TY - JOUR A1 - Rjoob, Khaled A1 - Bond, Raymond R. A1 - Finlay, Dewar D. A1 - McGilligan, Victoria A1 - Leslie, Stephen A1 - Rababah, Ali A1 - Iftikhar, Aleeha A1 - Güldenring, Daniel A1 - Knoery, Charles A1 - McShane, Anne A1 - Peace, Aaron A1 - Macfarlane, Peter T1 - Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications T2 - Artificial Intelligence In Medicine N2 - Background The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over recent decades, there has been a steady increase in the number of research studies that are using machine learning (ML) to read or interrogate ECG data. Objective The aim of this study is to review the use of ML with ECG data using a time series approach. Methods Papers that address the subject of ML and the ECG were identified by systematically searching databases that archive papers from January 1995 to October 2019. Time series analysis was used to study the changing popularity of the different types of ML algorithms that have been used with ECG data over the past two decades. Finally, a meta-analysis of how various ML techniques performed for various diagnostic classifications was also undertaken. Results A total of 757 papers was identified. Based on results, the use of ML with ECG data started to increase sharply (p < 0.001) from 2012. Healthcare applications, especially in heart abnormality classification, were the most common application of ML when using ECG data (p < 0.001). However, many new emerging applications include using ML and the ECG for biometrics and driver drowsiness. The support vector machine was the technique of choice for a decade. However, since 2018, deep learning has been trending upwards and is likely to be the leading technique in the coming few years. Despite the accuracy paradox, accuracy was the most frequently used metric in the studies reviewed, followed by sensitivity, specificity, F1 score and then AUC. Conclusion Applying ML using ECG data has shown promise. Data scientists and physicians should collaborate to ensure that clinical knowledge is being applied appropriately and is informing the design of ML algorithms. Data scientists also need to consider knowledge guided feature engineering and the explicability of the ML algorithm as well as being transparent in the algorithm's performance to appropriately calibrate human-AI trust. Future work is required to enhance ML performance in ECG classification. KW - Elektrokardiogramm KW - Biomedizin KW - Maschinelles Lernen KW - Biomedical Signal Processing Y1 - 2022 UR - https://opus4.kobv.de/opus4-hs-kempten/frontdoor/index/index/docId/1565 SN - 0933-3657 VL - 132 IS - October 2022 SP - 1 EP - 9 PB - Elsevier CY - Amsterdam ER -