HEART SOUND CLASSIFICATION AND APPLICATION

1. Project Guide Presented
By:
Mr. Ajeet Kumar Hitendra
Singh
M. Tech.
Final Year
Roll No -
2204860105001
A Review on Heart Sound Signals and
Application

2. Contents
 Introduction
 Overview of Heart Sounds
 Phonocardiography
 Normal vs Abnormal Heart Sounds
 Applications
 Related Works
 Conclusion
 References

3. Introduction
 Earlier in 1816, Hyacinth Laennec invented the acoustic stethoscope, a device
for monitoring the heart. Direct auscultation (paying attention to the body) has
been the conventional strategy for cardiovascular determination. Around then,
inadequate physiologic information on the cardiovascular framework brought
about incorrect translation of heart sounds.
 In any case, the achievement of sound recording and graphic representation of
cardiac sounds in 1895 improved clinical conclusions. The phonocardiogram
(PCG), a waveform display that records heart sounds, was developed to externally
evaluate heart sounds with the ultimate purpose of clinical analysis.
 Valvular cardiovascular dysfunctions can be identified inexpensively and
productively utilizing auscultation with cutting edge procedures of sign handling.
There are two stages to the auscultation process: securing and monitoring cardiac
sounds [1-2].

4.  The procedure of the heart sound classification usually consists of
three steps: heart sound segmentation, feature extraction, and
classification. The heart sound segmentation aims at segmenting
the heart sound signal into a series of cardiac cycles. From each of
cardiac cycles, the feature is extracted, which captures the
information about the mechanical activity of the heart in one cardiac
period. The extracted feature is input into the classifier, such as
Artificial Neural Networks (ANN), Support Vector Machines (SVM)
and Hidden Markov Models (HMMs), to identify the abnormal heart
sound which usually relates to some heart condition.
 In fact, the primary task of heart sound classification can be
performed without heart sound segmentation, as done in. The goal
of the primary task is only to detect the presence of a disorder in
the heart sound rather than to further identify it, which is helpful to
provide a primary diagnosis in the primary health center and home
care. Certainly, the results of the primary diagnosis can also be

5. Overview of Heart Sounds
Heart sounds refer to the noises produced by the
beating heart and the resultant flow of blood
through the heart's chambers and valves. These
sounds are typically heard through a stethoscope
and are essential diagnostic indicators for
assessing cardiac health. The primary heart
sounds, often described as "lub-dub," are labeled
as S1 and S2.
 S1 (First Heart Sound or "Lub"): This sound is
associated with the closure of the atrioventricular
(AV) valves (mitral and tricuspid valves) during
the beginning of ventricular contraction or systole.
S1 marks the onset of ventricular contraction and
the initiation of blood ejection into the pulmonary
and systemic circulation.

6.  S2 (Second Heart Sound or "Dub"): S2 is linked to
the closure of the semilunar valves (aortic and
pulmonary valves) at the end of ventricular systole.
This sound occurs as the ventricles relax and blood
flow diminishes, preventing backflow of blood from the
major arteries into the ventricles.
 In addition to S1 and S2, there are additional heart
sounds, such as S3 and S4, which may be indicative
of underlying cardiac conditions. These extra sounds
are less prominent and are associated with specific
phases of the cardiac cycle.
• S3 (Third Heart Sound): S3 occurs during early
diastole and is often associated with conditions like
heart failure. It is caused by rapid ventricular filling.
• S4 (Fourth Heart Sound): S4 occurs late in diastole
and is associated with conditions like hypertrophic
cardiomyopathy. It results from the contraction of the
atria forcing blood into a stiff or hypertrophied
ventricle.

7. Phonocardiography
 Phonocardiography is a diagnostic technique used in cardiology to
record and analyze the sounds produced by the heart. It involves the
use of a device called a phonocardiogram to convert heart sounds into
visual or electronic signals. The primary tool for capturing these sounds
is a stethoscope, which is placed on the chest to detect and amplify the
various heart sounds.
 Recording Heart Sounds:
A stethoscope is placed on specific locations on the chest, typically at the
apex and base of the heart, to capture the sounds produced during the
cardiac cycle.
The heart sounds, including S1 (lub), S2 (dub), and additional sounds like
S3 and S4, are detected by the stethoscope's diaphragm or bell.
 Amplification and Filtering:
The electrical signals are then amplified to enhance their strength for better
analysis.
Filtering techniques are often applied to isolate specific frequency ranges
associated with different heart sounds.

