An open-access simultaneous electrocardiogram and phonocardiogram database.

Objective.The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) recording, with additional channels for environmental audio to enhance PCG through active noise cancellation. The objective was to study multimodal electro-mechanical activities of the heart, offering insights into the differences and synergies between these modalities during various cardiac activity levels.Approach.We developed and tested several hardware prototypes of a simultaneous ECG-PCG acquisition device. Using this technology, we collected simultaneous ECG and PCG data from 24 healthy adults during different physical activities, including resting, walking, running, and stationary biking, in an indoor fitness center. The data were annotated using a robust software that we developed for detecting ECG R-peaks and PCG S1 and S2 components, and overseen by a human expert. We also developed machine learning models using ECG-based, PCG-based, and joint ECG-PCG features, like R-R and S1-S2 intervals, to classify physical activities and analyze electro-mechanical dynamics.Main results.The results show a significant coupling between ECG and PCG components, especially during high-intensity exercise. Notable micro-variations in S2-based heart rate show differences in the heart's electrical and mechanical functions. The Lomb-Scargle periodogram and approximate entropy analyses confirm the higher volatility of S2-based heart rate compared to ECG-based heart rate. Correlation analysis shows stronger coupling between R-R and R-S1 intervals during high-intensity activities. Hybrid ECG-PCG features, like the R-S2 interval, were identified as more informative for physical activity classification through mRMR feature selection and SHAP value analysis.Significance.The EPHNOGRAM database, is available on PhysioNet. The database enhances our understanding of cardiac function, enabling future studies on the heart's mechanical and electrical interrelationships. The results of this study can contribute to improved cardiac condition diagnoses. Additionally, the designed hardware has the potential for integration into wearable devices and the development of multimodal stress test technologies.

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.

Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network.

Given the patient to doctor ratio of 50,000:1 in low income and middle-income countries, there is a need for automated heart sound classification system that can screen the Phonocardiogram (PCG) records in real-time. This paper proposes deep neural network architectures such as a one-dimensional convolutional neural network (1D-CNN) and Feed-forward Neural Network (F-NN) for the classification of unsegmented phonocardiogram (PCG) signal. The research paper aims to automate the feature engineering and feature selection process used in the analysis of the PCG signal. The original PCG signal is down-sampled at 500 Hz. Then they are divided into smaller time segments of 6 s epochs. Savitzky-Golay filter is used to suppress the high-frequency noises in the signal by data point smoothening. The processed data was then provided as an input to the proposed deep neural network (DNN) architectures. 1081 PCG records were used for training and validating the proposed DNN models. The Feed-forward Neural Network model with five hidden layers provided a better overall accuracy of 0.8565 with a sensitivity of 0.8673, and specificity of 0.8475. The balanced accuracy of the model was found to be 0.8574. The performance of the model was also studied using the Receiver Operating Characteristic (ROC) plot, which produced an Area Under the Curve (AUC) value of 0.857. The classification accuracy of the proposed models was compared to the related works on PCG signal analysis for cardiovascular disease detection. The DNN models studied in this study provided comparable performance in heart sound classification without the requirement of feature engineering and segmentation of heart sound signals.

Design and development of a digital stethoscope encapsulation for simultaneous acquisition of phonocardiography and electrocardiography signals: the SmartHeart case study.

The stethoscope is a major symbol of modern medicine. It is used for diagnosis of different conditions and enables physicians to listen to internal body sounds. Electrocardiography was only introduced in medicine in the beginning of the twentieth century. Today measuring heart's electrical activity is also fundamental cardiac diseases diagnosis. Although performed with independent devices, requiring physician and patient presence in the same physical space, in combination they enhance cardiovascular assessment. In this paper, a digital stethoscope encapsulation was designed, adding new functionality to this advanced medical device. Today wired and wireless communications enable different medical devices to share data and information, over long distances. Using low-cost hardware technologies, the encapsulation will add the ability to acquire and transmit via Bluetooth the Electrocardiographic activity, determined in the same cardiac focus and synchronised with the Phonocardiographic sound recordings. Several encapsulation concepts were developed and prototyped using 3D printing. They were easily fitted to the digital stethoscope and tested in a hospital environment for ergonomics, acoustic and electric signals acquisition. The best concept was chosen with the help of a physician's opinion and the final prototype performance was very satisfactory.

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVETand Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram.

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.

Design and Evaluation of a Diaphragm for Electrocardiography in Electronic Stethoscopes.

