Hidden Markov model (HMM) is known to be one of the most powerful methods in the acoustic modeling of heart sound signals. Conventionally, we usually use a fixed number of states for each HMM. However, due to the various types of the heart sound signals, it seems that more accurate acoustic modeling is possible by varying the number of states in the HMM depending on the signal types to be modeled. In this paper, we propose to assign different number of states to the HMM for better acoustic modeling and consequently, improving the classification performance of the heart sound signals. Compared with when fixing the number of states, the proposed approach has shown some performance improvement in the classification experiments on various types of heart sound signals.
Acoustics ; Heart ; Heart Sounds

Recently, hidden Markov models (HMMs) have been found to be very effective in classifying heart sound signals. For the classification based on the HMM, the continuous cyclic heart sound signal needs to be manually segmented to obtain isolated cycles of the signal. However, the manual segmentation will be practically inadequate in real environments. Although, there have been some research efforts for the automatic segmentation, the segmentation errors seem to be inevitable and will result in performance degradation in the classification. To solve the problem of the segmentation, we propose to use the ergodic HMM for the classification of the continuous heart sound signal. In the classification experiments, the proposed method performed successfully with an accuracy of about 99(%) requiring no segmentation information.
Classification* ; Heart Sounds* ; Heart*

Strange attractor can be constructed from time series data such as heart sound. In the areas of the recognition and diagnosis of abnormal heart sounds, signal presentation method is very useful because good features can be detected from good presentation. This paper examines efficiency in diagnosing abnormal heart sounds of the two different methods for constructing attractor. Nine different heart sounds from typical clinical conditions were used for this study. The first method was constructing attractors using original heart sounds, and the second was modifying the original sounds by autocorrelation and they were then applied to the orignal sounds as to cross correlation checks. Attractors could be constructed using signals generated by these methods, and values of fractal dimensions would then be calculated which has been a well known method to measure characteristics of attractors. The results showed that the second method appeared to provide more efficient way to correctly classify abnormal heart sounds.
Diagnosis ; Fractals* ; Heart Sounds* ; Heart*

Strange attractor can be constructed from time series data such as heart sound. In the area of the recognition and diagnosis problem, signal presentation method is very important because good features can be detected from good presentation. This paper discusses a way to extract a cycle from strange attractor and introduce new attractor construction method using autocorrelation value of the heart rate. The result shows well-formed attractor and good ability for extraction features. Largest Lyapunov Exponent is used to check whether the attractors provide distinguish abilities among different types of heart rate. The result shows good points that can be applied to some areas of human signal processing.
Diagnosis ; Heart Rate* ; Heart Sounds ; Heart* ; Humans

This research is about embodiment of system to support auscultation education more effectively. Cardiac sound data that is stored to PC made many learner deliver by wireless system. For this system we developed a special radio transmitter receiver and a program to manage and remake data. Because of selecting radio system, there is no limitation of establishment and education place. also through web server database and update of data are available. For this reason we can add cardiac sound data newly in education. In case of utilizing existent electron stethoscope in auscultation education, the biggest demerit is that do not deliver sense of sound of actuality stethoscope properly. But radio receiving apparatus that we developed is no difference with sense of sound of cardiac through actuality stethoscope and did so that heighten effect of auscultation education.
Auscultation ; Education ; Heart Sounds ; Stethoscopes

This research is about embodiment of system to support auscultation education more effectively. Cardiac sound data that is stored to PC made many learner deliver by wireless system. For this system we developed a special radio transmitter receiver and a program to manage and remake data. Because of selecting radio system, there is no limitation of establishment and education place. also through web server database and update of data are available. For this reason we can add cardiac sound data newly in education. In case of utilizing existent electron stethoscope in auscultation education, the biggest demerit is that do not deliver sense of sound of actuality stethoscope properly. But radio receiving apparatus that we developed is no difference with sense of sound of cardiac through actuality stethoscope and did so that heighten effect of auscultation education.
Auscultation ; Education ; Heart Sounds ; Stethoscopes

This paper is about the system using a wireless stethoscope to analysis the FFT and the time-frequency for a heart sound and to manage the collected data for a web-based system. We reformed a wireless stethoscope, connected to PC interface and added the analysis function. In result, we combined merits of an existed wireless system to be convenient for measuring the heart sound and to be available for many listener to ausculate the heart sound simultaneously, and an existed wired system to supply the various analysis functions. The heart sounds data was made into the database to search or refer to the patient data. It is possible to search and refer by the web-browser to the recorded heart sound file, the analyzed file by FFT and the STFT time-frequency method.
Heart Sounds* ; Heart* ; Humans ; Statistics as Topic* ; Stethoscopes*

Segmentation of fundamental heart sounds–S1 and S2 is important for automated monitoring of cardiac activity including diagnosis of the heart diseases. This pa-per proposes a novel hybrid method for S1 and S2 heart sound segmentation using group sparsity denoising and variation mode decomposition (VMD) technique. In the proposed method, the measured phonocardiogram (PCG) signals are denoised using group sparsity algorithm by exploiting the group sparse (GS) property of PCG signals. The denoised GS-PCG signals are then decomposed into subsequent modes with specific spectral characteristics using VMD algorithm. The appropriate mode for further processing is selected based on mode central frequencies and mode energy. It is then followed by the extraction of Hilbert envelope (HEnv) and a thresholding on the selected mode to segment S1 and S2 heart sounds. The performance advantage of the proposed method is verified using PCG signals from benchmark databases namely eGeneralMedical, Littmann, Washington, and Michigan. The proposed hybrid algorithm has achieved a sensitivity of 100%, positive predictivity of 98%, accuracy of 98% and detection error rate of 1.5%. The promising results obtained suggest that proposed approach can be considered for automated heart sound segmentation.
Benchmarking ; Diagnosis ; Heart Diseases ; Heart Sounds ; Heart ; Methods ; Michigan ; Washington

We proposed a research of a heart sound envelope extraction system in this paper. The system was implemented on LabVIEW based on the Hilbert-Huang transform (HHT). We firstly used the sound card to collect the heart sound, and then implemented the complete system program of signal acquisition, pretreatment and envelope extraction on LabVIEW based on the theory of HHT. Finally, we used a case to prove that the system could collect heart sound, preprocess and extract the envelope easily. The system was better to retain and show the characteristics of heart sound envelope, and its program and methods were important to other researches, such as those on the vibration and voice, etc.
Heart Sounds ; Humans ; Signal Processing, Computer-Assisted

This article describes how to develop a practical new type of digital heart sound signal detection device that can achieve quantitative and accurate capture of human heart sounds and records. According to the mechanism and characteristics of the heart sound signal, the goal of this system design is to set the platform. The system uses a contact-type piezoelectric film microphone, which can effectively pick up the effective frequency band of the heart sound, then amplify and filter the collected original signal, and perform preliminary verification on the system to obtain the desired heart sound signal.
Heart Sounds ; Humans ; Signal Processing, Computer-Assisted