# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## 3 ECG Signal Analysis
Finally, the PR interval shows the time the electrical signal travels from the atria to the ventricles. These features provide crucial information about the heart's electrical activity and help diagnose and monitor several cardiac conditions. * Frequency domain: Frequency domain features of ECG refer to the characteristics of the ECG waveform in terms of frequency, obtained by converting the ECG signal from the time domain to the frequency domain using techniques like Fourier transform [59; 60; 61]. ECG's most common frequency domain features are power spectral density (PSD), frequency bands, and heart rate variability (HRV). PSD measures the ECG signal's power at different frequencies and provides information about the signal's energy distribution over a broad range of frequencies. The frequency bands associated with ECG signals are shallow frequency (VLF), low frequency (LF), and High frequency (HF). VLF power is linked to long-term heart rate and blood pressure fluctuations. The autonomic nervous system's sympathetic and parasympathetic branches control several physiological processes, including heart rate variability (HRV). Frequency domain methods, such as spectral analysis, which divides the HRV signal into several frequency bands, are frequently used to investigate HRV. In HRV analysis, the high-frequency (HF) band (usually 0.15-0.4 Hz) is linked to parasympathetic (vagal) activity, while the low-frequency (LF) band (generally 0.04-0.15 Hz) is thought to represent a combination of sympathetic and parasympathetic impulses. HRV measures  
Figure 3: An example of an ECG wave with and without baseband wander [51] CC by 4.0.  
the time intervals between successive heartbeat variations, helps to assess the risk of arrhythmia, and provides information about the autonomic nervous system's function. By analyzing the ECG's frequency domain features, medical professionals can gain insights into the heart's functioning and the autonomic nervous system. * Statistical domain: The statistical features of ECG are parameters that describe the distribution of the ECG signal in the time and frequency domains. These features include measures of central tendency, such as mean and median, measures of variability, such as variance and standard deviation, and measures of distribution, such as skewness and kurtosis. The mean of the ECG signal represents the average amplitude of the waveform over a specific time interval. At the same time, the variance measures the spread of the signal around the mean. High variability can indicate that the signal has many variabilities, while low variability indicates that the signal is relatively stable. Skewness measures the degree of asymmetry of the distribution, while kurtosis measures the degree of the peakedness of the distribution. Heart rate, also known as rate, is another statistical feature of ECG that describes the number of heartbeats per minute. This feature can assess the cardiovascular system's overall health and detect rapid or slow heart rate abnormalities. These statistical features can be used to diagnose and monitor cardiovascular diseases by extracting them through mathematical techniques like signal processing and machine learning algorithms. They can also be used to develop automated ECG analysis tools for clinical applications. The feature selection stage is mandatory because, based on the objective, target variable, or outcome, the requirement of several features and a choice of feature changes. The features can be ranked based on the correlation with the class variable, and a threshold can be used to choose a certain number of features. Usually, there are three types of feature selection techniques:  
* Filter-based: The filter-based method [62] evaluates each feature's relevance independently of the machine learning algorithm by analyzing the correlation between each feature and the target variable. Filter-based methods include correlation-based feature selection (CFS), mutual information, and variance threshold.