Key result
Various machine learning techniques are examined for their performance in autonomously detecting and distinguishing cardiac arrhythmias using ECG data.
Why the study?
ECG can vary for the same individual depending on lifestyle, making arrhythmia detection hard even for proficient cardiologists and creating high demand for autonomous identification systems.
Do machine learning techniques accurately detect and classify cardiac arrhythmias from ECG data?
Do machine learning techniques accurately detect and classify cardiac arrhythmias from ECG data?
This survey reviews machine learning techniques for the autonomous identification and classification of cardiac arrhythmias using ECG data.
ML approaches show promise for ECG arrhythmia detection; leaves open the need for prospective validation before clinical adoption.
Cardiac rhythm can be defined as the irregular electrical activity of the heart. It can be monitored using electrocardiogram (ECG). As ECG doesn't require any kind of surgery or need to put an instrument inside the body for reading the electrical activity of the heart, it is extensively used for identifying rhythms. The pulsation of the heart can be monitored through ECG which consists of P Wave, PQ Interval, QRS Complex, ST Segment, T Wave, and sometimes U Wave. The ECG can vary for the same individual depending on their lifestyle. Hence detecting arrhythmias is hard even for the most proficient cardiologist. Thus, system for autonomously identifying and classifying arrhythmia is in high demand. Feature extraction and segmentation are important parameters for autonomously identifying the majority of cardiovascular diseases. The primary goal of this paper is to examine the various ml techniques for detecting and distinguishing arrhythmias.
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Ramkumar et al. (2021) conducted a review in Cardiac Arrhythmia. Machine learning techniques was evaluated. Various machine learning techniques are examined for their performance in autonomously detecting and distinguishing cardiac arrhythmias using ECG data.
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