Name
Abstract | ||
|---|---|---|
Humans are different in many ways: fat or thin, young or old, sick or healthy; they may differ in auscultation sites which may vary according to the patient's anatomy. Emphasis must be placed on the characteristics of heart sound based on its intensity which greatly depends on the location of the stethoscope to its pericardium. Each one of these areas will emphasize certain characteristics components of the heart sound. Grouping of the first heart sound (lub) is called the S1 features while the second heart sound (dub) is called the S2 features, the systolic or diastolic features are important factor to determine the types of murmurs. To this end, studies have been limited to reflect on the development and evaluation methods in order to detect the various components constituting signal of the heart sound at one specific auscultation point. The principle area of interest in this paper is, however placing the stethoscope at the semi lunar valve called aortic as position one and pulmonary as position two which will provide better quality of the S2 sound. The S1 heart sound can be heard more clearly in the atroventricle (AV) where the mitral valve as position three and tricuspid valve as position four. Comparative experiments with respect to MFCC feature, different number of HMM states and different number of gaussian mixtures were investigated to measure the influence of these factors on the classification performance at the four locations of auscultation of the heart sound. Interestingly, a five-state model outperformed the four-state model which was supposed to model the four basic components of the heart sounds. It can be said, a five-state average over all Gaussian mixtures model and at the four locations provide the best overall performance of 90.1% accuracy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISSPA.2012.6310669 | Information Science, Signal Processing and their Applications |
Keywords | Field | DocType |
Gaussian processes,biomedical ultrasonics,cardiology,cepstral analysis,hidden Markov models,medical signal detection,signal classification,valves,Gaussian mixtures,HMM states,MAHDS,MFCC feature,aortic valve,atroventricle,auscultation point,auscultation sites,classification performance,diastolic features,heart sound characteristics,heart sound diagnostic system,mitral valve,multipoint auscultation,patient anatomy,pericardium,pulmnary valve,semilunar valve,signal detection,stethoscope,systolic features,tricuspid valve,cardiac ausculatation,heart murmurs,hidden markov model | Phonocardiogram,Mel-frequency cepstrum,Stethoscope,Pattern recognition,Heart murmur,Computer science,Tricuspid valve,Speech recognition,Artificial intelligence,Auscultation,Mitral valve,Heart sounds | Conference |
ISBN | Citations | PageRank |
978-1-4673-0380-4 | 0 | 0.34 |
References | Authors | |
3 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hadrina Sh-Hussain | 1 | 4 | 2.07 |
| S. Hussain | 2 | 47 | 9.46 |
| Ahmad Kamaru Ariff | 3 | 1 | 0.70 |
| Osama Alhamdani | 4 | 0 | 0.34 |
| Tan Tian Swee | 5 | 37 | 1.77 |
| Alias Mohd Noor | 6 | 13 | 2.38 |
| Hamed Oemar | 7 | 0 | 0.34 |
| Khalid Yusoff | 8 | 0 | 0.34 |
| Salleh, S.H. | 9 | 0 | 0.34 |