Key result
A proposed system combining a machine learning algorithm and wavelet transformation efficiently classified heartbeats in publicly available arrhythmia data with low computational requirements.
Why the study?
Accurate detection of cardiac pathological events is vital for ECG evaluation, motivating systems that maximize heartbeat classification performance with minimal training and low computational demands.
A novel machine learning and wavelet transformation-based system provides efficient ECG heartbeat classification with low computational requirements suitable for IoT implementation.
May enable low-power arrhythmia screening on wearables; leaves open prospective clinical validation before adoption.
Accurate detection of cardiac pathological events is an important part of electrocardiogram (ECG) evaluation and subsequent correct treatment of the patient. For this purpose, several adaptive filter structures were proposed during the past decades for noise cancellation and arrhythmia detection. Currently there are a lot of devices on the market that analyze ECGs, such as patient monitors, stress test systems, and Holter analysis systems, that are able to detect beats and classify arrhythmia. This paper proposes a system for ECG analysis and heartbeat classification. The proposed solution relies on a combination of machine learning algorithm and a wavelet transformation in order to maximize its performance with the minimum possible training phase. Experimental results with public available data for arrhythmia indicate the efficiency in classifying heartbeats, whereas its low-computational and memory requirements makes it suitable for being implemented as part of an embedded (IoT) system.
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Balaskas et al. (2019) studied Arrhythmia. Machine learning algorithm and wavelet transformation was evaluated on Heartbeat classification efficiency. A proposed system combining a machine learning algorithm and wavelet transformation efficiently classified heartbeats in publicly available arrhythmia data with low computational requirements.
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