Cardiac Sound Signals Research Articles

Combined empirical mode decomposition and phase space reconstruction based psychologically stressed and non-stressed state classification from cardiac sound signals

The psychological stress and associated mental health conditions causes high socioeconomic impacts on society and the onset of pandemic worsened the situation making it imperative to timely detect psychological stress. This paper presents a novel framework for using a combination of empirical mode decomposition (EMD) and phase space reconstruction (PSR) analysis to capture non-linear, non-stationary dynamics of cardiac sound signals acquired from fifty-four healthy male adults for detecting psychological stress. The time interval among successive S1 peaks of acquired cardiac sound signals is extracted to obtain Interbeat Interval signal used for decomposition to Intrinsic Mode Functions (IMFs) using EMD technique. Thereafter, the feature vectors namely- largest singular value, smallest singular value from two-dimensional PSRs of IMFs and the mean value of Euclidean distance, standard deviation of Euclidean distance from three-dimensional PSRs are extracted to detect psychologically stressed state. The non-parametric Kruskal-Wallis statistical test is applied to select statistically significant features that are fed to Decision Tree, Naïve Bayes and Support Vector Machine classifiers for classifying stressed and non-stressed state with fivefold cross-validation to make it a reliable system. The average accuracy, sensitivity and specificity achieved is 97.14%, 99.8% and 94% respectively using SVM and Radial Basis Function kernel function. The proposed framework performed better on the dataset in comparison to ratio of low-frequency to high-frequency (LF/HF) power parameter of Electrocardiography signal. The use of easy to acquire, cost-effective cardiac sound signals for detecting psychological stress makes proposed framework feasible for rural healthcare centres of developing economies, homecare and telemedicine.

Single fibre enables acoustic fabrics via nanometre-scale vibrations.

Fabrics, by virtue of their composition and structure, have traditionally been used as acoustic absorbers1,2. Here, inspired by the auditory system3, we introduce a fabric that operates as a sensitive audible microphone while retaining the traditional qualities of fabrics, such as machine washability and draping. The fabric medium is composed of high-Young'smodulus textile yarns in the weft of a cotton warp, converting tenuous 10-7-atmosphere pressure waves at audible frequencies into lower-order mechanical vibration modes. Woven into the fabric is a thermally drawn composite piezoelectric fibre that conforms to the fabric and converts the mechanical vibrations into electrical signals. Key to the fibre sensitivity is an elastomeric cladding that concentrates the mechanical stress in a piezocomposite layerwith a high piezoelectric charge coefficient of approximately 46 picocoulombs per newton, a result of the thermal drawing process. Concurrent measurements of electric output and spatial vibration patterns in response to audible acoustic excitation reveal that fabric vibrational modes with nanometre amplitude displacement are the source of the electrical output of the fibre. With the fibre subsuming less than 0.1% of the fabric by volume, a single fibre draw enables tens of square metres of fabric microphone. Three different applications exemplify the usefulness of this study: a woven shirt with dual acoustic fibres measures the precise direction of an acoustic impulse, bidirectional communications are established between two fabrics working as sound emitters and receivers, and a shirt auscultates cardiac sound signals.

Acoustic-pressure sensor array system for cardiac-sound acquisition

This paper presents a cardiac-sound-detection system based on an acoustic-pressure sensor array. The traditional cardiac-sound probe has the disadvantages of high cost and thick structure, which makes embedding difficult. Therefore, a methodology for cardiac-sound detection, comprising a sound-pressure sensor array, is proposed to achieve a low cost, very thin, and wearable cardiac-sound probe, which can be embedded in furniture or clothes for long-term heart sound monitoring. By introducing several miniature electret-condenser-microphone-based acoustic-pressure sensors, a sensor array for picking up cardiac sound was designed. The data-processing platform comprised a field-programmable gate array and microcontroller unit. Furthermore, a series of de-noising methods including oversampling, finite impulse response digital filtering, and digital extraction were used, thereby facilitating accurate cardiac-sound signal identification. Test results showed that the average Root Mean Square amplitude of the noise after processing was more than 3 dB lower than that of the signal obtained from a single sensor in the array. The signal-to-noise ratio improved more than 7 dB after processing. Furthermore, actual cardiac sounds were acquired and compared using the proposed sensor array and the dedicated cardiac-sound sensor. The proposed 4 mm thick sensor array could effectively acquire S1- and S2-segments of cardiac sound. The proposed system demonstrates the potential for use in the development of wearable cardiac-sound monitoring systems.

