning Classification ◽  
Increased Risk ◽  
Wave Morphology ◽  
The Mean

Abstract Background Atrial fibrillation (AF) - the most common sustained cardiac arrhythmia - while not a life-threatening condition itself, leads to an increased risk of stroke and high rates of mortality. Early detection and diagnosis of AF is a critical issue for all health stakeholders. Purpose The aim of this study is to identify P-wave morphology patterns encountered in patients with Paroxysmal AF (PAF) and to develop a classifier discriminating PAF patients from healthy volunteers. Methods Three-dimensional 1000Hz ECG signals of 5 minutes duration were obtained through the use of a Galix GBI-3S Holter monitor from a total of 68 PAF patients and 52 healthy individuals. Signal pre-processing, consisting of denoising, QRS auto-detection, and ectopic beats removal was performed and a signal window of 250ms prior to the Q-wave (Pseg) was considered for every single beat. P‑wave morphology analysis based on the dynamic application of the k‑means clustering process was performed. For those Pseg that were assigned in the largest cluster, the mean P-wave was computed. The correlation of every P-wave with the mean P-wave of the main cluster was calculated. In case that it exceeded a prespecified threshold, the P-wave was allocated to the main morphology. For the remaining P‑waves, the same approach was followed once again, and the secondary morphology was extracted (picture). The P-waves of the dominant morphology were further analyzed using wavelet transform, whereas time-domain characteristics were also extracted. A Support Vector Machine (SVM) model was created using the Gaussian Radial Basis Function kernel and the forward feature selection wrapper approach was followed. ECGs were allocated to the training, internal validation, and testing datasets in a 3:1:1 ratio. Results The percentage of P-waves following the main morphology in all three leads was lower in PAF patients (91.2 ±7.3%) than in healthy subjects (96.1 ±3.5%, p = 0.02). Classification between the two groups highlighted 7 features, while the SVM classifier resulted in a balanced accuracy of 91.4 ± 0.2% (sensitivity 94.2 ± 0.3%, specificity 88.6 ± 0.1%) Conclusion An Artificial Intelligence based ECG Classifier can efficiently identify PAF patients during normal sinus rhythm. Abstract Figure.

scholarly journals IDENTIFIKASI FIBRILASI ATRIUM PADA ISYARAT ELEKTROKARDIOGRAM (EKG) MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM)

2018 ◽  
Vol 9 (1) ◽  
pp. 231-240
Author(s):  
Mohammad Rofii
Keyword(s):  
Atrial Fibrillation ◽  
Support Vector Machine ◽  
Standard Deviation ◽  
Sinus Rhythm ◽  
Naive Bayes ◽  
Normal Sinus Rhythm ◽  
Naïve Bayes ◽  
Support Vector ◽  
Bayes Classifier ◽  
Normal Sinus

Jantung merupakan salah satu organ penting yang terdapat pada tubuh manusia. Fungsi vital yang diperankan oleh organ jantung berpengaruh besar terhadap kondisi seseorang yang dapat dilihat dari isyarat fisiologi yang dihasilkan oleh aktivitas kelistrikan jantung yang dapat diukur dan direkam berupa electrocardiogram (EKG). Tujuan dari penelitian ini adalah untuk mengidentifikasi kelainan jantung atau aritmia berupa atrial fibrillation (AF) pada isyarat EKG. Data penelitian yang digunakan berasal dari Rumah Sakit  Umum Daerah Tugurejo Semarang yang  terdiri dari data  pasien dengan kasus  atrial fibrillation (AF) dan data ECG normal atau normal sinus rhythm (NSR). Data yang diambil dalam bentuk data cetak, selanjutnya di lakukan scanning   untuk mendapatkan data citra digital agar dapat diproses dengan komputer. Pada penelitian ini terdapat beberapa tahapan, diantaranya adalah pra-pengolahan, ekstraksi ciri, dan klasifikasi. Proses ekstraksi ciri berdasarkan ciri statistik (mean, standard deviation, kurtosis, variance, skewness) isyarat periodogram dari EKG, selanjutnya diklasifikasi menggunakan algoritma Support Vector Machine (SVM) dan Naive bayes Classifier (NBC) sebagai algoritma pembanding. Hasil yang didapatkan pada penelitian ini, SVM memiliki kinerja yang lebih baik dengan nilai akurasi sebesar sebesar 84,0%, sensitivitas 80,5%, dan spesifisitas 92,8%.

