Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review
Graphical abstract
Introduction
Arrhythmia, or heart rhythm disorder, is a common manifestation of heart disease [3], [74]. An arrhythmia is said to occur if the heart beats too early, late, slowly, quickly, or at irregular intervals. This happens because there is a dysfunction in the electrical conduction pattern of the heart. Among the many different forms of arrhythmias, atrial fibrillation (AF) is the most common. AF induces varying and often very fast or slow heartbeats. In AF, the electrical impulses in the atria (upper chambers of the heart) are disorganized and are unsynchronized or out of coordination with the ventricles (lower chambers of the heart). Examples of signals from a normal and a heart with AF are seen in Figure 1. It can be observed that the electrical impulses in the normal heart proceed in an orderly flow, but the electrical impulses in the AF heart are chaotic. In AF, due to the irregular heart rhythm, the heart may pump over 150 times per minute. In a healthy heart, the heart pumps approximately 60 to 100 times per minute. During AF, the multiple ectopic foci in the atria discharge at an extremely fast rate resulting in multiple irregular stimulations. As the atria cannot respond mechanically to the disorganized electrical signal stimulation, the atria end up twitching rather than contracting properly.
AF is categorized into paroxysmal, persistent, or permanent forms (longstanding persistent AF) [76]. In paroxysmal AF, the ECG signals exhibit irregular heartbeats (AF) that return or spontaneously cardiovert to regular heartbeats (sinus rhythm) within a short time (minutes to a few days). In persistent AF, the AF returns to sinus rhythm only if the patient receives electrical or pharmacological cardioversion treatment to abolish the AF. Lastly, in permanent AF, there is no intention to cardiovert the AF, and the irregular heartbeat is left untreated.
As reported by the World Heart Federation, AF has become a global health interest as the prevalence and associated mortality have grown exponentially in the past decade [77]. According to a study published in Circulation [23], it was reported that 33.5 million people globally have AF. It was also mentioned in the study that in 2005, 3 million Americans are afflicted by AF and the number is expected to increase to 8 million by 2050. Even though AF per se is not a life-threatening condition, it is a critical medical condition that calls for medical attention. This is because the progression of AF can lead to stroke, with its potential for long-term paralysis and disability, congestive heart failure and even death [24]. Typically, the irregular heartbeats in AF do not manifest any symptom until a later stage, when cardioversion success becomes more limited. Consequently, early diagnosis of AF is crucial to halt the further advancement of AF to other heart and stroke complications.
The diagnostic approaches for AF include clinical examination, electrocardiogram (ECG), Holter monitor, an event monitor, stress test, and echocardiogram [95]. Clinical examination to detect AF comprises auscultation, and manual pulse assessment [95]. However, clinical methods are neither adequately sensitive nor specific to secure an accurate diagnosis. The ECG machine is commonly used to confirm the diagnosis. The ECG is a tool that records the electrical activity of the heart. Healthcare professionals often assess the cardiac condition of the patient based on the ECG signals. Similarly, the Holter monitor, event monitor, and stress test produce ECG signals that are recorded over an extended period of time, or when the subject is under physical stress (for example, when running). On the other hand, the echocardiogram utilizes sound waves to construct images of the heart [52]. It can provide an overall visualization of the heart structure. The images are displayed in real-time on a video monitor and these images are recorded for analysis. Still, the gold standard to test for AF is ECG analysis [62].
Section snippets
The morphology of an AF ECG signal
The ECG waveform contains essential information regarding the health conditions of the heart [10]. Figure 2 compares a typical healthy versus an AF ECG signal. A normal ECG waveform is made up of the P wave, QRS complex, and T wave. However, in an AF ECG signal, the P wave is not present but instead, replaced by many and inconsistent fibrillatory waves. Nonetheless, the QRS complex is still present in the waveform [15]. Hence, cardiologists and other healthcare professionals make their
Computer-aided diagnosis system
The CADx encompasses four main processes: pre-processing of ECG signals, extraction of distinctive features from the signals, selection of the most and highly significant features, and lastly, feed the selected features into the classifier for classification [8] (see Figure 3).
- (i)
Input ECG signal:
The ECG signals may be acquired from opensource databases such as the MIT-BIH atrial fibrillation (afdb) and MIT-BIH arrhythmia (mitdb) [37].
- (ii)
Pre-processing (Pre):
The typical frequency of P and T wave
Discussion
Table 4 summarizes a list of published studies of CADx algorithms that could objectively differentiate AF ECG signals from the ECG signals of a healthy person or the ECG signals obtained from other heart conditions. Numerous techniques have been proposed to develop the CADx system. The time-frequency domain and nonlinear analysis approaches were commonly employed. Based upon the studies in Table 4, it can be concluded that the combination of nonlinear and time-frequency domain features can
Future development
Common triggers for AF are usually caused by coronary artery disease (CAD), heart failure (HF), high blood pressure (HBP), and myocardial infarction (MI) [73]. Thus, it is necessary for the CADx system to also be able to detect these cardiac abnormalities. From literature studies, there are existing CADx models developed to diagnose different heart conditions, namely CAD [4], [99], CHF [96], and MI [6], using ECG signals. Therefore, the model will be able to provide preliminary pronouncement of
Conclusion
AF is an irregular and often fast heart rhythm that may lead to serious health problems. Early detection of AF will avert further progression, and with appropriate preventive treatment, reduce the likelihood of patients developing stroke and other heart-related complications. Therefore, it is vital to attend to screening for AF effectively and efficiently. This review paper provided a comprehensive overview of existing CADx systems designed to aid cardiologists in their diagnoses of AF. The
References (110)
- et al.
Dynamic ECG features for atrial fibrillation recognition
Comput. Methods Progr. Biomed.
(2016) - et al.
Linear and nonlinear analysis of normal and CAD-affected heart rate signals
Comput. Methods Progr. Biomed.
(2014) - et al.
Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network
Knowl. Based Syst.
(2017) - et al.
Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network
Inf. Sci.
(2017) - et al.
Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
Inf. Sci.
(2017) - et al.
Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network
Future Gener. Comput. Syst.
(2018) - et al.
A deep convolutional neural network model to classify heartbeats
Comput. Biol. Med.
(2017) - et al.
ECG-based atrial fibrillation detection using different orderings of conjugate symmetric-complex Hadamard transform
Int. J. Cardiovas. Acad.
(2016) - et al.
Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine
Comput. Biol. Med.
(2015) - et al.
Neural network and wavelet average framing percentage energy for atrial fibrillation classification
Comput. Methods Progr. Biomed.
(2014)