A Review of Computer-Aided Heart Sound Detection Techniques.

Cardiovascular diseases have become one of the most prevalent threats to human health throughout the world. As a noninvasive assistant diagnostic tool, the heart sound detection techniques play an important role in the prediction of cardiovascular diseases. In this paper, the latest development of the computer-aided heart sound detection techniques over the last five years has been reviewed. There are mainly the following aspects: the theories of heart sounds and the relationship between heart sounds and cardiovascular diseases; the key technologies used in the processing and analysis of heart sound signals, including denoising, segmentation, feature extraction and classification; with emphasis, the applications of deep learning algorithm in heart sound processing. In the end, some areas for future research in computer-aided heart sound detection techniques are explored, hoping to provide reference to the prediction of cardiovascular diseases.

S1 and S2 Heart Sound Recognition Using Deep Neural Networks.

OBJECTIVE: This study focuses on the first (S1) and second (S2) heart sound recognition based only on acoustic characteristics; the assumptions of the individual durations of S1 and S2 and time intervals of S1-S2 and S2-S1 are not involved in the recognition process. The main objective is to investigate whether reliable S1 and S2 recognition performance can still be attained under situations where the duration and interval information might not be accessible. METHODS: A deep neural network (DNN) method is proposed for recognizing S1 and S2 heart sounds. In the proposed method, heart sound signals are first converted into a sequence of Mel-frequency cepstral coefficients (MFCCs). The K-means algorithm is applied to cluster MFCC features into two groups to refine their representation and discriminative capability. The refined features are then fed to a DNN classifier to perform S1 and S2 recognition. We conducted experiments using actual heart sound signals recorded using an electronic stethoscope. Precision, recall, F-measure, and accuracy are used as the evaluation metrics. RESULTS: The proposed DNN-based method can achieve high precision, recall, and F-measure scores with more than 91% accuracy rate. CONCLUSION: The DNN classifier provides higher evaluation scores compared with other well-known pattern classification methods. SIGNIFICANCE: The proposed DNN-based method can achieve reliable S1 and S2 recognition performance based on acoustic characteristics without using an ECG reference or incorporating the assumptions of the individual durations of S1 and S2 and time intervals of S1-S2 and S2-S1.

A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics.

OBJECTIVE: This research work was aimed at developing a reliable screening device for diagnosis of heart murmurs in pediatrics. This is a significant problem in pediatric cardiology because of the high rate of incidence of heart murmurs in this population (reportedly 77-95%), of which only a small fraction arises from congenital heart disease. The screening devices currently available (e.g. chest X-ray, electrocardiogram, etc.) suffer from poor sensitivity and specificity in detecting congenital heart disease. Thus, patients with heart murmurs today are frequently assessed by consultation as well with advanced imaging techniques. The most prominent among these is echocardiography. However, echocardiography is expensive and is usually only available in healthcare centers in major cities. Thus, for patients being evaluated with a heart murmur, developing a more accurate screening device is vital to efforts in reducing health care costs. METHODS AND MATERIAL: The data set was collected from incoming pediatrics at the cardiology clinic of The Children's Hospital (Denver, Colorado), on whom echocardiography had been performed to identify congenital heart disease. Recordings of approximately 10-15s duration were made at 44,100Hz and the average record length was approximately 60,000 points. The best three cycles with respect to signal quality sounds were extracted from the original recording. The resulting data comprised 241 examples, of which 88 were examples of innocent murmurs and 153 were examples of pathological murmurs. The selected phonocardiograms were subject to the digital signal processing (DSP) technique of fast Fourier transform (FFT) to extract the energy spectrum in frequency domain. The spectral range was 0-300Hz at a resolution of 1Hz. The processed signals were used to develop statistical classifiers and a classifier based on our in-house artificial neural network (ANN) software. For the latter, we also tried enhancements to the basic ANN scheme. These included a method for setting the decision-threshold and a scheme for consensus-based decision by a committee of experts. RESULTS: Of the different classifiers tested, the ANN-based classifier performed the best. With this classifier, we were able to achieve classification accuracy of 83% sensitivity and 90% specificity in discriminating between innocent and pathological heart murmurs. For the problem of discrimination between innocent murmurs and murmurs of the ventricular septal defect (VSD), the accuracy was higher, with sensitivity of 90% and specificity of 93%. CONCLUSIONS: An ANN-based approach for detection and identification of congenital heart disease in pediatrics from heart murmurs can result in an accurate screening device. Considering that only a simple feature set was used for classification, the results are very encouraging and point out the need for further development using improved feature set with more potent diagnostic variables.

