Artificial Intelligence in the Diagnosis and Management of Arrhythmias

Venkat D. Nagarajan; Su-Lin Lee; Jan-Lukas Robertus; Christoph A. Nienaber; Natalia A. Trayanova; Sabine Ernst

Disclosures

Eur Heart J. 2021;42(38):3904-3916. 

In This Article

Cardiac Electrical Signal Analysis Using Artificial Intelligence Methodologies

Human intelligence is characterized by the capability of learning, reasoning, analysing, and decision-making. When machines mimic the use of these capabilities, it can be termed AI. Although the concept and the term AI have been used for over six decades,[1–3] its usage has skyrocketed over the last decade. Whilst ML is the most commonly used term for AI, it only denotes one of the methodologies of AI (Figure 2 and Supplementary material online, Section S1).

Figure 2.

Artificial intelligence methodologies with their individual characteristics. AI, artificial intelligence; ANN, artificial neural network; CovNN, convolutional neural network; DNN, deep neural network; KNN, K-nearest neighbours; LR, logistic regression; RF, random forest; RNN, recurrent neural network; SVM, support vector machine.

Most of the AI applications in EP are based on the analysis of signals that represent cardiac electrical activity, with the signal varying with the type of sensor and underlying technology. Two of the most commonly used signals are electrocardiogram (ECG) and photo plethysmography (PPG). Photo plethysmography is more contemporary and has been used in some of the wearable devices including watches, wrist bands, and smartphones. Fundamental AI processes employed in analysing data obtained from these devices are essentially similar (Figure 3).

Figure 3.

Schematic representation of steps involved in cardiac impulse analysis from the data acquisition to analysis by machine learning algorithm. ECG, electrocardiogram; ML, machine learning; PPG, photo plethysmography.

Following data collection, data are pre-processed, feature engineering (Table 1) carried out, and followed by classification by one of the ML methodologies (Table 2). The ML methodology is first trained using the 'training data' with appropriate labels. The second stage involves ML methodology assessment using a 'validation data set' and fine tuning the algorithm. Following these two steps, the ML algorithm would then be ready to be used with a third 'test set'. Performance of the algorithm is expressed using values such as sensitivity, specificity, accuracy, receiver operating curve (ROC), and area under ROC (AUC).[4]

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