Automated Assessment of the Quality of Phonocardographic Recordings through Signal-to-Noise Ratio for Home Monitoring Applications.

The signal quality limits the applicability of phonocardiography at the patients' domicile. This work proposes the signal-to-noise ratio of the recorded signal as its main quality metrics. Moreover, we define the minimum acceptable values of the signal-to-noise ratio that warrantee an accuracy of the derived parameters acceptable in clinics. We considered 25 original heart sounds recordings, which we corrupted by adding noise to decrease their signal-to-noise ratio. We found that a signal-to-noise ratio equal to or higher than 14 dB warrants an uncertainty of the estimate of the valve closure latencies below 1 ms. This accuracy is higher than that required by most clinical applications. We validated the proposed method against a public database, obtaining results comparable to those obtained on our sample population. In conclusion, we defined (a) the signal-to-noise ratio of the phonocardiographic signal as the preferred metric to evaluate its quality and (b) the minimum values of the signal-to-noise ratio required to obtain an uncertainty of the latency of heart sound components compatible with clinical applications. We believe these results are crucial for the development of home monitoring systems aimed at preventing acute episodes of heart failure and that can be safely operated by naïve users.

Design, assembly and validation of a low-cost, high-fidelity simulator for heart exploration.

INTRODUCTION: Heart exploration is an essential clinical competence that requires continuous training and exposure. Low availability and accessibility to patients with heart disease constitutes a barrier to acquiring this competence. Inadequate cardiac auscultation skills in medical students, residents, and graduate physicians have been documented. OBJECTIVE: To develop and validate a low-cost, high-fidelity simulator for heart exploration. METHODS: A low-cost, high-fidelity heart examination simulator capable of reproducing normal cardiac sounds was designed and developed. Subsequently, the simulator was validated by a group of experts who gave their opinion according to a Likert scale. RESULTS: Ninety-four percent agreed that the simulator motivates the learning of heart exploration, and 92 % considered it to be a realistic model; 91 % considered that the simulator is an attractive tool to reinforce learning and 98 % recommended its further use. CONCLUSIONS: The use of the simulator facilitates the acquisition of skills and stimulates learning in the student, which can be attributed to repeated practice, longer exposure time and cognitive interaction.

INTRODUCCIÓN: La exploración cardiaca es una competencia clínica fundamental que requiere exposición o entrenamiento continuo. La baja disponibilidad y accesibilidad de pacientes con patología cardiaca constituye una barrera para adquirir esta competencia. Se han documentado inadecuadas habilidades de auscultación cardiaca en estudiantes de medicina, residentes y médicos graduados. OBJETIVO: Elaborar y validar un simulador de alta fidelidad y bajo costo para exploración cardiaca. MÉTODOS: Se diseñó y elaboró un simulador para exploración cardiaca, realista y de bajo costo capaz de reproducir ruidos cardiacos normales. Posteriormente se realizó la validación del simulador por un grupo de expertos que emitieron su opinión de acuerdo con una escala tipo Likert. RESULTADOS: El 94 % afirmó que el simulador motiva el aprendizaje de la exploración cardiaca y 92 % lo consideró un modelo realista; 91 % consideró que el simulador es una herramienta atractiva para fortalecer el aprendizaje y 98 % recomendó seguir utilizándolo. CONCLUSIONES: El uso del simulador facilita la adquisición de competencias y estimula el aprendizaje en el estudiante, lo cual puede ser atribuido a la práctica deliberada, a un mayor tiempo de exposición y a la interacción cognitiva.

Diseño, elaboración y validación de un simulador realista y de bajo costo para exploración cardiaca / Design, assembly and validation of a low-cost, high-fidelity simulator for heart exploration