Combining Phonocardiography (PCG) and Electrocardiography (ECG) data has been recognized within the state-of-the-art as of added value for enhanced cardiovascular assessment. However, multiple aspects of ECG data acquisition in a stethoscope form factor remain unstudied, and existing devices typically enforce a substantial change into routine clinical auscultation procedures, with predictably low technology acceptance. As such, in this paper, we present a novel approach to ECG data acquisition throughout the five main cardiac auscultation points, and that intends to be incorporated in a commonly used electronic stethoscope. Therefore, it enables analysis and acquisition of both PCG and ECG signals in a single pass. We describe the development, experimental evaluation, and comparison of the ECG signals obtained using our proposed approach and a gold standard medical device, through metrics that allow the evaluation of morphological similarities. Results point to a high correlation between the two evaluated setups, thus supporting the idea of meaningfully collecting ECG data along medical auscultation points with the proposed form factor. Moreover, this work has led us to conclude that for the studied population, signals acquired on focuses F1, F2, and F3 are usually highly correlated with leads V1 and V2 of the standard ECG medical recording procedure.

Automatic Signal Quality Index Determination of Radar-Recorded Heart Sound Signals Using Ensemble Classification.

OBJECTIVE: Radar technology promises to be a touchless and thereby burden-free method for continuous heart sound monitoring, which can be used to detect cardiovascular diseases. However, the first and most crucial step is to differentiate between high- and low-quality segments in a recording to assess their suitability for a subsequent automated analysis. This paper gives a comprehensive study on this task and first addresses the specific characteristics of radar-recorded heart sound signals. METHODS: To gather heart sound signals recorded from radar, a bistatic radar system was built and installed at the university hospital. Under medical supervision, heart sound data were recorded from 30 healthy test subjects. The signals were segmented and labeled as high- or low-quality by a medical expert. Different state-of-the-art pattern classification algorithms were evaluated for the task of automated signal quality determination and the most promising one was optimized and evaluated using leave-one-subject-out cross validation. RESULTS: The proposed classifier is able to achieve an accuracy of up to 96.36% and demonstrates a superior classification performance compared with the state-of-the-art classifier with a maximum accuracy of 76.00%. CONCLUSION: This paper introduces an ensemble classifier that is able to perform automated signal quality determination of radar-recorded heart sound signals with a high accuracy. SIGNIFICANCE: Besides achieving a higher performance compared with state-of-the-art classifiers, this study is the first one to deal with the quality determination of heart sounds that are recorded by radar systems. The proposed method enables contactless and continuous heart sound monitoring for the detection of cardiovascular diseases.

Analyzing approaches to mathematic modeling of heart sounds

The article introduces the findings of the analysis of the existing approaches to the development of mathematical models of acoustic heart phenomena. The analysis of mathematical methods that can be used to model heart sounds has been performed with the use of reference signals from the 3M Open Library (Littmann Library) and a set of signals obtained by the authors during their previous scientific efforts. The analysis findings have allowed revealing the approaches and methods that are most suitable for developing the mathematical models of human phonocardiograms (normal and pathological) for further research efforts meant to develop methods to single out heart beats against the high level of interference and creating intervalograms to characterize the heart rate at the current moments of time. In addition to the generation of model phonocardiograms, the article reviews the methods to analyze model and real-life phonocardiograms with the assessment of an input from random and deterministic components.

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.

Is there an Optimal Localization of Cardio-microphone Sensors for Phonocardiogram Analysis?

Heart auscultation is one of the most useful medical diagnostic tools for getting valuable information of heart valves and heart hemodynamics functions. However, the information acquired by a traditional stethoscope can be inaccurate and insufficient. Phonocardiogram (PCG) was developed to improve accuracy through visual inspection and analysis. Digitally processed, PCG can then be analyzed by automated heart sound analysis systems. But there is no standardization for PCG data acquisition unlike electrocardiogram (ECG). This study aims at analyzing the influence of cardiomicrophone localization on the chest for the study of cardiac sounds S1 and S2. For that purpose, simultaneous acquisitions of 12 PCG signals with one ECG signal were realized and a comparative analysis of delays between R waves of ECG and detected S1 and S2 sounds was conducted. Results show that there are significant differences between R-S1 (or R-S2) intervals obtained from different areas of sensor placement on the chest. For future works on PCG, studies dealing with the analysis of heart sounds or proposing new heart sounds detection algorithms may pay attention to the location and attachment of PCG sensors.