An Efficient Computational Approach for Phonocardiogram Signals Analysis and Normal/Abnormal heart sounds diagnosis

In the present work, we proposed an intelligent approach for the examination and classification of cardiac sound signals “phonocardiogram (PCG)”. In this approach, artificial neural network (ANN) is executed as indicator and classifier of PCG abnormalities using the features extracted from PCG acoustic signals via the discrete wavelet transform (DWT). To develop and validate the proposed approach, the PASCAL CHSC 2011 dataset was utilized. The k-fold cross validation was utilized to assess the efficiency of the proposed intelligent approach. The results demonstrate that the approach achieves high performance compared to other classification techniques for PCG datasets. The obtained results showed an overall accuracy of 99.89%. Moreover, the proposed approach results are compared with the ones that achieved utilizing different machine learning (ML) approaches recently published. The achieved results showed that our proposed system has ability for efficient diagnosis and classifications of PCG acoustic signals also; it can also assist the clinicians to take accurate decisions in detecting cardiovascular abnormalities.

Detection of cardiac sounds components: a pilot study

<span>This paper presents a preliminary study related to the detection and identification of cardiac sounds components including first sound (S1), second sound (S2) and murmurs. Detection and identification of cardiac sounds are an important process in automated cardiac sound analysis system in order to automatically diagnose people who are having cardiovascular disorder and determine the existence of murmurs. Sixteen of recorded cardiac sounds (eight normal cardiac sounds, four abnormal cardiac sounds with systole murmur, and four abnormal cardiac sounds with diastole murmur) from PASCAL Classifying Heart Sounds Challenge database were examined for analysis. This work is significant in studying the time and time-frequency based detection of cardiac sounds components characteristics. In time-based analysis, envelope of signal energy was used to do the peak detection of S1, S2 and murmur and also analysis of cardiac cycle, systole and diastole duration. While time-frequency based analysis was used to determine the S1, S2 and murmur frequency range. The findings yield the overall accuracy of envelope-based detection for normal cardiac sound signal at 60.85% while for abnormal cardiac sound signal at 57.24%.</span>

A Framework Classification of Heart Sound Signals in PhysioNet Challenge 2016 Using High Order Statistics and Adaptive Neuro-Fuzzy Inference System

To investigate the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS), activated by spectral analysis features, for detection of abnormal cardiac valves sound signals. A dataset of 1837 heart sound signals were acquired from international PhysioNet Challenge 2016 databases (classes A, B and E). This included 1369 normal and 468 abnormal signals. The signals were de-noised using Notch and Butterworth filtering, fed to Discrete Fourier Transform, and 5 features using High Order Spectral (HOS) analysis were extracted from the third Cumulant. Later, the ANFIS neural network was trained and tested to discern abnormal signals. The results showed that the selected features were statistically significant ( $\text{p} ). The proposed method was tested and achieved classification of 63-89% accuracy, 63-100% sensitivity, and 62-100% specificity, respectively. The results were compared with reports utilizing different neural network techniques, indicating competitive performance. The HOS spectral features can be reliable to participate in neural network systems to sort heart sound (HS) signals as normal or abnormal. The bispectral matrix is a new presentation of attributes describing signals. The ANFIS is a suggestive successful tool, which has not been attempted in Physio-net challenge 2016. The HOS attributes and ANFIS can participate successfully in PhysioNet Challenge 2016.

Intelligent methodologies for cardiac sound signals analysis and characterization in cepstrum and time‐scale domains

