Background 
Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established


Objective 
To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds This systematic review provides a complete summary of methods used in the literature to give a baseline for future works


Data sources 
A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases Additional articles were further obtained by references listed in the articles found Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification


Study selection 
Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated


Data extraction 
Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved


Data synthesis 
A total of 77 reports from the literature were included in this review 55 (7143%) of the studies focused on wheeze, 40 (5195%) on crackle, 9 (1169%) on stridor, 9 (1169%) on rhonchi, and 18 (2338%) on other sounds such as pleural rub, squawk, as well as the pathology Instrumentation used to collect data included microphones, stethoscopes, and accelerometers Several references obtained data from online repositories or book audio CD companions Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques Performance reported in the surveyed works were converted to accuracy measures for data synthesis


Limitations 
Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different A standard validation method has not been established, resulting in different works using different methods and performance measure definitions


Conclusion 
A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis The performance of recent studies showed a high agreement with conventional non-automatic identification This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases

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Automatic adventitious respiratory sound analysis: A systematic review.

Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries useful information about the functionality and status of the heart and hence several signal processing and machine learning technique can be applied to study and diagnose heart disorders. Based on PCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal categories. We have created database of 5 categories of heart sound signal (PCG signals) from various sources which contains one normal and 4 are abnormal categories. This study proposes an improved, automatic classification algorithm for cardiac disorder by heart sound signal. We extract features from phonocardiogram signal and then process those features using machine learning techniques for classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and classification we have used support vector machine (SVM), deep neural network (DNN) and centroid displacement based k nearest neighbor. To improve the results and classification accuracy, we have combined MFCCs and DWT features for training and classification using SVM and DWT. From our experiments it has been clear that results can be greatly improved when Mel Frequency Cepstral Coefficient and Discrete Wavelets Transform features are fused together and used for classification via support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy. The code and dataset can be accessed at “https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-/blob/master/README.md”.

Classification of Heart Sound Signal Using Multiple Features

Lung sound classification using cepstral-based statistical features

Accurate measurement of heart sound and murmur parameters is of great importance in the automated analysis of phonocardiogram (PCG) signals. In this paper, we propose a novel unified PCG signal delineation and murmur classification method without the use of reference signal for automatic detection and classification of heart sounds and murmurs. The major components of the proposed method are the empirical wavelet transform-based PCG signal decomposition for discriminating heart sounds from heart murmurs and suppressing background noises, the Shannon entropy envelope extraction, the instantaneous phase-based boundary determination, heart sound and murmur parameter extraction, the systole/diastole discrimination and the decision rules-based murmur classification. The accuracy and robustness of the proposed method is evaluated using a wide variety of normal and abnormal PCG signals taken from the standard PCG databases, including PASCAL heart sounds challenge database, PhysioNet/CinC challenge heart sound database, and real-time PCG signals. Evaluation results show that the proposed method achieves an average sensitivity (Se) of 94.38%, positive predictivity (Pp) of 97.25%, and overall accuracy (OA) of 91.92% for heart sound segmentation and Se of 97.58%, Pp of 96.46%, and OA of 94.21% in detecting the presence of heart murmurs for SNR of 10 dB. The method yields an average classification accuracy of 95.5% for the PCG signals with SNR of 20 dB. Results show that the proposed method outperforms other existing heart sound segmentation and murmur classification methods.

Effective Heart Sound Segmentation and Murmur Classification Using Empirical Wavelet Transform and Instantaneous Phase for Electronic Stethoscope

One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient's heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.

Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring.

Traditionally, the clinical diagnosis of a respiratory disease is made from a careful clinical examination including chest auscultation. Objective analysis and automatic interpretation of the lung sound based on its physical characters are strongly warranted to assist clinical practice. In this paper, a new method is proposed to distinguish between the normal and the abnormal subjects using the morphological complexities of the lung sound signals. The morphological embedded complexities used in these experiments have been calculated in terms of texture information (lacunarity), irregularity index (sample entropy), third order moment (skewness), and fourth order moment (Kurtosis). These features are extracted from a mixed data set of 10 normal and 20 abnormal subjects and are analyzed using two different classifiers: extreme learning machine (ELM) and support vector machine (SVM) network. The results are obtained using 5-fold cross-validation. The performance of the proposed method is compared with a wavelet analysis based method. The developed algorithm gives a better accuracy of 92.86% and sensitivity of 86.30% and specificity of 86.90% for a composite feature vector of four morphological indices.

