Table 2 Open in new tab Commonly used...

Algorithm

Learning method

Description

Utility in EP ML

Support vector machine

Most commonly used supervised learning method

Used to classify complex non-linear data
Creates ‘hyperplane’ that non-linearly separates the two classes in a feature space
Good classification and generalization properties

Arrhythmia classification using heart rate variability
VF detection algorithm in automated external defibrillators

Random Forest

Supervised learning method

Ensemble learning methods that combine multiple decision trees (algorithms)
Decision trees arranged in a hierarchical manner
Final prediction derived by calculating the mean or mode of the individual DT’s decision

Classification of ECG beats
CRT outcomes prediction

Bayesian networks

Supervised learning method

Graphical structures to represent knowledge about an uncertain domain
Represent variables and their probabilistic relationships
HMM—one of the frequently used examples of BNs

Classification of ECG beats
CRT outcomes prediction

Neural networks

Can be supervised or unsupervised learning method

Computational model mimicking biological neural networks
Data is propagated in a hierarchical manner via nodes in each layer
Input/target pairs are used during model training

Classifying large amounts of data
Classification of ECG beats

Convolutional neural networks

Can be supervised or unsupervised learning method

Evolved form of deep neural networks (multiple hidden layers between input and output)
Convolution layers produce a spatially dependent feature for the subsequent layer
Most widely used DL

For deciphering diseased state footprints in 12-lead ECG
Cardiac imaging

Table 2

Open in new tab

Commonly used machine learning classification algorithms

Algorithm	Learning method	Description	Utility in EP ML
Support vector machine	Most commonly used supervised learning method	Used to classify complex non-linear data Creates ‘hyperplane’ that non-linearly separates the two classes in a feature space Good classification and generalization properties	Arrhythmia classification using heart rate variability VF detection algorithm in automated external defibrillators
Random Forest	Supervised learning method	Ensemble learning methods that combine multiple decision trees (algorithms) Decision trees arranged in a hierarchical manner Final prediction derived by calculating the mean or mode of the individual DT’s decision	Classification of ECG beats CRT outcomes prediction
Bayesian networks	Supervised learning method	Graphical structures to represent knowledge about an uncertain domain Represent variables and their probabilistic relationships HMM—one of the frequently used examples of BNs	Classification of ECG beats CRT outcomes prediction
Neural networks	Can be supervised or unsupervised learning method	Computational model mimicking biological neural networks Data is propagated in a hierarchical manner via nodes in each layer Input/target pairs are used during model training	Classifying large amounts of data Classification of ECG beats
Convolutional neural networks	Can be supervised or unsupervised learning method	Evolved form of deep neural networks (multiple hidden layers between input and output) Convolution layers produce a spatially dependent feature for the subsequent layer Most widely used DL	For deciphering diseased state footprints in 12-lead ECG Cardiac imaging

Algorithm	Learning method	Description	Utility in EP ML
Support vector machine	Most commonly used supervised learning method	Used to classify complex non-linear data Creates ‘hyperplane’ that non-linearly separates the two classes in a feature space Good classification and generalization properties	Arrhythmia classification using heart rate variability VF detection algorithm in automated external defibrillators
Random Forest	Supervised learning method	Ensemble learning methods that combine multiple decision trees (algorithms) Decision trees arranged in a hierarchical manner Final prediction derived by calculating the mean or mode of the individual DT’s decision	Classification of ECG beats CRT outcomes prediction
Bayesian networks	Supervised learning method	Graphical structures to represent knowledge about an uncertain domain Represent variables and their probabilistic relationships HMM—one of the frequently used examples of BNs	Classification of ECG beats CRT outcomes prediction
Neural networks	Can be supervised or unsupervised learning method	Computational model mimicking biological neural networks Data is propagated in a hierarchical manner via nodes in each layer Input/target pairs are used during model training	Classifying large amounts of data Classification of ECG beats
Convolutional neural networks	Can be supervised or unsupervised learning method	Evolved form of deep neural networks (multiple hidden layers between input and output) Convolution layers produce a spatially dependent feature for the subsequent layer Most widely used DL	For deciphering diseased state footprints in 12-lead ECG Cardiac imaging

BN, bayesian networks; CRT, cardiac resynchronization therapy; DL, deep learning; ECG, electrocardiogram; EP, electrophysiology; HMM, Hidden Markov Models; ML, machine learning; VF, ventricular fibrillation.

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