Resumen Introducción: La exploración cardiaca es una competencia clínica fundamental que requiere exposición o entrenamiento continuo. La baja disponibilidad y accesibilidad de pacientes con patología cardiaca constituye una barrera para adquirir esta competencia. Se han documentado inadecuadas habilidades de auscultación cardiaca en estudiantes de medicina, residentes y médicos graduados. Objetivo: Elaborar y validar un simulador de alta fidelidad y bajo costo para exploración cardiaca. Métodos: Se diseñó y elaboró un simulador para exploración cardiaca, realista y de bajo costo capaz de reproducir ruidos cardiacos normales. Posteriormente se realizó la validación del simulador por un grupo de expertos que emitieron su opinión de acuerdo con una escala tipo Likert. Resultados: El 94 % afirmó que el simulador motiva el aprendizaje de la exploración cardiaca y 92 % lo consideró un modelo realista; 91 % consideró que el simulador es una herramienta atractiva para fortalecer el aprendizaje y 98 % recomendó seguir utilizándolo. Conclusiones: El uso del simulador facilita la adquisición de competencias y estimula el aprendizaje en el estudiante, lo cual puede ser atribuido a la práctica deliberada, a un mayor tiempo de exposición y a la interacción cognitiva.

Abstract Introduction: Heart exploration is an essential clinical competence that requires continuous training and exposure. Low availability and accessibility to patients with heart disease constitutes a barrier to acquiring this competence. Inadequate cardiac auscultation skills in medical students, residents, and graduate physicians have been documented. Objective: To develop and validate a low-cost, high-fidelity simulator for heart exploration. Methods: A low-cost, high-fidelity heart examination simulator capable of reproducing normal cardiac sounds was designed and developed. Subsequently, the simulator was validated by a group of experts who gave their opinion according to a Likert scale. Results: Ninety-four percent agreed that the simulator motivates the learning of heart exploration, and 92 % considered it to be a realistic model; 91 % considered that the simulator is an attractive tool to reinforce learning and 98 % recommended its further use. Conclusions: The use of the simulator facilitates the acquisition of skills and stimulates learning in the student, which can be attributed to repeated practice, longer exposure time and cognitive interaction.

Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network.

We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal heart sounds. A hidden Markov model is used to find the position of each cardiac cycle in the heart sound signal and determine the exact position of the four periods of S1, S2, systole, and diastole. Then the one-dimensional cardiac cycle signal was converted into a two-dimensional time-frequency picture using the MFSWT. Finally, two CNN models are trained using the aforementioned pictures. We combine two CNN models using sample entropy (SampEn) to determine which model is used to classify the heart sound signal. We evaluated our model on the heart sound public dataset provided by the PhysioNet Computing in Cardiology Challenge 2016. Experimental classification performance from a 10-fold cross-validation indicated that sensitivity (Se), specificity (Sp) and mean accuracy (MAcc) were 0.95, 0.93, and 0.94, respectively. The results showed the proposed method can classify normal and abnormal heart sounds with efficiency and high accuracy. Graphical abstract Block diagram of heart sound classification.

Coronary artery disease risk reclassification by a new acoustic-based score.

To determine the potential of a non-invasive acoustic device (CADScor®System) to reclassify patients with intermediate pre-test probability (PTP) and clinically suspected stable coronary artery disease (CAD) into a low probability group thereby ruling out significant CAD. Audio recordings and clinical data from three studies were collected in a single database. In all studies, patients with a coronary CT angiography indicating CAD were referred to coronary angiography. Audio recordings of heart sounds were processed to construct a CAD-score. PTP was calculated using the updated Diamond-Forrester score and patients were classified according to the current ESC guidelines for stable CAD: low < 15%, intermediate 15-85% and high > 85% PTP. Intermediate PTP patients were re-classified to low probability if the CAD-score was ≤ 20. Of 2245 patients, 212 (9.4%) had significant CAD confirmed by coronary angiography ( ≥ 50% diameter stenosis). The average CAD-score was higher in patients with significant CAD (38.4 ± 13.9) compared to the remaining patients (25.1 ± 13.8; p < 0.001). The reclassification increased the proportion of low PTP patients from 13.6% to 41.8%, reducing the proportion of intermediate PTP patients from 83.4% to 55.2%. Before reclassification 7 (3.1%) low PTP patients had CAD, whereas post-reclassification this number increased to 28 (4.0%) (p = 0.52). The net reclassification index was 0.209. Utilization of a low-cost acoustic device in patients with intermediate PTP could potentially reduce the number of patients referred for further testing, without a significant increase in the false negative rate, and thus improve the cost-effectiveness for patients with suspected stable CAD.

Deep Convolutional Neural Networks for Heart Sound Segmentation.

