# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Cardiac Arrhythmia Detection  
A cardiac arrhythmia is when the heart beats erratically [129]. The primary reason for an arrhythmia can be a heart rate that is abnormally high, abnormally low, or irregular [130]. This happens due to abnormal signals from the heart. Mainly two kinds of heart arrhythmias exist. They are usually categorized using the heart rate, namely tachycardia and bradycardia (Figure 16) [131]. tachycardia is a medical term used to describe an elevated heart rate that exceeds the normal range, often above 100 beats per minute in adult individuals. In contrast, bradycardia is characterized by a diminished heart rate, typically falling below 60 beats per minute among adult individuals. Both tachycardia and bradycardia  
Figure 15: The risk factors for heart attack. can manifest within the context of sinus rhythm, wherein the heart's electrical impulses originate from the sinus node, which serves as the heart's intrinsic pacemaker. Various approaches to analyzing HRV signals may exhibit distinct behaviors when employed on individuals experiencing tachycardia or bradycardia as discussed below. * Time-Domain Analysis: In subjects with tachycardia, shorter RR intervals can result in reduced variability between consecutive intervals, potentially affecting time-domain measures such as the standard deviation of NN intervals (SDNN) and the root mean square of successive NN interval differences (RMSSD) [29]. In subjects with bradycardia, longer RR intervals might lead to increased variability, potentially influencing the same time-domain measures [132]. * Frequency-Domain Analysis: In subjects with tachycardia, the faster heart rate can lead to higher-frequency power dominating the spectrum [133], potentially obscuring the interpretation of low-frequency (LF) and high-frequency (HF) components. In subjects with bradycardia, the opposite might occur, making frequency-domain interpretation more straightforward [134]. * Non-linear Analysis: Non-linear HRV analysis methods, such as Poincare plots and fractal analyses, might be affected by the irregularity of the heart rate in tachycardia and the extended RR intervals in bradycardia [135]. These methods might need adjustments or specific considerations for accurate interpretation. * Geometric Analysis: Geometric methods, like the triangular index and Lorenz plot, might be sensitive to the distribution of RR intervals [136], which can differ in tachycardia and bradycardia. These methods might need normalization or transformation to account for such differences. In general, the issues that might arise from arrhythmias include the risk of having a stroke and heart failure [137]. Arrhythmias have also been linked to a higher risk of blood clots. A blood clot can move from the heart to the brain and cause a stroke if it breaks away [138]. Many researchers have suggested different detection methods for cardiac arrhythmias. Atrial fibrillation (AF) is an arrhythmia that may modify heart rhythm dynamics and ECG morphology. It is distinguished by the presence of irregular and rapid electrical impulses inside the atria, resulting in an irregular and frequently elevated heart rate. The measurement of HRV in individuals diagnosed with AF might offer valuable insights into  
Figure 16: Types of cardiac arrhythmias. the impact of the autonomic nervous system on the development and progression of this condition. Furthermore, such an analysis may have significant consequences for the process of assessing an individual's risk level and managing their treatment plan. Nevertheless, it is crucial to acknowledge that the study of HRV in patients with AF can present challenges owing to the irregularity of the cardiac rhythm. Conventional approaches for measuring HRV may not be well-suited for examining datasets characterized by irregular intervals between successive heartbeats. Various algorithms and methodologies have been specifically designed to tackle these challenges and derive significant metrics related to HRV from the data of individuals with AF.