heart sounds

Several studies have reported the asynchronous closure of normal bileaflet valves (NBVs), resulting in a split in its closing sound; however, the clinical significance of this split has never been studied in malfunctioning bileaflet valves (MBVs). The study comprised 218 valves in 184 patients, including normal monoleaflet valves (n = 10), NBVs (n = 198), and MBVs (n = 10). Valve function was confirmed by cinefluoroscopy prior to analysis of the valve sound by the Morlet continuous wavelet transform (CWT). The split interval (SI) for each heartbeat was measured, and the coefficient of variation (CV) of its mean (valve SI) was calculated as a parameter for the fluctuation of the SI. The CWT of monoleaflet valves showed a single spike, whereas NBVs exhibited a clear split. There was no significant difference in valve SI between the aortic and mitral positions; however, the mean of the CV was significantly greater in the mitral position (n = 90, 0.507 +/- 0.254) than in aortic position...

As health care centres have becomes popular, daily monitoring of health-status related parameters is becoming important. An easy, comfortable and patient friendly solution for acquisition, processing and remotely transmitting the information from patient to the centre is therefore an important issue. Phonocardiogram (PCG) is a physiological signal reflecting the cardiovascular status. This paper deals with a Signal Processing Module for the computer-aided analysis of the condition of heart. The module has three main blocks: Data Acquisition, Signal Processing & Remote Monitoring of heart sounds. Data acquired includes the heart sounds. The system integrates embedded internet technology and wireless technology. As the data is being send by internet, it realizes real-time recording and monitoring of physiological parameter of patients at low cost and both at home and in hospital. The analysis can be carried out using computer initially and further by doctor. The tele-monitoring system...

Auscultation, which means listening to heart sounds, is one of the most basic medical methods used by physicians to diagnose heart diseases. These voices provide considerable information about the pathological cardiac condition of arrhythmia, valve disorders, heart failure and other heart conditions. This is why cardiac sounds have a great prominence in the early diagnosis of cardiovascular disease. Heart sounds mainly have two main components, S1 and S2. These components need to be well identified to diagnose heart conditions easily and accurately. In this case, the segmentation of heart sounds comes into play and naturally a lot of work has been done in this regard. The first step in the automatic analysis of heart sounds is the segmentation of heart sound signals. Correct detection of heart sounds components is crucial for correct identification of systolic or diastolic regions. Thus, the pathological conditions in these regions can be clearly demonstrated. In previous studies, frequency domain studies such as Shannon energy and Hilbert transformation method were generally performed for segmentation of heart sounds. These methods involve quite long and exhausting stages. For this reason, in this study, a re-sampled energy method which can easily segment heart sounds in the time domain has been developed. The results obtained from the experiments show that the proposed method segments S1 and S2 sounds very efficiently.

The stethoscope has been used for many years, and has been very effective to diagnose certain cardiologic and pulmonologic sounds. For many years healthcare professionals would listen quietly to patients internal organs so they could diagnose from specific sounds of such internal organs. The objective is to develop a Peripheral Interface Controller based digital Stethoscope to capture the heart sound. The proposed designed device consists of following hardware stages: Front-end pickup circuitry, PIC18F2550 microcontroller, (128×64) graphic LCD and a Serial EEPROM. The captured data can be sent to PC for software analysis using LabVEIW. A digital stethoscope would help healthcare professional record their findings on a Serial EEPROM. Once the data is stored, the healthcare professionals can hear and plot the graph. This would be quick and more effective since there is a visual and audio representation to diagnose such cardiologic sounds.

Purpose: Digital recording of heart sounds commonly known as Phonocardiogram (PCG) signal, is a convenient primary diagnostic tool for analyzing condition of heart. Phono-cardiogram aids physicians to visualize the acoustic energies that results from mechanical aspect of cardiac activity. PCG signal cycle segmentation is an essential processing step towards heart sound signal analysis. Sound artifacts due to inappropriate placement of stethoscope , body movement, cough etc. makes segmentation difficult. Artifact-free segmented heart sound cycles are convenient for physicians to interpret and it is also useful for computerized automated classification of abnormality. Methods: We have developed a framework which selects good quality heart sound subse-quences which are artifact-free and reused the features involved in this processing in segmenta-tion. In this work, we have used information contained in frequency subbands by decomposing the signal using Discrete Wavelet Packet Transform (DWPT). The algorithm identifies the parts of the signal where artifacts are prominent and it also detects major events in heart sound cycles. Results: The algorithm shows good results when tested on normal and five commonly occurring pathological heart sound signals. An average accuracy of 93.71% is registered for artifact-free subsequence selection process. The cycle segmentation algorithm gives an accuracy of 98.36%, 98.18% and 93.97% respectively for three databases used in the experiment. Conclusions: The work provides a solution for artifact-free segmentation of heart sound cycles to assist interpretation of heart sound by physicians in objective analysis through recording in a computer. It is also useful for development of an automated decision support system on heart sound abnormality. MAIN KEY FINDINGS • Artifact free subsequence detection is preferable over attempt to reduce the effect of artifacts due to overlap of information content. • DWPT is useful for detection of artifact contaminated subsequences due to its ability to provide for more detailed information of higher frequency components. • DWPT features of subsequence detection can be reused for automatic segmentation of heart sound. • The artifact-free segmented heart sound cycle detection system can work in real-time.