AbstractBiometric authentication is the process that allows an individual to be identified based on a set of unique biological features data. In this study, we present different experiments to use the cardiac sound signals (phonocardiogram “PCG”) as a biometric authentication trait. We have applied different features extraction approaches and different classification techniques to use the PCG as a biometric trait. Through all experiments, data acquisition is based on collecting the cardiac sounds from HSCT‐11 and PASCAL CHSC2011 datasets, while preprocessing is concerned with de‐noising of cardiac sounds using multiresolution‐decomposition and multiresolution‐reconstruction (MDR‐MRR). The de‐noised signal is then segmented based on frame‐windowing and Shanon energy (SE) methods. For feature extraction, Cepstral (Cp) domain (based on mel‐frequency) and time‐scale (T‐S) domain (based on Wavelet Transform) features are extracted from the de‐noised signal after segmentation. The features, extracted from the Cp‐domain and the T‐S domain, are fed to four different classifiers: Artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and K‐nearest neighbor (KNN). The performance of the classifications is assessed based on the k‐fold cross validation. The computation complexity of the feature extraction domains is expressed using the Big‐O measurements. The T‐S features are superior to PCG heart signals in terms of the classification accuracy. The experiments' results give the highest classification accuracy with lowest computation complexity for RF in the Cp domain and SVM and ANN in the T‐S domain.

Metodología para discernir entre sonido cardiaco no patológico de regurgitación y estenosis aórtica, empleando DTW

En este artículo se describe la metodología empleada para adquirir, almacenar y procesar señales de sonido cardiaco con el objetivo de comparar las señales de sonidos patológicos con los no patológicos y determinar las fronteras de decisión correspondientes para una futura clasificación. Las señales de sujetos no patológicos se han adquirido con el sistema propuesto en este documento, el cual se ha diseñado considerando las frecuencias tanto de sonidos patológicos como no patológicos. Dos casos, uno de estenosis aórtica y otro de regurgitación aórtica, obtenidos de un sitio público, se han analizado para efectuar las comparaciones. Empleando el algoritmo computacional de Alineamiento Temporal Dinámico se evaluó la similitud que existe en la forma de las señales no patológicas y patológicas. Los resultados han ayudado a proponer las fronteras de decisión de separación entre las clases de señales.

Automatic diagnosis of septal defects based on tunable-Q wavelet transform of cardiac sound signals

Accurate and quick diagnosis of cardiac disorders like septal defects can be performed by automatic analysis of cardiac sound signals using advanced signal processing methods. In this paper, we present a new method for diagnosis of septal defects from cardiac sound signals using tunable-Q wavelet transform (TQWT) based features. To start with, the established constrained TQWT based approach has been used to derive the heart beat cycles from cardiac sound signals. Then the TQWT based decomposition of segmented heart beat cycles has been performed up to a certain level. The combinations of sub-bands obtained from TQWT based decomposition can be used to extract the diagnostic features. The correlation between sub-bands can characterize the various types of murmurs in cardiac sound signals. Therefore, in order to represent the murmurs in cardiac sound signals, proposed feature set was obtained with sum of average magnitude difference function (SAMDF) that have been computed from reconstruction of decomposed sub-bands. In search of effective feature set based on SAMDF that could provide significant classification performance, various decomposition levels have been examined. Moreover, in order to establish the usefulness of the proposed method for diagnosis of septal defects, besides cardiac sound signals for septal defects and normal, this study covers signals to be detected for valvular defects and other defects like ventricular hypertrophy, constrictive pericarditis etc. as available from publicly available datasets. The classification has been performed using least squares support vector machine (LS-SVM) with different kernel functions. At each decomposition level under study, the effect of quality-factor (Q) of the TQWT from 1 to 50 on classification performance has been evaluated. Moreover, in order to show the effectiveness of the proposed method, results have been compared with existing method.

Classification of cardiac sound signals using constrained tunable-Q wavelet transform

The features extracted from the cardiac sound signals are commonly used for detection and identification of heart valve disorders. In this paper, we present a new method for classification of cardiac sound signals using constrained tunable-Q wavelet transform (TQWT). The proposed method begins with a constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that from containing both. Therefore, heart sounds and murmur have been separated using constrained TQWT. Then the proposed novel raw feature set has been created by the parameters that have been optimized while constraining the output of TQWT together with that of extracted by using time-domain representation and Fourier–Bessel (FB) expansion of separated heart sounds and murmur. However, the adaptively selected features have been used to obtain the final feature set for subsequent classification of cardiac sound signals using least squares support vector machine (LS-SVM) with various kernel functions. The performance of the proposed method has been validated with publicly available datasets and the results have been compared with the existing short-time Fourier transform (STFT) based method. The proposed method shows higher percentage classification accuracy of 94.01 as compared to 93.53 of STFT based method. In comparison with STFT based method, it is noteworthy that the proposed method uses well defined and lower dimensionality of feature vector that can reduce the computational complexity.

Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform

The automatic segmentation of cardiac sound signals into heart beat cycles is generally required for the diagnosis of heart valve disorders. In this paper, a new method for segmentation of the cardiac sound signals using tunable-Q wavelet transform (TQWT) has been presented. The murmurs from cardiac sound signals are removed by suitably constraining TQWT based decomposition and reconstruction. The Q-factor, redundancy parameter and number of stages of decomposition of the TQWT are adapted to the desired statistical properties of the murmur-free reconstructed cardiac sound signals. The envelope based on cardiac sound characteristic waveform (CSCW) is extracted after the removal of low energy components from the reconstructed cardiac sound signals. Then the heart beat cycles are derived from the original cardiac sound signals by mapping the required timing information of CSCW which is obtained using established methods. The experimental results are included in order to show the effectiveness of the proposed method for segmentation of cardiac sound signals in comparison with other existing methods for various clinical cases.

Development of a four-channel cardiovascular signals collector based on STM32 with digital-controlled gain

To get better methods on diagnosis of cardiovascular diseases and health assessment by comparison analysis of four kinds of cardiovascular signals, which are blood pressure signal (BPS), plus wave signal (PWS), electrocardiosignal (ECS) and cardiac sound signal (CSS), a four-channel signals collector is introduced in this article. This collector is composed of microcomputer STM32F103RB with integrated 12-bit analog to digital converters (ADCs) and some integrated chips (IC) such as PGA113, a programmable gain amplifier with internal calibration channels for system-level calibration. Therefore, the data of four kinds of cardiovascular signals can be both collected by this signal collector with a high precision and output to a computer by universal serial bus (USB) interface, which can also offers power for this instrument. In addition, the gain of input signal can be changed by computer software through USB interface under microchip commands. Thus it has many merits, such as the small volume, low cost, high performance and precision, digital-controlled gain and so on. Consequently, it is convenient to acquire the data of four kinds of cardiovascular signals by it, and a waveform of one sample is shown.

Constrained Tunable-Q Wavelet Transform based Analysis of Cardiac Sound Signals

In this paper, we present a new method for analysis of cardiac sound signals containing murmurs using constrained tunable-Q wavelet transform (TQWT). The fundamental heart sounds (FHS) and murmurs are separately reconstructed by suitably constraining TQWT. The segmentation of reconstructed murmurs into heart beat cycles is achieved using cardiac sound characteristic wave-form (CSCW) of reconstructed FHS. The frequency domain based approximate entropy, spectral entropy, Lempel-Ziv complexity, and time domain Shannon entropy are computed for each segmented heart beat cycles for least squares support vector machine (LS-SVM) based classification. The experimental results are included to show the effectiveness of the proposed method.

Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders

Heart murmurs are pathological sounds produced by turbulent blood flow due to certain cardiac defects such as valves disorders. Detection of murmurs via auscultation is a task that depends on the proficiency of physician. There are many cases in which the accuracy of detection is questionable. The purpose of this study is development of a new mathematical model of systolic murmurs to extract their crucial features for identifying the heart diseases. A high resolution algorithm, multivariate matching pursuit, was used to model the murmurs by decomposing them into a series of parametric time–frequency atoms. Then, a novel model-based feature extraction method which uses the model parameters was performed to identify the cardiac sound signals. The proposed framework was applied to a database of 70 heart sound signals containing 35 normal and 35 abnormal samples. We achieved 92.5% accuracy in distinguishing subjects with valvular diseases using a MLP classifier, as compared to the matching pursuit-based features with an accuracy of 77.5%.

Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique

In this paper, a novel cardiac sound spectral analysis method using the normalized autoregressive power spectral density (NAR-PSD) curve with the support vector machine (SVM) technique is proposed for classifying the cardiac sound murmurs. The 489 cardiac sound signals with 196 normal and 293 abnormal sound cases acquired from six healthy volunteers and 34 patients were tested. Normal sound signals were recorded by our self-produced wireless electric stethoscope system where the subjects are selected who have no the history of other heart complications. Abnormal sound signals were grouped into six heart valvular disorders such as the atrial fibrillation, aortic insufficiency, aortic stenosis, mitral regurgitation, mitral stenosis and split sounds. These abnormal subjects were also not included other coexistent heart valvular disorder. Considering the morphological characteristics of the power spectral density of the heart sounds in frequency domain, we propose two important diagnostic features Fmax and Fwidth, which describe the maximum peak of NAR-PSD curve and the frequency width between the crossed points of NAR-PSD curve on a selected threshold value (THV), respectively. Furthermore, a two-dimensional representation on ( Fmax, Fwidth) is introduced. The proposed cardiac sound spectral envelope curve method is validated by some case studies. Then, the SVM technique is employed as a classification tool to identify the cardiac sounds by the extracted diagnostic features. To detect abnormality of heart sound and to discriminate the heart murmurs, the multi-SVM classifiers composed of six SVM modules are considered and designed. A data set was used to validate the classification performances of each multi-SVM module. As a result, the accuracies of six SVM modules used for detection of abnormality and classification of six heart disorders showed 71–98.9% for THVs=10–90% and 81.2–99.6% for THVs=10–50% with respect to each of SVM modules. With the proposed cardiac sound spectral analysis method, the high classification performances were achieved by 99.9% specificity and 99.5% sensitivity in classifying normal and abnormal sounds (heart disorders). Consequently, the proposed method showed relatively very high classification efficiency if the SVM module is designed with considering THV values. And the proposed cardiac sound murmurs classification method with autoregressive spectral analysis and multi-SVM classifiers is validated for the classification of heart valvular disorders.

Comparison of envelope extraction algorithms for cardiac sound signal segmentation

This paper describes a comparative study of the envelope extraction algorithms for the cardiac sound signal segmentation. In order to extract the envelope curves based on the time elapses of the first and the second heart sounds of cardiac sound signals, three representative algorithms such as the normalized average Shannon energy, the envelope information of Hilbert transform, and the cardiac sound characteristic waveform (CSCW) are introduced. Performance comparison of the envelope extraction algorithms, and the advantages and disadvantages of the methods are examined by some parameters.

PC를 기반으로 한 선박용 디지털 심전도 및 청진기 개발

심전도 측정기와 청진기를 통하여 획득된 심전도와 심음도는 비관혈적 방법으로 환자를 진단할 수 있는 생체신호이며, 의사들의 진단도구로 널리 이용되어져 왔다. 그러나 의사들이 심전도 측정기나 청진기를 이용하여 환자를 진단하는 장소는 병원 내 진료실과 응급실로 한정되어 있기 때문에, 병원에서 거리가 먼 원거리 지역이나 자동차, 비행기, 선박에서는 환자의 심음을 확인할 수 없다. 특히, 원양항해에 종사하는 선박 환자는 전문 의료진으로부터 진료를 받을 수 없는 경우가 있다. 본 논문에서는 환자의 심음 및 심전을 용이하게 측정함으로써 PC기반의 선박용 디지털 심전도 및 청진기를 개발하였으며, 해상 원격진료의 출현을 앞 당길 수 있으리라 본다. Electrocardiogram and phonocardiogram acquired from ECG and stethoscope by a non-invasive method are bio-signals which laue been a great role in patients diagnosis and used extensively by doctors. And these are mainly used at the examination room or first-and room of hospital. When the patients are located at some places far away from hospital like a car, an airplane and a vessel under way, there are some difficulties in confirming their cardiac sound Especially the patient of ocean going vessel might not have a medical treatment from the professional doctor at times. In this paper, we developed the digital ECG and stethoscope system based on personal computer which can easily measure the patient's cardiac sound and ECG signal, and this might shorten the appearance of the remote maritime telemedicine.

A System for AR Modeling Study of Cardiac Sound Signal

In cardiology, the cardiac sound signal forms the basis for the analysis of heart functions. Normally cardiac sound signal interprets the functions of the heart valve and ventricles. Also, the cardiac sound signal generated by the blood flow murmurs are used to diagnose the disease of the coronary arteries. Processing these signals using Digital Signal Processing techniques bring out crucial, but otherwise unfathomable information about the functioning of the heart. In the present work, a software tool has been designed for the study of the spectral characteristics of cardiac sound signal using AR Modeling. The system has been developed using Yule-Walker method and the Levinson-Durbin algorithm in Matlab-Windows environment. This research falling under the domain of Medical Informatics assists both the researchers and practicing specialists in the area of cardiology.