This paper studies the use of deep convolutional neural networks to segment heart sounds into their main components. The proposed methods are based on the adoption of a deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. Different temporal modeling schemes are applied to the output of the proposed neural network, which induce the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole). In particular, convolutional neural networks are used in conjunction with underlying hidden Markov models and hidden semi-Markov models to infer emission distributions. The proposed approaches are tested on heart sound signals from the publicly available PhysioNet dataset, and they are shown to outperform current state-of-the-art segmentation methods by achieving an average sensitivity of 93.9% and an average positive predictive value of 94% in detecting S1 and S2 sounds.

Adaptive Sojourn Time HSMM for Heart Sound Segmentation.

Heart sounds are difficult to interpret due to events with very short temporal onset between them (tens of milliseconds) and dominant frequencies that are out of the human audible spectrum. Computer-assisted decision systems may help but they require robust signal processing algorithms. In this paper, we propose a new algorithm for heart sound segmentation using a hidden semi-Markov model. The proposed algorithm infers more suitable sojourn time parameters than those currently suggested by the state of the art, through a maximum likelihood approach. We test our approach over three different datasets, including the publicly available PhysioNet and Pascal datasets. We also release a pediatric dataset composed of 29 heart sounds. In contrast with any other dataset available online, the annotations of the heart sounds in the released dataset contain information about the beginning and the ending of each heart sound event. Annotations were made by two cardiopulmonologists. The proposed algorithm is compared with the current state of the art. The results show a significant increase in segmentation performance, regardless the dataset or the methodology presented. For example, when using the PhysioNet dataset to train and to evaluate the HSMMs, our algorithm achieved average an F-score of [Formula: see text] compared to [Formula: see text] achieved by the algorithm described in [D.B. Springer, L. Tarassenko, and G. D. Clifford, "Logistic regressionHSMM-based heart sound segmentation," IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 822-832, 2016]. In this sense, the proposed approach to adapt sojourn time parameters represents an effective solution for heart sound segmentation problems, even when the training data does not perfectly express the variability of the testing data.

Assessment of systolic and diastolic function in clinically healthy horses using ambulatory acoustic cardiography.

BACKGROUND: Assessment of cardiac electromechanical function in horses requires training, experience and specialised equipment and does not allow continuous monitoring over time. OBJECTIVES: The objective of this study was to establish the use of an acoustic ECG monitor (Audicor® ) in healthy horses. It provides noninvasive, examiner-independent, continuous analyses combining ECG and phonocardiography to calculate indices of cardiac mechanical activity and haemodynamics. Device usability was investigated, reference intervals calculated and reproducibility of analyses assessed. STUDY DESIGN: Prospective descriptive study. METHODS: Continuous overnight recordings were obtained in 123 healthy horses. ECG and acoustic cardiography analyses were performed. Electromechanical activating time (EMAT), rate-corrected EMATc, left ventricular systolic time (LVST), rate-corrected LVSTc and intensity and persistence of the third and fourth heart sound (S3, S4) were reported. Associations with age and reproducibility of analyses were assessed. RESULTS: Audicor® recordings of diagnostic quality were obtained in 116 horses, with an artefact-free recording time of 1:08-14:03 h (mean 10:21 h). 44.8% of the horses had atrial premature complexes (up to 0.18% of analysed beats), 4.3% had ventricular premature complexes (up to 0.021% of analysed beats). Reference intervals for acoustic cardiography variables were reported. S3 was significantly more often graded ≥5 (scale 0-10) in younger compared to older horses (P = 0.0036, R2  = 0.072). The between-day coefficient of variation ranged from 2.5 to 7.7% for EMAT, EMATc, LVST and LVSTc. MAIN LIMITATIONS: Audicor® algorithms are based on human databases. Horses were deemed clinically healthy without advanced diagnostics. Some data were lost because of technical difficulties, artefacts and noises. CONCLUSIONS: Overnight Audicor® recordings are feasible in horses. Combining ambulatory ECG and phonocardiography allows noninvasive, continuous assessment of variables representing systolic and diastolic cardiac function. ECG rhythm analyses require over-reading by a specialist, but acoustic cardiography variables are based on automated algorithms independent of examiner input. Further studies are required to establish the clinical value of acoustic cardiography in horses.