BACKGROUND Cardiac simulators have been developed as an alternative training model in order to improve the cardiac auscultation skills of medical students. The effectiveness of the cardiac simulator's use in cardiac auscultatory training is presently not yet well established. OBJECTIVES The authors aimed to investigate whether the use of a cardiac simulator can improve the auscultation skills of medical students. MATERIAL AND METHODS The students taking the auscultation training on the cardiac simulator were grouped as Group A and the students not taking the auscultation training on the cardiac simulator were grouped as Group B (before). The students in Group B (before) were grouped as Group B (after) after receiving the auscultation training on the cardiac simulator. The percentages of accurate diagnoses for the tested heart murmurs were compared between Group A and Group B (before), and between Group B (before) and Group B (after). RESULTS The rate of making correct diagnoses ...

Auscultation is a widely used technique in clinical activity to diagnose heart diseases. However, heart sounds are difficult to interpret because a) of events with very short temporal onset between them (tens of milliseconds) and b) dominant frequencies that are out of the human audible spectrum. In this paper, we propose a model to segment heart sounds using a semi-hidden Markov model instead of a hidden Markov model. Our model in difference from the state-of-the-art hidden Markov models takes in account the temporal constraints that exist in heart cycles. We experimentally confirm that semi-hidden Markov models are able to recreate the "true" continuous state sequence more accurately than hidden Markov models. We achieved a mean error rate per sample of 0.23.

It has been reported that asynchronous leaflet closure in a bileaflet mechanical valve causes a split in the valve closing sound. We have previously reported that the continuous wavelet transform (CWT) with the Morlet wavelet as modified by Ishikawa (the Morlet wavelet) is the most suitable method among the CWTs for detecting a split in the bileaflet mechanical valve sound because this method can detect the highest frequency signal among the CWT methods with higher time resolution. This is the first article which discusses the acoustic properties of five types of bileaflet valves using the Morlet CWT. Similar behavior of the valve sound split intervals with wide fluctuations over consecutive heartbeats was found to be the common finding for all the bileaflet valves. This result suggests that fluctuation of the split interval proves the normal movement of both leaflets without movement disturbance. The mean differences in the split interval between these bileaflet valves were statist...

Phonocardiogram based auscultation is the most suitable cardiac examination technique for primary health care since heart sound can be captured and analyzed using a smart-phone and a digital stethoscope. The phonocardiogram signal provides, among others, valuable information about valve functioning of the heart. It is well known that many heart problems are associated with valve dysfunctions. Notably, the time differences between valves closure are very critical to diagnose some pathologies. Hence, the need of the correct detection of these instants. Up to now, this research problem represents a serious challenge. This Study takes place in this area of concern and targets to propose a greedy-based two-stage strategy to detect the instants of the heart valves closure. The first stage concerns the dictionary construction from the estimation of the impulse response functions associated to each heart valve. In the second stage, the instants of valves closures are identified by applying the Orthogonal Matching Pursuit algorithm alongside the constructed dictionaries. Simulations on both synthetic and real-life phonocardiogram signals are performed to validate the performance of the proposed two-stage approach in detecting the closure instants of the heart valves.

A methodology is proposed to segment and label the fundamental activities, namely the first and second heart sounds, S1 and S2, of the phonocardiogram (PCG). Information supplementary to the PCG, such as a cue from a synchronously acquired electrocardiogram (ECG), subject-specific prior information, or training examples regarding the activities, is not required by the proposed methodology. A bank of Morlet

Cardiac simulators have been developed as an alternative training model in order to improve the cardiac auscultation skills of medical students. The effectiveness of the cardiac simulator's use in cardiac auscultatory training is presently not yet well established. The authors aimed to investigate whether the use of a cardiac simulator can improve the auscultation skills of medical students. The students taking the auscultation training on the cardiac simulator were grouped as Group A and the students not taking the auscultation training on the cardiac simulator were grouped as Group B (before). The students in Group B (before) were grouped as Group B (after) after receiving the auscultation training on the cardiac simulator. The percentages of accurate diagnoses for the tested heart murmurs were compared between Group A and Group B (before), and between Group B (before) and Group B (after). The rate of making correct diagnoses of normal heart sounds was similar in all the group...

This paper is concerned with a synthesis study of the fast Fourier transform (FFT), the short-time Fourier transform (STFT), the Wigner distribution (WD) and the wavelet transform (WT) in analysing the phonocardiogram signal (PCG). It is shown that these transforms provide enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time–frequency

The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of pathological events. While threshold-based segmentation methods have shown modest success, probabilistic models, such as hidden Markov models, have recently been shown to surpass the capabilities of previous methods. Segmentation performance is further improved when a priori information about the expected duration of the states is incorporated into the model, such as in a hidden semi-Markov model (HSMM). This article addresses the problem of the accurate segmentation of the first and second heart sound within noisy, real-world PCG recordings using a HSMM, extended with the use of logistic regression for emission probability estimation. In addition, we implement a modified Viterbi algorithm for decoding the most-likely sequence of states, ...

A system for FM radiotelemetry of heart and breath sounds is described. Patients' heart and breath sounds are detected by oesophageal or precordial stethoscopes. The anaesthetist, carrying a portable radio receiver, is then free to move around theatre while listening to these sounds through headphones or an ear-piece. The FM telemetry system has also been used to assist patient monitoring in noisy environments such as ambulances.