Key result
An IoT monitoring system using k-nearest neighbors achieves ~94% accuracy in classifying PVCs.
Why the study?
IoT technologies can enable remote patient diagnosis, motivating the development of a system to monitor ECG signals and alert clinicians when arrhythmias occur.
Does an IoT-based system using machine learning accurately detect arrhythmias from ECG signals?
Does an IoT-based system using machine learning accurately detect arrhythmias from ECG signals?
An IoT-based ECG monitoring system using k-nearest neighbors machine learning can accurately classify various arrhythmias, potentially enabling remote patient monitoring.
May facilitate remote arrhythmia monitoring; leaves open prospective validation before clinical use.
Internet of Things (IoT) technologies allow building a digital representation of people, objects, or physical phenomena to be available on the Internet. Thus, stakeholders can access this information from remote places or computational systems could analyze this data to find patterns, make decisions, or execute actions. For instance, a doctor could diagnose patients by analyzing the received data from an IoT system even when patients are located in a remote place. This article proposes an IoT system for monitoring electrocardiogram (ECG) signal and processing heart data in order to generate an alert when an arrhythmia is present. This system involves a Polar H10 heart sensor, machine-learning models to classify heart events, and communication technology to share and store patient's information. In the first place, the architecture of the IoT monitoring system and the communication between the components are described by discussing the designing criteria. Second, the experimentation process performs the training and the assessment of three classification algorithms, random forest, convolutional neural network, and k-nearest neighbors. The results show that k-nearest neighbor has the best accuracy percentage classifying the arrhythmias under study (premature ventricular contraction 94%, fusion of ventricular beat 81%, and supraventricular premature beat 82%); also, it is able to discern normal and unclassifiable beats with 93% and 97%, respectively.
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Cañón-Clavijo et al. (2023) studied Arrhythmia. IoT system with k-nearest neighbors algorithm vs. Random forest and convolutional neural network was evaluated on Accuracy of classifying arrhythmias. An IoT monitoring system using the k-nearest neighbors algorithm achieved high accuracy in classifying arrhythmias, including premature ventricular contraction (94%) and normal beats (93%).
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