Accuracy of Heart Auscultation Examination by General Practitioners From Various Levels of Competency in Detection of Heart Abnormality in Basic School Children

Background. Most of heart defects in children do not show typical clinical symptoms. Ten percent of the cases are late detected. Echocardiography is an examination with high sensitivity and specificity in detecting heart defects in children, but it cannot be performed by all health workers, expensive and not always available in hospitals. Auscultation is an important part of a physical examination that inexpensive, easy examination, and becomes a competency of all doctors. The aim of this study to determine the accuracy of the screening method by listening to murmurs on heart auscultation by various levels of physician competence. Methods. This is a diagnostic test of 250 elementary school children held in the pediatric ward of dr. Mohammad Hoesin Palembang from September to November 2019. The auscultation examination was performed by three pediatrics resident from three stages (i.e. junior, middle and senior), followed by echocardiography examinations by a pediatric cardiologist. Results. The highest sensitivity of auscultation was found in senior resident, 42.4%, while the lowest was found in junior resident, 12.1%. The results of the kappa analysis of the cardiac auscultation examination on the three examiners showed a poor level of agreement on junior stage compared to senior resident (k = 0.189; CI = 0.033-0.346) and the level of agreement was sufficient in junior stage compared to middle stage resident (k = 0.297; CI = 0.134 -0.461) and middle stage compared to senior resident (k = 0.301; CI = 0.147-0.456). Conclusion. Experience and length of learning will affect the accuracy of the auscultation examination in detecting heart defects in children.

Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process

Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.

Enhanced Performance of PCG Signal using Effective Feature Extraction Method

Phonocardiography (PCG) is the realistic portrayal of sounds created in the heart auscultation. PCG is an improvement for ECG. Particularly in observing of patient and biomedical research, these signals need to do the diagnosis. This paper deals with the processing of heart sound signals i.e., Phonocardiography (PCG) Signals. The primary goal of analyzing these heart sound signals is to separate the signals from the noisy background and to extract some parameters which are used for patient monitoring and for other researches. Various momentum explore ventures are going on biomedical signal processing and its applications. The performance of the PCG signal will comprise of sectioning the signal into S1 and S2 and then compare, whether the PCG is normal or abnormal. In the previous framework the different change approaches are utilized to break down the PCG signal.In the primary stage, for include extraction; acquired heart sound signals were isolated to its subgroups utilizing discrete wavelet change with Level-1 to Level-10. This upgraded strategy proposes a best component for Heart Signal Features, which are removed and changed in to other area to arrange signals. This enhanced method proposes a best feature for Heart Signal Features, which are extracted and transformed in to other domain to classify signals. In the proposed strategy the Wavelet is utilized for highlight extraction and different Statistical strategies are utilized. InformationGain (IG), Mutual Information (MI) and so on. Feature selection techniques are compared using classifiers like kNN(k-Nearest Neighbor), Naïve Bayes, C4.5 and Support Vector Machines (SVMs). MATLAB & WEKA Soft wares are used for analysis Purpose. In this paper, coiffelet technique is utilized to analyze the synthetic PCG and the classifier parameters are compared with one another.

Intelligent Diagnosis of Heart Murmurs in Children with Congenital Heart Disease

Heart auscultation is a convenient tool for early diagnosis of heart diseases and is being developed to be an intelligent tool used in online medicine. Currently, there are few studies on intelligent diagnosis of pediatric murmurs due to congenital heart disease (CHD). The purpose of the study was to develop a method of intelligent diagnosis of pediatric CHD murmurs. Phonocardiogram (PCG) signals of 86 children were recorded with 24 children having normal heart sounds and 62 children having CHD murmurs. A segmentation method based on the discrete wavelet transform combined with Hadamard product was implemented to locate the first and the second heart sounds from the PCG signal. Ten features specific to CHD murmurs were extracted as the input of classifier after segmentation. Eighty-six artificial neural network classifiers were composed into a classification system to identify CHD murmurs. The accuracy, sensitivity, and specificity of diagnosis for heart murmurs were 93%, 93.5%, and 91.7%, respectively. In conclusion, a method of intelligent diagnosis of pediatric CHD murmurs is developed successfully and can be used for online screening of CHD in children.

Acoustic Labyrinth: Validation of a game – based heart auscultation educational tool

The aim of the study was to validate a prototype of a game-based educational tool for improving auscultation skills. The tool was presented to 12 medical school students studying at a foundation university. The data collection tools of the study were: Cardiac sound identification form, educational tool evaluation form and auscultation survey form. Key findings of the study were: 1—Each medical student increased their identification skills and retention was possible. 2—The most incorrectly identified heart sound was the most correctly identified heart sound after using the tool. 3—Medical students sided with the tool for it is flexible, quicker method of learning and getting feedback, can be used anytime, anywhere without interruption of daily life. 4—Since students felt skillful and epic, in real-World tackling problems, on the mission; saving lives, and competitive, they repeated the content otherwise they would not. 5—The tool created a hype and motivation for further learning. 6—Tool was effective on the users with possible restricted acoustic capability which could imply findings might also be used for improving listening skills and musical ear. Keywords: Stethoscope skills, heart auscultation training, mobile learning, game-based learning, retention.

A Comparison of Electronic and Traditional Stethoscopes in the Heart Auscultation of Obese Patients

Background and objectives: As the prevalence of obesity is increasing in a population, diagnostics becomes more problematic. Our aim was to compare the 3M Littmann 3200 Electronic Stethoscope and 3M Littman Cardiology III Mechanical Stethoscope in the auscultation of obese patients. Methods. A total of 30 patients with body mass index >30 kg/m2 were auscultated by a cardiologist and a resident physician: 15 patients by one cardiologist and one resident and 15 patients by another cardiologist and resident using both stethoscopes. In total, 960 auscultation data points were verified by an echocardiogram. Sensitivity and specificity data were calculated. Results. Sensitivity for regurgitation with valves combined was higher when the electronic stethoscope was used by the cardiologist (60.0% vs. 40.9%, p = 0.0002) and the resident physician (62.1% vs. 51.5%, p = 0.016); this was also the same when stenoses were added (59.4% vs. 40.6%, p = 0.0002, and 60.9% vs. 50.7%, p = 0.016, respectively). For any lesion, there were no significant differences in specificity between the electronic and acoustic stethoscopes for the cardiologist (92.4% vs. 94.2%) and the resident physician (93.6% vs. 94.7%). The detailed analysis by valve showed one significant difference in regurgitation at the mitral valve for the cardiologist (80.0% vs. 56.0%, p = 0.031). No significant difference in specificity between the stethoscopes was found when all lesions, valves and both physicians were combined (93.0% vs. 94.4%, p = 0.30), but the electronic stethoscope had higher sensitivity than the acoustic (60.1% vs. 45.7%, p < 0.0001). The analysis when severity of the abnormality was considered confirmed these results. Conclusions. There is an indication of increased sensitivity using the electronic stethoscope. Specificity was high using the electronic and acoustic stethoscope.