AcCorps: A low-cost 3D printed stethoscope for fetal phonocardiography.

The analysis of fetal heart rate provides valuable information regarding the fetus wellbeing. Fetal phonocardiography is a low-cost and passive method allowing the acquisition of fetal heart rate by recording acoustic vibrations on the mother's abdomen. However, most of available stethoscopes are not optimized for a robust acquisition of fetal heart sound. In this publication, we investigated a new design of low-cost and 3D printed stethoscope. This device was optimized to provide an acoustic amplification especially in the low-frequency band which corresponds to the fetal heart sounds. This device was tested i) in silico, ii) on a test bench and iii) on 5 pregnant volunteers.

Analysis of PCG signals using quality assessment and homomorphic filters for localization and classification of heart sounds.

BACKGROUND AND OBJECTIVE: Accurate localization of heart beats in phonocardiogram (PCG) signal is very crucial for correct segmentation and classification of heart sounds into S1 and S2. This task becomes challenging due to inclusion of noise in acquisition process owing to number of different factors. In this paper we propose a system for heart sound localization and classification into S1 and S2. The proposed system introduces the concept of quality assessment before localization, feature extraction and classification of heart sounds. METHODS: The signal quality is assessed by predefined criteria based upon number of peaks and zero crossing of PCG signal. Once quality assessment is performed, then heart beats within PCG signal are localized, which is done by envelope extraction using homomorphic envelogram and finding prominent peaks. In order to classify localized peaks into S1 and S2, temporal and time-frequency based statistical features have been used. Support Vector Machine using radial basis function kernel is used for classification of heart beats into S1 and S2 based upon extracted features. The performance of the proposed system is evaluated using Accuracy, Sensitivity, Specificity, F-measure and Total Error. The dataset provided by PASCAL classifying heart sound challenge is used for testing. RESULTS: Performance of system is significantly improved by quality assessment. Results shows that proposed Localization algorithm achieves accuracy up to 97% and generates smallest total average error among top 3 challenge participants. The classification algorithm achieves accuracy up to 91%. CONCLUSION: The system provides firm foundation for the detection of normal and abnormal heart sounds for cardiovascular disease detection.

Radar-Based Heart Sound Detection.

This paper introduces heart sound detection by radar systems, which enables touch-free and continuous monitoring of heart sounds. The proposed measurement principle entails two enhancements in modern vital sign monitoring. First, common touch-based auscultation with a phonocardiograph can be simplified by using biomedical radar systems. Second, detecting heart sounds offers a further feasibility in radar-based heartbeat monitoring. To analyse the performance of the proposed measurement principle, 9930 seconds of eleven persons-under-tests' vital signs were acquired and stored in a database using multiple, synchronised sensors: a continuous wave radar system, a phonocardiograph (PCG), an electrocardiograph (ECG), and a temperature-based respiration sensor. A hidden semi-Markov model is utilised to detect the heart sounds in the phonocardiograph and radar data and additionally, an advanced template matching (ATM) algorithm is used for state-of-the-art radar-based heartbeat detection. The feasibility of the proposed measurement principle is shown by a morphology analysis between the data acquired by radar and PCG for the dominant heart sounds S1 and S2: The correlation is 82.97 ± 11.15% for 5274 used occurrences of S1 and 80.72 ± 12.16% for 5277 used occurrences of S2. The performance of the proposed detection method is evaluated by comparing the F-scores for radar and PCG-based heart sound detection with ECG as reference: Achieving an F1 value of 92.22 ± 2.07%, the radar system approximates the score of 94.15 ± 1.61% for the PCG. The accuracy regarding the detection timing of heartbeat occurrences is analysed by means of the root-mean-square error: In comparison to the ATM algorithm (144.9 ms) and the PCG-based variant (59.4 ms), the proposed method has the lowest error value (44.2 ms). Based on these results, utilising the detected heart sounds considerably improves radar-based heartbeat monitoring, while the achieved performance is also competitive to phonocardiography.

Structural Risk Evaluation of a Deep Neural Network and a Markov Model in Extracting Medical Information from Phonocardiography.

