Yongwan Park

Separation of Heart Sound Signal from Noise in Joint Cycle Frequency–Time–Frequency Domains Based on Fuzzy Detection

Noise is generally unavoidable during recordings of heart sound signal. Therefore, noise reduction is one of the important preprocesses in the analysis of heart sound signal. This was achieved in joint cycle frequency-time-frequency domains in this study. Heart sound signal was decomposed into components (called atoms) characterized by time delay, frequency, amplitude, time width, and phase. It was discovered that atoms of heart sound signal congregate in the joint domains. On the other hand, atoms of noise were dispersed. The atoms of heart sound signal could, therefore, be separated from the atoms of noise based on fuzzy detection. In a practical experiment, heart sound signal was successfully separated from lung sounds and disturbances due to chest motion. Computer simulations for various clinical heart sound signals were also used to evaluate the performance of the proposed noise reduction. It was shown that heart sound signal can be reconstructed from simulated complex noise (perhaps non-Gaussian, nonstationary, and colored). The proposed noise reduction can recover variations in the both waveform and time delay of heart sound signal during the reconstruction. Correlation coefficient and normalized residue were used to indicate the closeness of the reconstructed and noise-free heart sound signal. Correlation coefficient may exceed 0.90 and normalized residue may be around 0.10 in 0-dB noise environment, even if the phonocardiogram signal covers only ten cardiac cycles.

Segmentation of heart sounds based on dynamic clustering

Abstract The heart sound signal is first separated into cycles, where the cycle detection is based on an instantaneous cycle frequency. The heart sound data of one cardiac cycle can be decomposed into a number of atoms characterized by timing delay, frequency, amplitude, time width and phase. To segment heart sounds, we made a hypothesis that the atoms of a heart sound congregate as a cluster in time–frequency domains. We propose an atom density function to indicate clusters. To suppress clusters of murmurs and noise, weighted density function by atom energy is further proposed to improve the segmentation of heart sounds. Therefore, heart sounds are indicated by the hybrid analysis of clustering and medical knowledge. The segmentation scheme is automatic and no reference signal is needed. Twenty-six subjects, including 3 normal and 23 abnormal subjects, were tested for heart sound signals in various clinical cases. Our statistics show that the segmentation was successful for signals collected from normal subjects and patients with moderate murmurs.

A TOA-AOA-based NLOS error mitigation method for location estimation

This paper proposes a geometric method to locate a mobile station (MS) in a mobile cellular network when both the range and angle measurements are corrupted by non-line-of-sight (NLOS) errors. The MS location is restricted to an enclosed region by geometric constraints from the temporal-spatial characteristics of the radio propagation channel. A closed-form equation of the MS position, time of arrival (TOA), angle of arrival (AOA), and angle spread is provided. The solution space of the equation is very large because the angle spreads are random variables in nature. A constrained objective function is constructed to further limit the MS position. A Lagrange multiplier-based solution and a numerical solution are proposed to resolve the MS position. The estimation quality of the estimator in term of biased or unbiased is discussed. The scale factors, which may be used to evaluate NLOS propagation level, can be estimated by the proposed method. AOA seen at base stations may be corrected to some degree. The performance comparisons among the proposed method and other hybrid location methods are investigated on different NLOS error models and with two scenarios of cell layout. It is found that the proposed method can deal with NLOS error effectively, and it is attractive for location estimation in cellular networks.

Heart sound cancellation from lung sound record using cyclostationarity

From the mechanism of heart sound generation, it is known that heart sounds are cyclic following the frequency of the heartbeat. This paper proposes a short-time cyclic frequency spectrum to calculate the instantaneous cycle frequency (ICF) of heart sounds as an estimation of the frequency of the heartbeat. Heart sounds in a lung sound record are detected with the assistance of ICF. Lung sounds (LSs) are recovered by removing heart sounds from the LS record. An LS record is the only input signal source; no other reference signal is necessary. Evaluation by visual inspection, auditory listening and spectral analysis all show that heart sounds are successfully cancelled without hampering the main components of lung sounds.

NLOS mitigation for TOA location based on a modified deterministic model

Wireless location becomes difficult due to contamination of measured time-of-arrival (TOA) caused by non-line-of-sight. In this letter, TOA measurements seen at base stations are adjusted by scale factors, and a modified deterministic model is built. An effective numerical solution is proposed to resolve the scale factors and mobile position. A simulation comparison of four algorithms indicates that the proposed algorithm outperforms the other three algorithms.

A macro-cell statistical location estimation method for TD-SCDMA networks

TD-SCDMA is an innovative wireless radio standard for a physical layer 3G air interface, approved as one 3GPP standards by ITU in May 2000. Based on the unique advantages of the physical layer, this paper investigates network-based methods to obtain the angle of arrival (AOA) and time of arrival (TOA) via subspace tracking and channel impulsive estimation. By incorporating the spatial-temporal channel of radio propagation, this paper proposes a unified objective function to extract mobile location information from the statistical distribution of AOA and TOA data in a TD-SCDMA cellular network. The proposed method may estimate mobile position even using one base station. The location performances are analyzed in detail in macro-cell scenarios. This method can be easily extended to other networks except TD-SCDMA, provided that the statistical information of the local radio propagation channel is available as pre-knowledge. Simulation comparing several hybrid location methods in two macro-cell scenarios show that the proposed method has a high accuracy; but it has a relatively high complexity.

