Elias Ebrahimzadeh1, Maede Kalantari2, Mohammadamin Joulani3, Reza Shahrokhi Shahraki4, Farahnaz Fayaz5, Fereshteh Ahmadi5. 1. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran; Seaman Family MR Research Center, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. Electronic address: e_ebrahimzadeh@ut.ac.ir. 2. Department of Biomedical Engineering, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran. 3. Student Research Committee, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran. 4. Faculty of Electrical and Biomedical Engineering, University of Sheikhbahaee, Isfahan, Iran. 5. Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran.
Abstract
BACKGROUND AND OBJECTIVE: Paroxysmal Atrial Fibrillation (PAF) is one of the most common major cardiac arrhythmia. Unless treated timely, PAF might transform into permanent Atrial Fibrillation leading to a high rate of morbidity and mortality. Therefore, increasing attention has been directed towards prediction of PAF, to enable early detection and prevent further progression of the disease. Notwithstanding the pharmacological and electrical treatments, a validated method to predict the onset of PAF is yet to be developed. We aim to address this issue through integrating classical and modern methods. METHODS: To increase the predictivity, we have made use of a combination of features extracted through linear, time-frequency, and nonlinear analyses performed on heart rate variability. We then apply a novel approach to local feature selection using meticulous methodologies, developed in our previous works, to reduce the dimensionality of the feature space. Subsequently, the Mixture of Experts classification is employed to ensure a precise decision-making on the output of different processes. In the current study, we analyzed 106 signals from 53 pairs of ECG recordings obtained from the standard database called Atrial Fibrillation Prediction Database (AFPDB). Each pair of data contains one 30-min ECG segment that ends just before the onset of PAF event and another 30-min ECG segment at least 45 min distant from the onset. RESULTS: Combining the features that are extracted using both classical and modern analyses was found to be significantly more effective in predicting the onset of PAF, compared to using either analyses independently. Also, the Mixture of Experts classification yielded more precise class discrimination than other well-known classifiers. The performance of the proposed method was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which led to sensitivity, specificity, and accuracy of 100%, 95.55%, and 98.21% respectively. CONCLUSION: Prediction of PAF has been a matter of clinical and theoretical importance. We demonstrated that utilising an optimized combination of - as opposed to being restricted to - linear, time-frequency, and nonlinear features, along with applying the Mixture of Experts, contribute greatly to an early detection of PAF, thus, the proposed method is shown to be superior to those mentioned in similar studies in the literature.
BACKGROUND AND OBJECTIVE:Paroxysmal Atrial Fibrillation (PAF) is one of the most common major cardiac arrhythmia. Unless treated timely, PAF might transform into permanent Atrial Fibrillation leading to a high rate of morbidity and mortality. Therefore, increasing attention has been directed towards prediction of PAF, to enable early detection and prevent further progression of the disease. Notwithstanding the pharmacological and electrical treatments, a validated method to predict the onset of PAF is yet to be developed. We aim to address this issue through integrating classical and modern methods. METHODS: To increase the predictivity, we have made use of a combination of features extracted through linear, time-frequency, and nonlinear analyses performed on heart rate variability. We then apply a novel approach to local feature selection using meticulous methodologies, developed in our previous works, to reduce the dimensionality of the feature space. Subsequently, the Mixture of Experts classification is employed to ensure a precise decision-making on the output of different processes. In the current study, we analyzed 106 signals from 53 pairs of ECG recordings obtained from the standard database called Atrial Fibrillation Prediction Database (AFPDB). Each pair of data contains one 30-min ECG segment that ends just before the onset of PAF event and another 30-min ECG segment at least 45 min distant from the onset. RESULTS: Combining the features that are extracted using both classical and modern analyses was found to be significantly more effective in predicting the onset of PAF, compared to using either analyses independently. Also, the Mixture of Experts classification yielded more precise class discrimination than other well-known classifiers. The performance of the proposed method was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which led to sensitivity, specificity, and accuracy of 100%, 95.55%, and 98.21% respectively. CONCLUSION: Prediction of PAF has been a matter of clinical and theoretical importance. We demonstrated that utilising an optimized combination of - as opposed to being restricted to - linear, time-frequency, and nonlinear features, along with applying the Mixture of Experts, contribute greatly to an early detection of PAF, thus, the proposed method is shown to be superior to those mentioned in similar studies in the literature.
Authors: Mohammed Baydoun; Lise Safatly; Ossama K Abou Hassan; Hassan Ghaziri; Ali El Hajj; Hussain Isma'eel Journal: IEEE J Transl Eng Health Med Date: 2019-11-07 Impact factor: 3.316
Authors: George Koulaouzidis; Tomasz Jadczyk; Dimitris K Iakovidis; Anastasios Koulaouzidis; Marc Bisnaire; Dafni Charisopoulou Journal: J Clin Med Date: 2022-07-05 Impact factor: 4.964
Authors: Jens Cosedis Nielsen; Yenn-Jiang Lin; Marcio Jansen de Oliveira Figueiredo; Alireza Sepehri Shamloo; Alberto Alfie; Serge Boveda; Nikolaos Dagres; Dario Di Toro; Lee L Eckhardt; Kenneth Ellenbogen; Carina Hardy; Takanori Ikeda; Aparna Jaswal; Elizabeth Kaufman; Andrew Krahn; Kengo Kusano; Valentina Kutyifa; Han S Lim; Gregory Y H Lip; Santiago Nava-Townsend; Hui-Nam Pak; Gerardo Rodríguez Diez; William Sauer; Anil Saxena; Jesper Hastrup Svendsen; Diego Vanegas; Marmar Vaseghi; Arthur Wilde; T Jared Bunch; Alfred E Buxton; Gonzalo Calvimontes; Tze-Fan Chao; Lars Eckardt; Heidi Estner; Anne M Gillis; Rodrigo Isa; Josef Kautzner; Philippe Maury; Joshua D Moss; Gi-Byung Nam; Brian Olshansky; Luis Fernando Pava Molano; Mauricio Pimentel; Mukund Prabhu; Wendy S Tzou; Philipp Sommer; Janice Swampillai; Alejandro Vidal; Thomas Deneke; Gerhard Hindricks; Christophe Leclercq Journal: Europace Date: 2020-08-01 Impact factor: 5.214