/pdf/detection-of-lungs-status-using-morphological-complexities-2my5ue21xy.pdf

Detection of lungs status using morphological complexities of respiratory sounds.

During the recording time of lung sound (LS) signals from the chest wall of a subject, there is always heart sound (HS) signal interfering with it. This obscures the features of lung sound signals and creates confusion on pathological states, if any, of the lungs. A novel method based on empirical mode decomposition (EMD) technique is proposed in this paper for reducing the undesired heart sound interference from the desired lung sound signals. In this, the mixed signal is split into several components. Some of these components contain larger proportions of interfering signals like heart sound, environmental noise etc. and are filtered out. Experiments have been conducted on simulated and real-time recorded mixed signals of heart sound and lung sound. The proposed method is found to be superior in terms of time domain, frequency domain, and time-frequency domain representations and also in listening test performed by pulmonologist.

Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique.

The primary problem with lung sound (LS) analysis is the interference of heart sound (HS) which tends to mask important LS features. The effect of heart sound is more at medium and high flow rate than that of low flow rate. Moreover, pathological HS obscures LS in a higher degree than normal HS. To get over this problem, several HS reduction techniques have been developed. An important preprocessing step in HS reduction is localization of HS components. In this paper, a new HS localization algorithm is proposed which is based on Hilbert transform (HT) and Heron’s formula. In the proposed method, the HS included segment is differentiated from the HS excluded segment by comparing their area with an adaptive threshold. The area of a HS component is calculated from the Hilbert envelope using Heron’s triangular formula. The method is tested on real recorded and simulated HS corrupted LS signals. All the experiments are conducted under low, medium and high breathing flow rates. The proposed method shows a better performance than the comparative Singular Spectrum Analysis (SSA) based method in terms of accuracy (ACC), detection error rate (DER), false negative rate (FNR), and execution time (ET).

/pdf/an-automated-tool-for-localization-of-heart-sound-components-2jotyulilx.pdf

An automated tool for localization of heart sound components S1, S2, S3 and S4 in pulmonary sounds using Hilbert transform and Heron’s formula

A computerized cardiac disorders classification system needs a proper boundary estimated cardiac cycle of recorded heart sound signals. In the proposed algorithm the boundaries of two primary heart sounds, S1 and S2 events are estimated using Hilbert transform method and a statistical approach. This method uses an adaptive threshold value which is calculated from the first- and second-order moments of the heart sound or Phonocardiogram signal (PCG) envelope. Here, Hilbert transform is used for extracting the envelope of the PCG signal and a threshold is applied to detect the boundary regions of the primary events, S1 and S2, of the same signal. The performance of the algorithm is evaluated for normal and five commonly occurring pathological cases. The proposed method is computationally fast and obtained an accuracy of 97.24 percent.

Boundary estimation of cardiac events S1 and S2 based on Hilbert transform and adaptive thresholding approach

Local Area Augmentation System (LAAS) based on multi- constellation GNSS can provide improved accuracy, availability and integrity needed to support all weather category II and III precision approach landing of aircraft. In order to receive satellite signals of GNSS, an antenna working over wide frequency band and high phase center stability is preferred. Commonly used antennas like crossed dipoles, patch etc. are inherently narrow band. This paper describes the design and development of half-cardioid shaped dual arm, wide band printed circuit antenna. The antenna has low VSWR of < 3 : 1, a stable phase center and good right hand circularly polarized radiation patterns covering full L-band frequencies. The simulated and measured results compare well. This compact antenna can also be used on ground, ship and airborne platforms to receive signals from multiple GNSS satellites above the horizon.

Ashok Mondal

Papers

Detection of lungs status using morphological complexities of respiratory sounds.

Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique.

An automated tool for localization of heart sound components S1, S2, S3 and S4 in pulmonary sounds using Hilbert transform and Heron’s formula

Boundary estimation of cardiac events S1 and S2 based on Hilbert transform and adaptive thresholding approach

A wide band antenna for multi-constellation gnss and augmentation systems