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.

Review: Most Recent Advancements in Digital Signal Processing of the Phonocardiogram.

The objective of the present paper is to provide a detailed review of the most recent developments in instrumentation and signal processing of digital phonocardiography and heart auscultation. After a short introduction, the paper presents a brief history of heart auscultation and phonocardiography, which is followed by a summary of the basic theories and controversies regarding the genesis of the heart sounds. The application of spectral analysis and the potential of new time-frequency representations and cardiac acoustic mapping to resolve the controversies and better understand the genesis and transmission of heart sounds and murmurs within the heart-thorax acoustic system are reviewed. The most recent developments in the application of linear predictive coding, spectral analysis, time-frequency representation techniques, and pattern recognition for the detection and follow-up of native and prosthetic valve degeneration and dysfunction are also presented in detail. New areas of research and clinical applications and areas of potential future developments are then highlighted. The Final section is a discussion about a multidegree of freedom theory on the origin of the heart sounds and murmurs, which is completed by the authors' conclusion.

An Intelligent Phonocardiography for Automated Screening of Pediatric Heart Diseases.

This paper presents a robust device for automated screening of pediatric heart diseases based on our unique processing method in murmur characterization; the Arash-Band method. The present study modifies the Arash-Band method and employs output of the modified method in conjunction with the two other original techniques to extract indicative feature vectors for the screening. The extracted feature vectors are classified by using the support vector machine method. Results show that the proposed modifications significantly enhances performance of the Arash-Band in terms of the both accuracy and sensitivity as the corresponding effect sizes are sufficiently large. The proposed algorithm has been incorporated into an Android-based tablet to constitute an intelligent phonocardiogram with the automatic screening capability. In order to obtain confidence interval of the accuracy and sensitivity, an inferable statistical test is applied on our database containing the phonocardiogram signals recorded from 263 of the referrals to a hospital. The expected value of the accuracy/sensitivity is estimated to be 87.45 % / 87.29 % with a 95 % confidence interval of (80.19 % - 92.47 %) / (76.01 % - 95.78 %) exhibiting superior performance than a pediatric cardiologist who relies on conventional or even computer-assisted auscultation.

A system for heart sounds classification.

The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases - one of the major causes of death around the globe - a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability.

Smart phone monitoring of second heart sound split.

Heart Auscultation (listening to heart sounds) is the basic element of cardiac diagnosis. The interpretation of these sounds is a difficult skill to acquire. In this work we have developed an application to detect, monitor, and analyze the split in second heart sound (S2) using a smart phone. The application records the heartbeat using a stethoscope connected to the smart phone. The audio signal is converted into the frequency domain using Fast Fourier Transform to detect the first and second heart sounds (S1 and S2). S2 is extracted and fed into the Discrete Wavelet Transform (DWT) and then to Continuous Wavelet Transform (CWT) to detect the Aortic (A2) and the Pulmonic (P2) components, which are used to calculate the split in S2. With our application, users can continuously monitor their second heart sound irrespective of ages and check for a split in their hearts with a low-cost, easily available equipment.

Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition.

The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs.

Noise/spike detection in phonocardiogram signal as a cyclic random process with non-stationary period interval.

The major aim of this study is to describe a unified procedure for detecting noisy segments and spikes in transduced signals with a cyclic but non-stationary periodic nature. According to this procedure, the cycles of the signal (onset and offset locations) are detected. Then, the cycles are clustered into a finite number of groups based on appropriate geometrical- and frequency-based time series. Next, the median template of each time series of each cluster is calculated. Afterwards, a correlation-based technique is devised for making a comparison between a test cycle feature and the associated time series of each cluster. Finally, by applying a suitably chosen threshold for the calculated correlation values, a segment is prescribed to be either clean or noisy. As a key merit of this research, the procedure can introduce a decision support for choosing accurately orthogonal-expansion-based filtering or to remove noisy segments. In this paper, the application procedure of the proposed method is comprehensively described by applying it to phonocardiogram (PCG) signals for finding noisy cycles. The database consists of 126 records from several patients of a domestic research station acquired by a 3M Littmann(®) 3200, 4KHz sampling frequency electronic stethoscope. By implementing the noisy segments detection algorithm with this database, a sensitivity of Se=91.41% and a positive predictive value, PPV=92.86% were obtained based on physicians assessments.