This paper presents a method for exploring structural risk of any artificial intelligence-based method in bioinformatics, the A-Test method. This method provides a way to not only quantitate the structural risk associated with a classification method, but provides a graphical representation to compare the learning capacity of different classification methods. Two different methods, Deep Time Growing Neural Network (DTGNN) and Hidden Markov Model (HMM), are selected as two classification methods for comparison. Time series of heart sound signals are employed as the case study where the classifiers are trained to learn the disease-related changes. Results showed that the DTGNN offers a superior performance both in terms of the capacity and the structural risk. The A-Test method can be especially employed in comparing the learning methods with small data size.

Pattern discovery and similarity assessment for robust heart sound segmentation.

Heart Sound Segmentation plays a fundamental role in pathology detection in Phonocardiogram (PCG) signals. This matter of study has been widely studied in the past decades, however the majority of algorithms' results correspond only to small databases, composed by only quality signals or signals specific to one acquisition system. In this work we proposed a robust segmentation algorithm integrated with clinical information, based on a pattern recognition approach for segmentation of the fundamental heart sounds, which is validated in several databases from different countries and with different acquisition instrumentations. The database comprises a total of 3153 recordings from 764 patients with a variety of pathological conditions. The general results were 95% and 96% of sensitivity and positive predictivity, respectively. Based on the results the algorithm is able to perform with accuracy maintaining generalization capabilities.

Pattern discovery and similarity assessment for robust Heart Sound Segmentation.

Heart Sound Segmentation plays a fundamental role in pathology detection in Phonocardiogram (PCG) signals. This matter of study has been widely studied in the past decades, however the majority of algorithms' results correspond only to small databases, composed by only quality signals or signals specific to one acquisition system. In this work we proposed a robust segmentation algorithm integrated with clinical information, based on a pattern recognition approach for segmentation of the fundamental heart sounds, which is validated in several databases from different countries and with different acquisition instrumentations. The database comprises a total of 3153 recordings from 764 patients with a variety of pathological conditions. The general results were 95% and 96% of sensitivity and positive predictivity, respectively. Based on the results the algorithm is able to perform with accuracy maintaining generalization capabilities.

Performance of an open-source heart sound segmentation algorithm on eight independent databases.

OBJECTIVE: Heart sound segmentation is a prerequisite step for the automatic analysis of heart sound signals, facilitating the subsequent identification and classification of pathological events. Recently, hidden Markov model-based algorithms have received increased interest due to their robustness in processing noisy recordings. In this study we aim to evaluate the performance of the recently published logistic regression based hidden semi-Markov model (HSMM) heart sound segmentation method, by using a wider variety of independently acquired data of varying quality. APPROACH: Firstly, we constructed a systematic evaluation scheme based on a new collection of heart sound databases, which we assembled for the PhysioNet/CinC Challenge 2016. This collection includes a total of more than 120 000 s of heart sounds recorded from 1297 subjects (including both healthy subjects and cardiovascular patients) and comprises eight independent heart sound databases sourced from multiple independent research groups around the world. Then, the HSMM-based segmentation method was evaluated using the assembled eight databases. The common evaluation metrics of sensitivity, specificity, accuracy, as well as the [Formula: see text] measure were used. In addition, the effect of varying the tolerance window for determining a correct segmentation was evaluated. MAIN RESULTS: The results confirm the high accuracy of the HSMM-based algorithm on a separate test dataset comprised of 102 306 heart sounds. An average [Formula: see text] score of 98.5% for segmenting S1 and systole intervals and 97.2% for segmenting S2 and diastole intervals were observed. The [Formula: see text] score was shown to increases with an increases in the tolerance window size, as expected. SIGNIFICANCE: The high segmentation accuracy of the HSMM-based algorithm on a large database confirmed the algorithm's effectiveness. The described evaluation framework, combined with the largest collection of open access heart sound data, provides essential resources for evaluators who need to test their algorithms with realistic data and share reproducible results.

Automated signal quality assessment of mobile phone-recorded heart sound signals.

