Classification of ECG Heartbeat Arrhythmia: A Review

Annam, Jagadeeswara Rao; Srinivas, Kalyanapu; Ch., Sureshbabu; Somala, Jayaprada; Bapi, Raju S.

doi:10.1016/j.procs.2020.04.074

Cited by 25 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [4], authors presented a review of literature on heartbeat abnormality detection. In particular, Annam et al propose a taxonomy of solutions of the arrhythmia distinguished in two main approaches: feature-based classification and timeseries-based classification.…”

Section: Related Workmentioning

confidence: 99%

Visual ECG Analysis in Real-world Scenarios

Breve¹,

Caruccio²,

Cirillo³

et al. 2021

International Conferences on Distributed Multimedia Systems

View full text Add to dashboard Cite

Cardiac arrhythmia is an alteration of the heart rhythm, for which the heartbeat is irregular. Based on the severity of this condition, an arrhythmia could represent a serious danger for a patient. An ECG is a graphic representation of an heart rhythm, which provides an overview of heart's conditions over a specific time interval. ECG signal analysis is entrusted to trained clinicians, although complex and frantic environments, such as emergency settings, can make hard to delegate continuous monitoring to the medical personnel. In such scenarios, an automatic detection methodology could provide crucial support in promptly alerting clinicians towards a potential degeneration of a patient's conditions. To this end, we propose a heartbeat classification module capable of capturing the semantics of visual information of ECG signals provided by video frames. The module relies on feature extraction techniques derived from video projected images resulting in ECG data, which are then classified by means of deep-learning models. It can be used to support the early detection of some arrhythmia in critical contexts, such as emergency rooms. We show how the proposed module can be used to support clinicians in this context, and discuss an experimental evaluation performed over ground-truth datasets.

show abstract

Section: Related Workmentioning

confidence: 99%

Visual ECG Analysis in Real-world Scenarios

Breve¹,

Caruccio²,

Cirillo³

et al. 2021

International Conferences on Distributed Multimedia Systems

View full text Add to dashboard Cite

show abstract

“…However, as these statistical methods move towards greater accuracy, the computational cost for the system also increases. The most popular techniques are deep neural networks also known as ANN consists of multiple hidden layers between the input and output layers [224]. Each layer consists of neurons with different weights and biases.…”

Section: B Classifiersmentioning

confidence: 99%

“…Recent developments in deep neural networks are widespread, with the latest techniques discussed in [224] are Recurrent Neural Networks (RNN), Convolution Neural Networks (CNN) and other generative models such as Autoencoders and Generative Adversarial Network (GAN). In the following subsection, we have selected the literature that was mainly focusing on classification schemes based on machine learning and neural network approaches for ECG signal processing.…”

Section: B Classifiersmentioning

confidence: 99%

Origins of ECG and Evolution of Automated DSP Techniques: A Review

Arora¹,

Mishra

2021

IEEE Access

View full text Add to dashboard Cite

Over the years researchers have studied the evolution of Electrocardiogram (ECG) and the complex classification of cardiovascular diseases. This review focuses on the evolution of the ECG and covers the most recent signal processing schemes with milestones over the last 150 years in a systematic manner. Development phases of ECG, ECG leads, portable ECG monitors, Signal Processing schemes and Complex Transformations are discussed. This paper summarizes the development of ECG features detection for cardiac anomalies and the history of the development of ECG monitors, beginning from String Galvanometer. It also discusses the automated detections on ECG beginning from 1960 to the most recent signal processing techniques. Additionally, this paper provides recommendations.

show abstract

“…However, as these statistical methods move towards greater accuracy, it also has a higher system complexity. Most recent techniques are deep neural networks also known as an artificial neural network (ANN) consists of multiple hidden layers between the input and output layers [190]. Each layer consists of neurons with different weights and biases.…”

Section: Classifiersmentioning

confidence: 99%