PhysioZoo ECG: Digital electrocardiography biomarkers to assess cardiac conduction

Gendelman, Sheina; Biton, Shany; Derman, Raphael; Zvuloni, Eran; Levy, Jeremy; Lugassy, Snir; Alexandrovich, Alexandra; Behar, Joachim A.

doi:10.23919/cinc53138.2021.9662857

Cited by 8 publications

(10 citation statements)

References 9 publications

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“…bSQI was computed using two peak detectors, epltd from the WFDB library and jQRS as a reference. Computation was done with the custom PEBM toolbox [4]. Each ECG lead was computed separately and the mean bSQI over the 12 leads was then stored.…”

Section: Methodsmentioning

confidence: 99%

“…For the HRV, features 1-20 from Chocron et-al [3] with an additional three features denoted "extended parabolic phase space mapping" [10] features were computed. MOR features were computed using the PEBM toolbox [4] for each ECG cycle and the median and standard deviation statistic were computed for each segment. Overall, we obtained 23 HRV and 2×22 MOR features for each lead, thus totaling 804 for the 12 leads.…”

Section: Methodsmentioning

confidence: 99%

“…Engineered features extracted from ECG have been investigated as input for machine learning (ML) algorithms [3], [4], supporting complex analysis tasks. These have been providing necessary insights on patients' conditions for risk prediction or diagnosis [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Atrial Fibrillation Recurrence Risk Prediction from 12-lead ECG Recorded Pre- and Post-Ablation Procedure

Zvuloni¹,

Gendelman²,

Mohanty³

et al. 2022

Computing in Cardiology Conference (CinC)

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Introduction: 12-lead electrocardiogram (ECG) is recorded during atrial fibrillation (AF) catheter ablation procedure (CAP). It is not easy to determine if CAP was successful without a long follow-up assessing for AF recurrence (AFR). Therefore, an AFR risk prediction algorithm could enable a better management of CAP patients. In this research, we extracted features from 12-lead ECG recorded before and after CAP and train an AFR risk prediction machine learning model. Methods: Pre-and post-CAP segments were extracted from 112 patients. The analysis included a signal quality criterion, heart rate variability and morphological biomarkers engineered from the 12lead ECG (804 features overall). 43 out of the 112 patients (n) had AFR clinical endpoint available. These were utilized to assess the feasibility of AFR risk prediction, using either pre or post CAP features. A random forest classifier was trained within a nested cross validation framework. Results: 36 features were found statistically significant for distinguishing between the pre and post surgery states (n=112). For the classification, an area under the receiver operating characteristic (AUROC) curve was reported with AU ROC pre = 0.64 and AU ROC post = 0.74 (n=43). Discussion and conclusions: This preliminary analysis showed the feasibility of AFR risk prediction. Such a model could be used to improve CAP management.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Atrial Fibrillation Recurrence Risk Prediction from 12-lead ECG Recorded Pre- and Post-Ablation Procedure

Zvuloni¹,

Gendelman²,

Mohanty³

et al. 2022

Computing in Cardiology Conference (CinC)

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“…Moreover, a mean signal quality index called bSQI [14] was computed as an additional HRV feature. The third set consisted in 22 morphological (MOR) features [15]. HRV and MOR features were computed for each lead, ending up with an overall of 557 engineered features per 12-lead ECG recording.…”

Section: B Feature Engineeringmentioning

confidence: 99%

On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG

Zvuloni¹,

Read²,

Ribeiro³

et al. 2022

Preprint

Self Cite

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Objective: Machine learning techniques have been used extensively for 12-lead electrocardiogram (ECG) analysis. For physiological time series, deep learning (DL) superiority to feature engineering (FE) approaches based on domain knowledge is still an open question. Moreover, it remains unclear whether combining DL with FE may improve performance. Methods: We considered three tasks intending to address these research gaps: cardiac arrhythmia diagnosis (multiclass-multilabel classification), atrial fibrillation risk prediction (binary classification), and age estimation (regression). We used an overall dataset of 2.3M 12-lead ECG recordings to train the following models for each task: i) a random forest taking the FE as input was trained as a classical machine learning approach; ii) an end-to-end DL model; and iii) a merged model of FE+DL. Results: FE yielded comparable results to DL while necessitating significantly less data for the two classification tasks and it was outperformed by DL for the regression task. For all tasks, merging FE with DL did not improve performance over DL alone. Conclusion: We found that for traditional 12-lead ECG based diagnosis tasks DL did not yield a meaningful improvement over FE, while it improved significantly the nontraditional regression task. We also found that combining FE with DL did not improve over DL alone which suggests that the FE were redundant with the features learned by DL. Significance: Our findings provides important recommendations on what machine learning strategy and data regime to chose with respect to the task at hand for the development of new machine learning models based on the 12-lead ECG.

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“…Similar to the work in [8], we computed the signal quality index bSQI [9][10][11] and discarded segments when below 0.8. ECG recordings were prefiltered using a zerophase second-order infinite impulse response bandpass filter, with a passband of 0.67-100 Hz to remove baseline wander and high-frequency noise [12].…”

Section: Preprocessingmentioning

confidence: 99%

Estimation of f-wave Dominant Frequency Using a Voting Scheme

Biton¹,

Suleiman²,

Moshe³

et al. 2022

Computing in Cardiology Conference (CinC)

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Introduction: Atrial fibrillation (AF) is the most common heart arrhythmia, characterized by the presence of fibrillatory waves (f-waves) in the ECG. We introduce a voting scheme to estimate the dominant atrial frequency (DAF) of f-waves. Methods: We analysed a subset of Holter recordings obtained from the University of Virginia AF Database. 100 Holter recordings with manually annotated AF events, resulting in a total 363 AF events lasting more than 1 min. The f-waves were extracted using four different template subtraction (TS) algorithms and the DAF was estimated from the first 1-min window of each AF event. A random forest classifier was used. We hypothesized that better extraction of the f-wave meant better AF/non-AF classification using the DAF as the single input feature of the RF model. Results: Performance on the test set, expressed in terms of AF/non-AF classification, was the best when the DAF was computed computed the three best-performing extraction methods. Using these three algorithms in a voting scheme, the classifier obtained AUC=0.60 and the DAFs were mostly spread around 6 Hz,. Conclusions: This study has two novel contributions: (1) a method for assessing the performance of f-wave extraction algorithms, and (2) a voting scheme for improved DAF estimation.

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PhysioZoo ECG: Digital electrocardiography biomarkers to assess cardiac conduction

Cited by 8 publications

References 9 publications

Atrial Fibrillation Recurrence Risk Prediction from 12-lead ECG Recorded Pre- and Post-Ablation Procedure

Atrial Fibrillation Recurrence Risk Prediction from 12-lead ECG Recorded Pre- and Post-Ablation Procedure

On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG

Estimation of f-wave Dominant Frequency Using a Voting Scheme

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