Mobile phones, due to their audio processing capabilities, have the potential to facilitate the diagnosis of heart disease through automated auscultation. However, such a platform is likely to be used by non-experts, and hence, it is essential that such a device is able to automatically differentiate poor quality from diagnostically useful recordings since non-experts are more likely to make poor-quality recordings. This paper investigates the automated signal quality assessment of heart sound recordings performed using both mobile phone-based and commercial medical-grade electronic stethoscopes. The recordings, each 60 s long, were taken from 151 random adult individuals with varying diagnoses referred to a cardiac clinic and were professionally annotated by five experts. A mean voting procedure was used to compute a final quality label for each recording. Nine signal quality indices were defined and calculated for each recording. A logistic regression model for classifying binary quality was then trained and tested. The inter-rater agreement level for the stethoscope and mobile phone recordings was measured using Conger's kappa for multiclass sets and found to be 0.24 and 0.54, respectively. One-third of all the mobile phone-recorded phonocardiogram (PCG) signals were found to be of sufficient quality for analysis. The classifier was able to distinguish good- and poor-quality mobile phone recordings with 82.2% accuracy, and those made with the electronic stethoscope with an accuracy of 86.5%. We conclude that our classification approach provides a mechanism for substantially improving auscultation recordings by non-experts. This work is the first systematic evaluation of a PCG signal quality classification algorithm (using a separate test dataset) and assessment of the quality of PCG recordings captured by non-experts, using both a medical-grade digital stethoscope and a mobile phone.

Prognostic value of acoustic cardiography in patients with chronic heart failure.

BACKGROUND: Rapid risk stratification in patients with heart failure is critically important but challenging. The aim of our study is to ascertain whether acoustic cardiography can identify heart failure (HF) patients at high risk for mortality. METHODS: A total of 474 HF patients were enrolled into our study (76±11years old). Acoustic cardiographic parameters included S3 score (ie, third heart sound exists) and systolic dysfunction index (SDI) (correlated closely with left ventricular systolic dysfunction). The event-free survival curves were plotted by Kaplan-Meier method. Cox regression analysis was used to identify independent predictors for all-cause mortality. RESULTS: During a mean follow-up of 484days, 169 (35.7%) patients died and 126 (26.6%) were due to cardiac causes. After controlling for age, systolic blood pressure, hemoglobin, blood urea nitrogen, albumin, as well as ACEI and beta-blocker treatment in multivariate Cox regression analysis, SDI ≥5 and S3 score ≥4 were both independent predictors for all-cause mortality. Kaplan-Meier analysis showed that HF patients with SDI ≥5 or S3 score ≥4 had a significantly lower survival (52.2% vs. 69.2%, Log-rank χ(2)=18.07, P<0.001; 56.8% vs. 68.6%, Log-rank χ(2)=10.58, P=0.001, respectively) than those with lower SDI or S3 score. CONCLUSIONS: Acoustic cardiography could serve as a cost-effective and time-efficient tool to identify HF patients at high risk for mortality who might benefit from aggressive monitoring and intervention. It may improve assessment and initial disposition decisions in HF management.

Logistic Regression-HSMM-Based Heart Sound Segmentation.

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 paper addresses the problem of the accurate segmentation of the first and second heart sound within noisy real-world PCG recordings using an 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, and evaluated this method on a large dataset of 10,172 s of PCG recorded from 112 patients (including 12,181 first and 11,627 second heart sounds). The proposed method achieved an average F1 score of 95.63 ± 0.85%, while the current state of the art achieved 86.28 ± 1.55% when evaluated on unseen test recordings. The greater discrimination between states afforded using logistic regression as opposed to the previous Gaussian distribution-based emission probability estimation as well as the use of an extended Viterbi algorithm allows this method to significantly outperform the current state-of-the-art method based on a two-sided paired t-test.

[Research on biometric method of heart sound signal based on GMM].

OBJECTIVE: Extraction of cepstral coefficients combined with Gaussian Mixture Model (GMM) is used to propose a biometric method based on heart sound signal. METHODS: Firstly, the original heart sounds signal was preprocessed by wavelet denoising. Then, Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are compared to extract representative features and develops hidden Markov model (HMM) for signal classification. At last, the experiment collects 100 heart sounds from 50 people to test the proposed algorithm. RESULTS: The comparative experiments prove that LPCC is more suitable than MFCC for heart sound biometric, and by wavelet denoising in each piece of heart sound signal, the system achieves higher recognition rate than traditional GMM. CONCLUSION: Those results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.