Heart murmurs in low birth weight infants.

This study is based on 315 infants in different prematurity stages who were patients in the Special Care Unit for Preterm Infants at the First City Hospital, Sofia. Heart murmurs were observed in 98 infants. The clinical observations and investigations can be grouped as follows: 1) murmurs due to late closing or reopening of ductus arteriosus; 2) murmurs in patients with anemia; 3) murmurs in patient with anemia; and 4) accidental murmurs. The study is focused on the high percentage of heart murmurs in low-birth weight infants during the post-adaptation period.

Reappraisal of the approach to the child with heart murmurs: is echocardiography mandatory?

The performance of cross-sectional echocardiography on every child with a heart murmur poses a heavy load and economic burden on pediatric cardiology services. We prospectively studied 100 consecutive patients newly referred to our clinic for evaluation of a heart murmur. After a thorough clinical assessment and review of the electrocardiogram and chest radiograms, 50 patients were diagnosed as having "innocent murmurs". Cross-sectional echocardiography confirmed the initial diagnosis in 48 patients (96%). Two patients had bicuspid aortic valve. Among the patients with echocardiographically proved innocent murmur, left ventricular anomalous bands were demonstrated in 15 patients (31%), right ventricular anomalous bands in 5 patients (10%) and increased left ventricular shortening fractions (greater than or equal to 0.5) in 10 patients (21%). Among the remaining 50 patients, the initial diagnosis was "suspected heart disease" in 10 patients, and "pathological murmurs" in 40 patients. Cross-sectional echocardiography was most useful in these patients providing the diagnosis in 80% (eight) of the former group and 95% (38) of the later group. Echocardiography need not be routinely performed in children initially diagnosed, by a pediatric cardiologist, as having an "innocent murmur".

Phonocardiogram signal analysis: a review.

Many disease of the heart cause changes in heart sounds and additional murmurs before other signs and symptoms appear. Hence, heart sound analysis by auscultation is the primary test conducted by physicians to assess the condition of the heart. Yet, heart sound analysis by auscultation as well as analysis of the phonocardiogram (PCG) signal have not gained widespread acceptance. This is due mainly to many controversies regarding the genesis of the sounds and the lack of quantitative techniques for reliable analysis of the signal features. The heart sound signal has much more information than can be assessed by the human ear or by visual inspection of the signal tracings on paper as currently practiced. Here, we review the nature of the heart sound signal and the various signal-processing techniques that have been applied to PCG analysis. Some new research directions are also outlined.

[Incidence of innocuous thoracic murmurs in a sample of 12,009 students]. / Incidencia de soplos inocentes toracicos en una muestra de 12,009 escolares.

We have studied a total of 12,009 school children (age range 6 to 14 years) in order to determine the incidence of thoracic innocent murmurs. 6,816 were male and 5,193 female. A initial clinical evaluation was carried out in the school and the second examination was performed in our hospital. Global incidence of innocent murmurs was 9.57% with a female/male ratio of 1.73. We divided the innocent murmurs according with Fishleder's method: mesocardial systolic murmur (62%), pulmonary systolic murmur (26%), protosystolic apical murmur (7%), "whooping" murmur (5%). Afterwards, children with murmurs were divided in two groups of age and anthropometric characteristics observing a significative increment in the incidence of mesocardial systolic murmur in children which ranged ages from 6 to 10 years (74%) and of the pulmonary systolic murmur in the group of 10 to 14 years (40%).

Innocent heart murmurs in children. A survey of 119 patients.

The clinical findings in 119 children with innocent heart murmurs have been reviewed. The majority were aged between 1 and 5 years, and it was in this age-group also that a maximum incidence of venous hum was recorded. There were no particular ECG or chest X-ray findings.

Intracardiac sound and pressure: right heart events. Toward an ultimate classification of auscultatory events.

These observations are presented as a tentative step towards an ultimate classification of auscultatory events based on ICSP studies, chest wall phonocardiography, and auscultatory areas, with continuing clarification or modification by angiography, Doppler techniques, echocardiography, computer usage, and any available, useful technique. The ultimate diagnostic value of ICSP information resides in the feed-back loop wherein this information is used daily at the bedside as part of the understanding and explanation of daily auscultatory phenomena.