Fetal Heart Rate Monitoring from Phonocardiograph Signal Using Repetition Frequency of Heart Sounds

As a passive, harmless, and low-cost diagnosis tool, fetal heart rate FHR monitoring based on fetal phonocardiography fPCG signal is alternative to ultrasonographic cardiotocography. Previous fPCG-based methods commonly relied on the time difference of detected heart sound bursts. However, the performance is unavoidable to degrade due to missed heart sounds in very low signal-to-noise ratio environments. This paper proposes a FHR monitoring method using repetition frequency of heart sounds. The proposed method can track time-varying heart rate without both heart sound burst identification and denoising. The average accuracy rate comparison to benchmark is 88.3% as the SNR ranges from −4.4u2009dB to −26.7u2009dB.

Phonocardiogram signal compression using sound repetition and vector quantization

BACKGROUNDnA phonocardiogram (PCG) signal can be recorded for long-term heart monitoring. A huge amount of data is produced if the time of a recording is as long as days or weeks. It is necessary to compress the PCG signal to reduce storage space in a record and play system. In another situation, the PCG signal is transmitted to a remote health care center for automatic analysis in telemedicine. Compression of the PCG signal in that situation is necessary as a means for reducing the amount of data to be transmitted. Since heart beats are of a cyclical nature, compression can make use of the similarities in adjacent cycles by eliminating repetitive elements as redundant. This study proposes a new compression method that takes advantage of these repetitions.nnnMETHODSnData compression proceeds in two stages, a training stage followed by the compression as such. In the training stage, a section of the PCG signal is selected and its sounds and murmurs (if any) decomposed into time-frequency components. Basic components are extracted from these by clustering and collected to form a dictionary that allows the generative reconstruction and retrieval of any heart sound or murmur. In the compression stage, the heart sounds and murmurs are reconstructed from the basic components stored in the dictionary. Compression is made possible because only the times of occurrence and the dictionary indices of the basic components need to be stored, which greatly reduces the number of bits required to represent heart sounds and murmurs. The residual that cannot be reconstructed in this manner appears as a random sequence and is further compressed by vector quantization. What we propose are quick search parameters for this vector quantization.nnnRESULTSnFor normal PCG signals the compression ratio ranges from 20 to 149, for signals with median murmurs it ranges from 14 to 35, and for those with heavy murmurs, from 8 to 20, subject to a degree of distortion of ~5% (in percent root-mean-square difference) and a sampling frequency of 4kHz.nnnDISCUSSIONnWe discuss the selection of the training signal and the contribution of vector quantization. Performance comparisons between the method proposed in this study and existing methods are conducted by computer simulations.nnnCONCLUSIONSnWhen recording and compressing cyclical sounds, any repetitive components can be removed as redundant. The redundancies in the residual can be reduced by vector quantization. The method proposed in this study achieves a better performance than existing methods.

Reinvestigation of the relationship between the amplitude of the first heart sound to cardiac dynamics

The relationships between the amplitude of the first heart sound (S1) and the rising rate of left ventricular pressure (LVP) concluded in previous studies were not consistent. Some researchers believed the relationship was positively linear; others stated the relationship was only positively correlated. To further investigate this relationship, this study simultaneously sampled the external phonocardiogram, electrocardiogram, and intracardiac pressure in the left ventricle in three anesthetized dogs, while invoking wide hemodynamic changes using various doses of epinephrine. The relationship between the maximum amplitude of S1 and the maximum rising rate of LVP and the relationship between the amplitude of dominant peaks/valleys and the corresponding rising rate of LVP were examined by linear, quadratic, cubic, and exponential models. The results showed that the relationships are best fit by nonlinear exponential models.

Modeling of heart sound morphology and analysis of the morphological variations induced by respiration

In this study, each peak/valley of a heart sound was modeled by a Gaussian curve and characterized by amplitude, timing, and supporting width. This model was applied to analyze the morphological variations induced by respiration in 12 subjects. It was observed that the morphology exhibited regular behaviors with respiration. The amplitude of the prominent peaks and valleys of S2 (the second heart sound) were commonly attenuated during expiration and were accentuated during inspiration whereas no consistent observations were obtained for S1 (the first heart sound). The supporting width of S1 commonly decreased with expiration and increased with inspiration whereas the supporting width of S2 displayed no significant changes during respiration. For all subjects, the delay of S1 increased during inspiration and decreased during expiration. However, the delay of the aortic component increased during expiration and decreased during inspiration. The pulmonary component of S2 was observed in 7 of 12 subjects, and the delay was opposite to that of the aortic component. The opposing delays yielded a splitting between the two components of S2 that increased during inspiration and decreased during expiration. The delay pattern was the most consistent observation in all subjects. These results suggest that a quantitative analysis of morphological variations, particularly the delay pattern, could be used as a non-invasive continuous monitoring method of hemodynamic change during respiratory cycles.