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Table 1

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Overview of feature engineering processes employed during development of machine learning methods

Feature engineering
Definition	Extraction	Optimization
Most informative and non-redundant characteristics of data signals Represented in a numerical form and together form a feature vector Data represented as features is computationally processed by ML algorithms	In supervised ML methods feature extraction is done by experts in the domain In unsupervised and DL methodologies feature engineering is done by the algorithm itself Features used for cardiac signal analysis—time intervals, morphological amplitudes, areas or distances	Selection of appropriate features is crucial for the success of ML methodology Algorithms used for relevant feature identification—particle swarm optimization, etc. Algorithms used for dimensionality reduction—principal component analysis and linear discriminant analysis

Feature engineering
Definition	Extraction	Optimization
Most informative and non-redundant characteristics of data signals Represented in a numerical form and together form a feature vector Data represented as features is computationally processed by ML algorithms	In supervised ML methods feature extraction is done by experts in the domain In unsupervised and DL methodologies feature engineering is done by the algorithm itself Features used for cardiac signal analysis—time intervals, morphological amplitudes, areas or distances	Selection of appropriate features is crucial for the success of ML methodology Algorithms used for relevant feature identification—particle swarm optimization, etc. Algorithms used for dimensionality reduction—principal component analysis and linear discriminant analysis

DL, deep learning; ML, machine learning.

Table 1

Open in new tab

Overview of feature engineering processes employed during development of machine learning methods

Feature engineering
Definition	Extraction	Optimization
Most informative and non-redundant characteristics of data signals Represented in a numerical form and together form a feature vector Data represented as features is computationally processed by ML algorithms	In supervised ML methods feature extraction is done by experts in the domain In unsupervised and DL methodologies feature engineering is done by the algorithm itself Features used for cardiac signal analysis—time intervals, morphological amplitudes, areas or distances	Selection of appropriate features is crucial for the success of ML methodology Algorithms used for relevant feature identification—particle swarm optimization, etc. Algorithms used for dimensionality reduction—principal component analysis and linear discriminant analysis

Feature engineering
Definition	Extraction	Optimization
Most informative and non-redundant characteristics of data signals Represented in a numerical form and together form a feature vector Data represented as features is computationally processed by ML algorithms	In supervised ML methods feature extraction is done by experts in the domain In unsupervised and DL methodologies feature engineering is done by the algorithm itself Features used for cardiac signal analysis—time intervals, morphological amplitudes, areas or distances	Selection of appropriate features is crucial for the success of ML methodology Algorithms used for relevant feature identification—particle swarm optimization, etc. Algorithms used for dimensionality reduction—principal component analysis and linear discriminant analysis

DL, deep learning; ML, machine learning.