AnomalyDetection in Electrocardiographic Signals Based on Machine Learning Models
Abstract
The ECG is a crucial tool for the prevention
and diagnosis of cardiovascular diseases. However,
manual analysis of large volumes of data is prone to
errors and generates false alarms. In this work, we
propose the design of a model for detecting anomalies
in ECG signals, based on machine learning models
(CAE, CAE + RF, CAE + SVM). Three approaches were
evaluated using performance metrics such as accuracy,
F1-score, recall, precision, MCC, ROC, and AUC.
According to the results obtained, the model that shows
the highest performance and robustness was CAE +
RF. Additionally, this model underwent a validation stage
with two test sets (A and B).
and diagnosis of cardiovascular diseases. However,
manual analysis of large volumes of data is prone to
errors and generates false alarms. In this work, we
propose the design of a model for detecting anomalies
in ECG signals, based on machine learning models
(CAE, CAE + RF, CAE + SVM). Three approaches were
evaluated using performance metrics such as accuracy,
F1-score, recall, precision, MCC, ROC, and AUC.
According to the results obtained, the model that shows
the highest performance and robustness was CAE +
RF. Additionally, this model underwent a validation stage
with two test sets (A and B).
Keywords
Electrocardiogram (ECG), anomaly detec- tion, machine learning