According to authors, heart rate variability is a complex analysis done in the medical profession. Therefore, the experiments on this analysis are done using complex statistical methods. The most effective technique that has been in use is the Poincare plot of RR intervals. The Poincare plot is a “visual tool with high ability to summarize RR time series derived from electrocardiogram machines” (Khandoker et al. 83). Hence, this technique gives quantitative information on both the long and short-term heart rate variability. The resultant plot is a cloud of points whose length and breadth describes the long-term and short-term heart rate variances respectively.
The shape of Poincare plot of RR intervals is inspected thoroughly by visual means to determine the quality and credibility of the recorded electrocardiogram signals to identify either the premature or the ectopic beats and the technical artifacts. This method is widely known for “describing effects of both sympathetic and parasympathetic influences on the heart variances at any given time” (Kamath, Watanabe, and Upton 116). Hence, it has been used in the physiological and cardiology studies that evaluate the influence of catecholamine or autonomic blockages on heart rate, changes in heart rate during various activities such as sleep, exercise, or even the paced breathing.
Additionally, the Poincare analysis helps to provide information about a death in post-myocardial infarction, heart failure, and other risks in patients who are subjected to heart surgery. However, the information is effective if the patient is put under the electrocardiogram, and thus there are signals to be analyzed. Additionally, Poincare is also used in other medical fields and especially to the patients who suffer from life-threatening illnesses such as stroke, high blood pressure, renal failure problems, and many others.
The researchers have two main aims, which include doing an extensive analysis of the Poincare plot by defining its parameters using a geometric language and the use of statistical methods to establish that a typical Poincare plot is symmetrical as well as quantifying the asymmetry. Researchers present their results of a study that was done to a hundred health individuals on 30 minutes RR time series to support this theoretical discussion. The results show that the plot has a one-way asymmetry, which is defined as heart rate asymmetry (HRA). However, the study was carried out only on the resting subjects, which could explain why the results have an asymmetric plot.
From the paper, it is observed that a typical Poincare plot for a healthy subject has a cloud shape that resembles an eclipse and thus it is described using two parameters. These two parameters are referred to as plot descriptors SD1 and SD2 where SD1 is the length of the cloud and SD2 is the breadth. The geometric analysis is done by using the second moments of distributions of the two descriptors SD1 and SD2 before establishing a line that passes through the center of the cloud, which proves that the cloud has an asymmetry line (Guzik et al. 273).
Additionally, the Poincare plot has an identity line defined in mathematical language as (y=x) that has a physiological interpretation. Interestingly, the points that pass across this line are said to correspond consecutively to equal RR intervals. However, the points that lie above the line correspond to the decreasing heart rate whereas those below it to the increasing heart rate. From the observations made by the electrocardiogram signals, the heart rate is periodical whereby an increasing rate is always followed by a decreasing rate and vice versa. Hence, in normal healthy condition, there is a higher chance of a symmetrical line passing through the plotted RR intervals in the Poincare plot.
In some case, the RR intervals are longer than normal, and they are referred to as isolated events. Under such circumstance, it is often hard to predict whether the next interval will reflect either a decreased or an increased heart rate. However, under normal health circumstances, a closer inspection always reveals that a broken down symmetry in the Poincare plot, but the asymmetry is still defined as it is always relative to the identity line (Piskorski and Guzik 288).
By applying another approach, researchers argue that if a line of symmetry exists, then it is certain that a centroid is present, and it should lie either on the line or in the close vicinity. Mathematically, this statement is true in the rule of the line of identity, which emphasizes that a centroid should lie on the identity line under normal circumstances. However, there is an exemption under abnormal circumstances, but still it should be in the close vicinity since there is an allowable margin of error in the observations.
The study method used to verify the Poincare plot asymmetry used only healthy individuals to increase chances of obtaining an asymmetrical plot. The sample was a hundred persons, and the recording was done in a resting environment for 30 minutes. To verify the method, the results were shuffled to obtain a random RR vector, which is highly preferable as opposed to the normal physiological order. An asymmetry should always be independent and thus shuffling the data should not have any effect time (Sosnowski et al. 11).
The study method was effective for data collection whereby subjects were kept in an environment that was free from any form of distractions for thirty minutes. Additionally, they had been advised to cool off the thinking activities to assume a resting mood to maximize possibilities of collecting reliable data. Subjects were advised to breathe spontaneously in the course of the study as it enhances to cool off the heart rate to normal conditions. However, to ensure that the recorded data was the most needed, the participants were subjected to a cardiovascular adaptation period of 15 minutes where no recording was done since the majority of the subjects had not rested as required.