The Sensitivity of The Heart Rate Detection for Atrial Electrograms Signal

2021 ◽  
Vol 20 (2) ◽  
pp. 33-41
Author(s):  
Pang Seng Kong ◽  
Nasarudin Ahmad ◽  
Fazilah Hassan ◽  
Anita Ahmad
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Screening Tool ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Large Area ◽  
Rr Intervals ◽  
Rr Interval

Atrial Fibrillation (AF) is the most familiar example of arrhythmia that will occur health problems such as stroke, heart failure and other complications. Globally, the number of AF patients will more than triple by 2050 worldwide. Current methods involve performing large-area ablation without knowing the exact location of key parts. The reliability of the technology can be used as a target for atrial fibrillation’s catheter ablation. The factors that leading to the onset of atrial fibrillation include the triggering factors that induce arrhythmia and the substrate that maintains the arrhythmia. The project’s aim is to create a method for identifying AF that can be used as screening tool in medical practice. The primary goals for the detection method’s design are to develop a MATLAB software program that can compare the complexity of a normal ECG signal and an AF ECG signal. Currently, this can be achieved by the ECG Signal’s R peaks and RR Interval. For AF detection, there are more R peaks and RR Intervals and it is irregular. In this research, the detection of AF is based on the heart rate (RR Intervals). For the ECG preprocessing, Pan-Tompkins Algorithm and Discrete Wavelet Transform is used to detect the sensitivity on the R peaks and RR Intervals. As a result, Discrete Wavelet Transform algorithm gives 100% sensitivity for the dataset obtained from MIT-BIH Atrial Fibrillation and MIT-BIH Arrhythmia Database.  

Abstract 17189: Explaining the Deep Learning Black Box by Identifying Segments of the Single-Lead ECG Signal Used for Detection of Atrial Fibrillation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Praharsh Ivaturi ◽  
Matteo Gadaleta ◽  
Amitabh C Pandey ◽  
Michael Pazzani ◽  
Steven R Steinhubl ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Making ◽  
Deep Learning ◽  
Network Architecture ◽  
Normal Sinus Rhythm ◽  
Black Box ◽  
P Wave ◽  
Ecg Signal ◽  
Rr Intervals ◽  
Post Hoc

Introduction: Deep learning (DL) has proved effective for automatic identification of atrial fibrillation (AF) using single-lead ECG. Adoption and trust of DL by clinicians is limited by its black box nature. Hypothesis: Post hoc explanations can elucidate what part of ECG signal is used by the black box DL algorithm, quantifying the importance of clinically relevant features in the classification decision. Making DL decision process transparent will help its integration into clinical practice. Methods: 8,528 single-lead ECG recordings collected using AliveCor devices (PhysioNet) were used. Each signal was labeled as normal sinus rhythm, AF, other arrhythmia or noise. DL automatic classification involves a lightweight convolutional neural network architecture - MobileNet - whose performance is analyzed with an explanation method for DL. Results: Each RR interval is divided into 8 equal segments, where segment 1 follows each R peak, 4 and 5 correspond to the isoelectric baseline, and 7 to the P wave. The explanation method substitutes one of these segments with a straight line, and the corresponding change in sensitivity highlights its importance for the DL algorithm decision. MobileNet achieved a sensitivity of 92.5% to identify AF (9.4% of ECGs were in AF). Sensitivity increases by 2.5% when Segment 7 is removed, indicating that the absence of P wave leads the network to classify more frequently samples as AF.(Figure) When Segments 4 and 5 are removed, the sensitivity decreases by 2.5% and 5.0%, and by 26.7% when removed together. When all RR intervals are normalized to the same value (RR in the Figure), sensitivity for AF drops by 78.3%, showing that RR intervals are key for AF detection by DL algorithm. Conclusions: Post hoc explanations for AF detection by DL from single-lead ECG show the importance of common morphological features used for classifying AF. These methods can be used to understand the decision-making process of DL and motivate its clinical adoption.

scholarly journals Abnormal ECG Classification using Empirical Mode Decomposition and Entropy

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
Suci Aulia ◽  
Sugondo Hadiyoso
Keyword(s):  
Atrial Fibrillation ◽  
Feature Extraction ◽  
Left Bundle Branch Block ◽  
Normal Sinus Rhythm ◽  
Heart Defects ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Specificity And Sensitivity

Heart disease is one of the leading causes of death in the world. Early detection followed by therapy is one of the efforts to reduce the mortality rate of this disease. One of the leading medical instruments for diagnosing heart disorders is the electrocardiogram (ECG). The shape of the ECG signal represents normal or abnormal heart conditions. Some of the most common heart defects are atrial fibrillation and left bundle branch block. Detection or classification can be difficult if performed visually. Therefore in this study, we propose a method for the automatic classification of ECG signals. This method generally consists of feature extraction and classification. The feature extraction used is based on information theory, namely Fuzzy entropy and Shannon entropy, which is calculated on the decomposed signal. The simulated ECG signals are of three types: normal sinus rhythm, atrial fibrillation, and left bundle branch block. Support vector machine and k-Nearest Neighbor algorithms were employed for the validation performance of the proposed method. From the test results obtained, the highest accuracy is 81.1%. With specificity and sensitivity of 79.4% and 89.8%, respectively. It is hoped that this proposed method can be further developed to assist clinical diagnosis.

scholarly journals Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3460 ◽  
Author(s):  
Shahriar Rahman Fahim ◽  
Subrata K. Sarker ◽  
S. M. Muyeen ◽  
Md. Rafiqul Islam Sheikh ◽  
Sajal K. Das
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Classification Model ◽  
Superior Performance ◽  
Fault Classification ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Fault Detection And Classification ◽  
Proposed Model ◽  
The Individual

Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.

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