8. Contd..
 Signal Processing:
The processed signals are then recorded on paper or displayed graphically
on a screen, creating a phonocardiogram.
The phonocardiogram represents the changing intensity and frequency of
heart sounds over time.
 Analysis and Interpretation:
Healthcare professionals analyze the phonocardiogram to assess the
timing, intensity, and characteristics of each heart sound.
Abnormalities in the heart sounds, such as murmurs or additional sounds,
can be indicative of underlying cardiac conditions.
 Clinical Applications:
Phonocardiography is used for various clinical purposes, including the
diagnosis of heart murmurs, valve disorders, and other cardiovascular
conditions.
It is often employed alongside other diagnostic tools, such as
electrocardiography (ECG) and echocardiography, to provide a
comprehensive assessment of cardiac health.

9. Normal vs Abnormal Heart Sounds
 Normal Heart Sounds:-
S1 (Lub):
1. Represents the closure of the atrioventricular (AV) valves (mitral and
tricuspid valves).
2. Occurs at the beginning of ventricular systole (when the ventricles
contract).
3. Louder at the apex of the heart.
S2 (Dub):
1. Marks the closure of the semilunar valves (aortic and pulmonary
valves).
2. Occurs at the end of ventricular systole (when the ventricles relax).
3. Louder at the base of the heart.
S1-S2 Interval:
1. The interval between S1 and S2 is normally shorter during inspiration
and longer during expiration (due to changes in intrathoracic pressure).
No Extra Heart Sounds:
1. In a healthy heart, there are no additional heart sounds (S3 or S4)
heard during routine auscultation.

10.  Abnormal Heart Sounds:-
Murmurs:
1. Unusual whooshing or swishing sounds during the
heartbeat.
2. Can be innocent (benign) or pathological (indicative of
heart valve disorders or other conditions).
S3 (Third Heart Sound):
1. Heard in early diastole.
2. May indicate heart failure or volume overload.
S4 (Fourth Heart Sound):
1. Heard in late diastole.
2. May indicate conditions like hypertrophic
cardiomyopathy.

11. Applications
 The field of artificial intelligence known as deep learning is a subfield that
attempts to simulate human thought processes by employing artificial neural
networks to do sophisticated computations that are driven by actual brain
activity.
 It can therefore eliminate the characteristics of distinct transmissions and figure
out the guidelines among information through a more profound learning than
the customary AI, subsequently working on its precision and productivity of
grouping.
 Deep learning makes use of the room's overall relationship to combine low-
level models into higher-level, demonstrably complex models, so greatly
enhancing the framework's preparation and execution. As of late, it has shown
great common sense and dependability in the fields of discourse
acknowledgment, picture acknowledgment, biomedical information
examination, signal handling, programmed driving and different regions. Heart
sound indicators have been categorized using profound learning models, which
mostly use Deep Neural Networks (DNN), Artificial Neural Networks (ANN),
Recurrent Brain Organizations (RNN), and so on.

12. Related Work
 The PC supported handling of cardiac sounds incorporates denoising, division,
highlight extraction and order [6].
 A DWT-focused PCG signal noise reduction computation utilizing the "Style 5", as
mother wavelet was proposed by Gabarda et al. [7] and joined with a versatile limit
assessment technique, a nonlinear transitional capacity strategy and a hereditary
calculation, to upgrade the customary discrete wavelet change (DWT) calculation.
 The further developed calculation dispensed with out-of-band clamors and
eliminated the coefficients at lesser detail levels, further developing the denoising
execution. Martínez et al. [8] presented an original heart-tone denoising technique in
view of the consolidated system of wavelet parcel change and SVD.
 The objective of element extraction is to figure out few agent elements to supplant
the high-layered crude signs. As a general rule, the characterization model in light of
highlights preparing is more productive and precise than that which depends on
crude signs preparing. Include extraction is carried out on the divided sign. DWT,
Mel Frequency Cepstrum Coefficient (MFCC), Consistent Wavelet Change (CWT),
Brief Time Frame Fourier Change (STFT), and DWT are often used techniques for
heart sound extraction.