The second most notable strength is the limitation of the problem whereby the researchers were focused on the Poincare plot, hence the use of the research methods that bore the desired results. Hence, the research is explanatory as it is limited to a specific goal rather than being descriptive, which in normal circumstances covers a wide scope.
Thirdly, Poincare analysis helps in providing information about a death in post-myocardial infarction, heart failure, and other risks in patients who are subjected to heart surgery. This analysis provides information that can be stored for future analysis in the electrocardiogram machine. Hence, if the patient is put under the electrocardiogram, there are signals that can be analyzed at a future date. Additionally, Poincare is used in other medical fields and especially with patients who suffer from life-threatening illnesses such as stroke, high blood pressure, renal failure problems, and many others because it is the most effective data collection method of heart rate in medicine.
Fourthly, shuffling data method is effective for analyzing statistical interdependence between variables, and thus a necessary tool for carrying out data analysis in the research. The efficiency of shuffling is evidenced by the emergence of asymmetrical properties, which in reality does not emerge in un-shuffled data.
One of the notable weaknesses of the paper is the use of professional language that is not easy for a non-professional to understand. Therefore, the paper can only be used for professional and academic purposes. Besides, the researchers say that the study results do not have any practical purposes in the real world, and thus it is limited to the theoretical realms, which is a weakness, as it should provide knowledge that could be used to fill an existing gap in the research problem. Moreover, the study was carried out only on the resting subjects, which could explain why the results have an asymmetric plot. Hence, the study hypothesis was based on the proving asymmetric characteristic of Poincare plot unlike in the real world situation where research would cover a wide scope of the subject. Even though this aspect is useful in proving a hypothesis in academic purposes, it is a weakness in the real world situations.
Second, the use of complex statistical methods for data analysis is a major weakness for the research since people with no statistical understanding capabilities cannot understand the methods. Other similar methods could have been used to provide asymmetrical properties of the Poincare plot that are easily understood by a nonprofessional. Hence, the researchers over-relied on the regression analysis method, and although it is effective for analyzing asymmetrical properties, it is most efficient for analyzing the correlation between variables.
Third, from the discussion part of the study, it is clear that Poincare plot reflects sudden acceleration and deceleration of heart rates on the both sides of the asymmetry. This aspect explains why researchers applied statistical methods to analyze the trends and characteristics of the plot. The acceleration and deceleration aspects, which in the Poincare plot seem to have a line of symmetry, are clear to a nonprofessional that Poincare has asymmetrical characteristics. Hence, there is little need for researchers going to great lengths of applying complex statistical methods to prove that point. Besides, the reasons behind the sudden fluctuations were not given much attention, which ought to have been explained.
Lastly, the reliability of the collected data is highly dependent on the physical condition and environment of the subjects. This aspect depicts a high chance of collecting unreliable data in the experiment since not every subject could adapt well to the environment exposed to in the process. Importantly, HRA “is a clearly visible and quantifiable phenomenon in resting healthy people” (Piskorski and Guzik 297). However, the study bears positive results only when the subjects are healthy and free from habits and activities that have effects on the heart rate at any given time. Hence, there could be chances of dishonest subjects participating and leading to unreliable results.
This article is informative and especially for individuals in the biomedical field. However, the language used is complex for non-professionals to understand the content of the article.
Guzik, Przemyslaw, Jaroslaw Piskorski, Tomasz Krauze, Andrzej Wykretowicz, and Henryk Wysocki. “Heart rate asymmetry by Poincare plots.” Biomedical Technology 51.4 (2006): 272–275. Print.
Kamath, Markad, Mari Watanabe, and Adrian Upton. Heart Rate Variability (HRV) Signal Analysis: Clinical Applications, New Jersey: CRC Press, 2012. Print.
Khandoker, Habib, Chandan Karmakar, Michael Brennan, Marimuthu Palaniswami, and Andrea Voss. Poincaré Plot Methods for Heart Rate Variability Analysis, New Delhi: Springer Science & Business Media, 2013. Print.
Piskorski, Jaroslaw, and Przemyslaw Guzik. “Geometry of the Poincaré plot of RR intervals and its asymmetry in healthy adults.” Physiological Measurement 28.1 (2007): 287–300. Print.
Sosnowski, Maciej, Elaine Clark, Shahid Latif, Peter Macfarlane, and Michal Tendera. “Heart rate variability fraction-a new reportable measure of 24-hour R-R interval variation Ann.” Noninvasive Electrocardiology 10.3(2005): 7–15. Print.