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Research | DOI: https://doi.org/10.31579/2693-2156/023
*Corresponding Author: Sidi Mohamed Debbal, Genie-Biomedical Laboratory (GBM), Faculty of Technology, University of Aboubekr Belkaid – Tlemcen, BP 119, Tlemcen (Algeria).
Citation: Fatima Mokeddem, Sidi M. Debbal (2021). Cardiac Severity Analysis. J Thoracic Disease and Cardiothoracic Surgery, 2(2); DOI: 10.31579/2693-2156/023
Copyright: © 2021 Sidi Mohamed Debbal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received: 28 May 2021 | Accepted: 30 July 2021 | Published: 11 August 2021
Keywords: severity; energy ratio; cardiac frequency; phonocardiogram signal; murmur
Phonocardiogram (PCG) signal is a particular approach to explore cardiac activity, to develop technics that may serve medical staff to diagnose several cardiac diseases. We took advantage of PCG signal that shows heart murmurs on its tracing dissimilar to other cardiac signals, to design an algorithm to study and classify heart murmurs. In this paper, the importance is given to the severity of murmurs to highlight its impact, since depending on its stage the patient could be in life-threatening point; therefore, the purpose of this paper is focused on three essential steps: according to the algorithm, extracting murmurs and classifying them to deferent stages then investigate the impact of severity on cardiac frequency through some parameters. The severity stage calculation was based on energy ratio (ER) which is recommended by recent studies as an effective factor, however, we succeed to validate that murmur energy (ME) is also a qualified feature to determine severity. But despite that murmur duration, it's an inefficient way to judge the cardiac severity, which is a very important indicator of the general health of the human body. This study is done on considering many patients and it reveals very interesting results.
1. Murmurs extraction
Murmurs extraction has been done on previous work [5,6] by calculating the average Shannon energy envelop to set the beginnings and ends of heart sounds and heart murmurs and isolate individual sounds (S1 or S2) and hold only murmurs. Since the aim of this work is not to explain the separation process we are passing it to the next step.
2. Severity calculation
Once murmurs are extracted, we classify them according to their severity. They are many techniques to calculate the severity [7] a used gradient of pressure and blood velocity nevertheless this technique shows some limitations because of its dependence on transvascular flow, in the same context the American Heart Association (AHA) and the American College of Cardiology (ACC) recommend using the valvular area to quantify the severity [8] but this parameter may tend to overestimate the severity and its need specific care during patient examination, D Kim et al reached to measure the duration of murmur at 300 Hz across a PCG time-frequency representation to define severity [9] but this technique still not give all about murmurs because chronology doesn’t reflect the intensity of murmur that’s why in the paper we try to proceed a method that explores the real severity of the murmur. The severity calculation process is built on energy calculation as an important factor to define the total presence of murmur on the cardiac cycle by comparing it to the energy of the other major sounds S1 and S2. Based on previous studies [10-12] that have been shown that energy ratio is a fair clue and useful argument for severity classification, energy ratio ER is given by the following equation:
Where:
E murmur: is the energy of murmur, ES1: energy of the first heart sound S1, ES2: energy of the
second heart sound S2. Murmurs are classified by multiplying RE by 100 to get a percentage in order that what is between:
Database of cardiac abnormalities of heart sounds was taken from [B, C]. The abbreviations of PCG signals used in this study and their sampling frequencies are given in Table 1.
This study reveals very interesting results that can be arranged on three points: severity of murmur calculation and classifying them into degrees, studying the impact of this severity on the cardiac frequency, and the link between murmur duration over heart cycle and its severity. Table.2 reflects the efficiency of using energy ratio RE as a procedure to define the stage of heart pathologies from mild and medium to severe pathology that needs to pay attention and have special care or an emergency medical intervention in some cases.
The table.2 shows the results beyond RE calculation for AS case and the same work was applied on the other pathologies mentioned above (table.1).
1. Correlation between murmur’s energy and energy ratio
In the first place, we tried to find the correlation between murmur energy and the severity degree, to validate that murmur energy can be also an effective way of judging heart disease severity. Figures from 2 to 5 show a strong correlation between these two features (Murmur energy /severity).