13. Contd..
 In the absence of division, include extraction can be guided by either the
denoised or crude signal. It is possible to group signals based on elements,
crude signs, and denoised signals. The objective of order is to introduce the
subjective aftereffects of the identification, isolating the heart sound signs into
the typical or unusual.
 Heart sound clustering techniques include Support Vector Machines (SVM),
Hierarchical Multiplier Methods (HMM), and Convolutional Neural Network
(CNN), Logistic Regression (LR), k-Nearest Neighbousr (kNN), Decision Tree
(DT), Euclidean distance, and more.
 The agent writing on the element extraction and arrangement of the heart sound
signs throughout recent years. These procedures (SVM, LR, kNN, BP brain
organization, and calculated relapse) all use AI a calculation that permits PC
frameworks to actually get to and break down information to change and work
on working in view of examples and experience, without the requirement for
express programming.

14. Conclusion
 In recent years, there has been a substantial rise in the number of people who suffer
from cardiovascular disorders, which has sparked interest in non-invasive heart
sound detection technologies. The most recent research on computer-aided heart
sound detection approaches has been undertaken over the course of the past five
years, and this article provides a summary of that research, with a particular
emphasis on the application of deep learning in the classification of heart sounds.
 It is recommended that further research be conducted on the following research
areas in order to evaluate the potential benefits that the technology may have for the
improvement of human health. A substantial quantity of data pertaining to heart
sounds is required in order to be added to the database of heart sounds. Heart sound
data can be a valuable resource for learning about the subtleties of cardiovascular
diseases. Therefore, the heart sound database and accompanying expert annotations
need to be finished and enhanced in order to provide better model training and an
assisted diagnosis that is more accurate. Hospitals already have large-scale
computer systems in place, so it's now feasible to build a complex deep learning
model that can handle heart sound data. Further research is necessary, in our
opinion, on the data processing and parameter optimization processes.

15. References
 Hedayioglu, F.L., Coimbra, M.T. and Mattos, S.S., 2009, Heart sound segmentation
for digital stethoscope integration. Masters Thesis, University of Porto.
 Yuenyong, S., Nishiara, A., Kongprawechnon, W. and Tungpimolrut, K., 2011, A
framework for automatic heart sound analysis without segmentation. Biomedical
Engineering OnLine, 10(13). Available online at: http://www.biomedical-
engineering-online.com/content/10/1/13 (accessed September 2011).
 M. Liu, J. Niu, and X. Wang, “An autopilot system based on ROS distributed
architecture and deep learning,” in 2017 IEEE 15th International Conference on
Industrial Informatics (INDIN), pp. 1229–1234, IEEE, Emden, 2017.
 Macartney, F.J., 1987, Diagnostic logics. British Medical Journal, 295(6609), 1325–
1331. Available online at: http://www.jstor.org/ stable/29528917 (accessed October
2010).
 Kihong, S. and Hammon, J., 2008, Fundamentals of Signal Processing for Sound and
Vibration Engineers (London, UK: John Wiley & Sons).

16. Contd.
 D. Shen, G. Wu, and H. I. Suk, “Deep Learning in Medical Image Analysis,”
Annual Review of Biomedical Engineering, vol. 19, no. 1, pp. 221–248, 2017.
 Martínez-Alajarín, J. and Ruiz-Merino, R., 2005, Efficient method for events
detection in phonocardiographic signals (Spain: Society of Photo-Optical
Instrumentation Engineers). Available online at:
http://wsdetcp.upct.es/Personal/R_Ruiz/Investigacion/SPIE-EMT05_
JMtnez_copyright.pdf Universidad Politecnica de Cartagene, Spain (accessed
January 2011).
 R. D. Judge and R. Mangrulkar, “e Open Michigan Heart Sound & Murmur
Library (OMHSML),” University of Michigan, 2015,
http://www.med.umich.edu/lrc/psb/heartsounds/.
 Cardiac Auscultation of Heart Murmurs database, eGeneral Medical Inc., 2017,
http://www.egeneralmedical.com/listohearmur.html.

17. THANK YOU