TY - JOUR
T1 - Effect of temporal resolution on the detection of cardiac arrhythmias using HRV features and machine learning
AU - Ben Itzhak, Sagi
AU - Ricon, Shir Sharony
AU - Biton, Shany
AU - Behar, Joachim A.
AU - Sobel, Jonathan A.
N1 - Publisher Copyright:
© 2022 Institute of Physics and Engineering in Medicine.
PY - 2022/4/29
Y1 - 2022/4/29
N2 - Objective. Arrhythmia is an abnormal cardiac rhythm that affects the pattern and rate of the heartbeat. Wearable devices with the functionality to measure and store heart rate (HR) data are growing in popularity and enable diagnosing and monitoring arrhythmia on a large scale. The typical sampling resolution of HR data available from non-medical grade wearable devices varies from seconds to several minutes depending on the device and its settings. However, the impact of sampling resolution on the performance and quality of arrhythmia detection has not yet been quantified. Approach. In this study, we investigated the detection and classification of three arrhythmias, namely atrial fibrillation, bradycardia, tachycardia, from down-sampled HR data with various temporal resolution (5-, 15-, 30- and 60 s averages) in 1 h segments extracted from an annotated Holter ECG database acquired at the University of Virginia Heart Station. For the classification task, a total of 15 common heart rate variability (HRV) features were engineered based on the HR time series of each patient. Three different types of machine learning classifiers were evaluated, namely logistic regression, support vector machine and random forest. Main results. A decrease in temporal resolution drastically impacted the detection of atrial fibrillation but did not substantially affect the detection of bradycardia and tachycardia. A HR resolution up to 15 s average demonstrated reasonable performance with a sensitivity of 0.92 and a specificity of 0.86 for a multiclass random forest classifier. Significance. HRV features extracted from low resolution long HR recordings have the potential to increase the early detection of arrhythmias in undiagnosed individuals.
AB - Objective. Arrhythmia is an abnormal cardiac rhythm that affects the pattern and rate of the heartbeat. Wearable devices with the functionality to measure and store heart rate (HR) data are growing in popularity and enable diagnosing and monitoring arrhythmia on a large scale. The typical sampling resolution of HR data available from non-medical grade wearable devices varies from seconds to several minutes depending on the device and its settings. However, the impact of sampling resolution on the performance and quality of arrhythmia detection has not yet been quantified. Approach. In this study, we investigated the detection and classification of three arrhythmias, namely atrial fibrillation, bradycardia, tachycardia, from down-sampled HR data with various temporal resolution (5-, 15-, 30- and 60 s averages) in 1 h segments extracted from an annotated Holter ECG database acquired at the University of Virginia Heart Station. For the classification task, a total of 15 common heart rate variability (HRV) features were engineered based on the HR time series of each patient. Three different types of machine learning classifiers were evaluated, namely logistic regression, support vector machine and random forest. Main results. A decrease in temporal resolution drastically impacted the detection of atrial fibrillation but did not substantially affect the detection of bradycardia and tachycardia. A HR resolution up to 15 s average demonstrated reasonable performance with a sensitivity of 0.92 and a specificity of 0.86 for a multiclass random forest classifier. Significance. HRV features extracted from low resolution long HR recordings have the potential to increase the early detection of arrhythmias in undiagnosed individuals.
KW - atrial fibrillation
KW - heart rate variability features
KW - machine learning
KW - wearable devices
KW - Heart Rate
KW - Bradycardia
KW - Algorithms
KW - Electrocardiography/methods
KW - Humans
KW - Atrial Fibrillation/diagnosis
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85129997302&partnerID=8YFLogxK
U2 - 10.1088/1361-6579/ac6561
DO - 10.1088/1361-6579/ac6561
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C2 - 35506573
AN - SCOPUS:85129997302
SN - 0967-3334
VL - 43
JO - Physiological Measurement
JF - Physiological Measurement
IS - 4
M1 - 045002
ER -