Signals in this stage are arranging from 1st degree to 5th /6th degree based on the energy ratio RE method.
The evolution of murmur energy points on graphs is very close to the increasing slope of optimization, which implies that a heavy murmur (high energy) is automatically a severe murmur with a proportional relation.
Since we are taking advantage form the nature of the phonocardiogram signal that shows murmurs on its graphical tracing, some features have been extracted to figure out the impact of severity of these murmurs on cardiac frequency, but first we need to define the cardiac frequency which is the inverse of duration T between two successive peaks of the first sound S1 as given by the equation below:
It should be noticed that the cardiac frequency here is not the heart rate well known as Bpm (number of beats per minute), while the cardiac frequency is a parameter with the unit (Hertz).
For a healthy adult, at rest, the average heartbeat is 75 Bpm and for this, the cardiac cycle time is 0.8 sec and Fc = 1.25 Hz (theoretically). The cardiac frequency for a normal person who presents no pathology has been calculated by our algorithm is around: 1.2475 Hz (the average value) and the findings in table 2 will be compared by this value [13, 14].
2. The impact of severity on cardiac frequency
To better understand these phenomena we extended the study to comprise the variation of the cardiac frequency over severity from 1st degree to 4th degree as illustrated by the following figures.
These figures reveal very interesting results and show that the severity not only affects the cardiac frequency in a general way because it’s normally fluctuating around 1.2475 Hz for healthy persons (here’s the Fc of all pathologies is deferent from this value) but also affecting this variability in such a way: according to the table.2 cardiac frequency is hugely affected by murmur’s severity where it jumps to highest values (3.9Hz) for severe murmur, which means noticeable heartbeats and uncomfortable symptoms, according to the reference D heart becomes weak and needs to work harder to pump blood through the body, experts classify severe AS as a serious matter because it’s related to the aortic valve damage and it could reach to a life-threatening point.
Besides figures from 6 to 8 present a high variation of cardiac frequency evolution for each pathology from 1st to 4th degree which means a remarkable variation that we quantified by ∆F(deference between the average value of Fc1st and Fc4th degree) presented by the following findings:
The previous figures Fig.9 and Fig.10 reflect the impact of severity by two important features, which are the standard deviation and the average value frequency over severity for each pathology.
At the first glance the average values of Fc (Fig.9) seem like a random distributed points cloud because it’s limited in a small interval [0.8-1.6] Hz, the optimization lines show the real tendency, whatever ascending or descending over severity it reveals on a very interesting result about severity impact.
Also according to Fig.10, the standard variation numbers over severity are attended to be convergent in an advanced stage of severity for AS case and more divergent for MS and SMP cases, which can be a highlight result for this study.
3. Correlation between murmur’s severity and duration
The point behind the determination of this relation is to figure out if a long duration of a heart murmur means a severe stage of the pathology, nevertheless, the representation in Fig.11 shows aleatory fluctuations between murmur’s duration and degree of severity of each PCG signal, ranging from 1st to 4th stage, the fluctuations did not take an increasing order which answers the hypothesis mentioned above. We recommend that the duration of a murmur is an insufficient criterion for determining the degree of severity.
Heart murmurs are a serious health matter in the entire world and phonocardiogram signal is a particular approach to extract as much possible information about murmurs. In this study, we tried to focus on severity and their impact on cardiac frequency.
Energy ratio shows its efficiency as an important process to calculate the severity stage of a heart murmur and classify them according to ER to mild, medium, and severe murmurs. Also, murmur’s energy (MR) has been demonstrating a strong correlation with severity, it can be also a qualified feature to classify heart murmurs severity. The study reveals on very interesting result concerning the impact of severity on the cardiac frequency where it’s hugely affected by severe murmurs, features like ∆F, the average value, and the standard deviation highlight this result. As well and according to this study, we have been confirmed that murmur duration, it cannot be an adequate criterion to judge the severity and it should be always checked by another method. All these results obtained can be further improved and reinforced if this statistical study can be done on a larger number of pathologies.
The authors would like to thank the Directorate-General of Scientific Research and Technological Development (Direction Générale de la Recherche Scientifique et du Développement Technologique, DGRSDT, URL: www.dgrsdt.dz, Algeria) for the financial assistance towards this research.
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