Lung Sound Signals Research Articles

A Framework for Detecting Pulmonary Diseases from Lung Sound Signals Using a Hybrid Multi-Task Autoencoder-SVM Model

Research focuses on the efficacy of Multi-Task Autoencoder (MTAE) models in signal classification due to their ability to handle many tasks while improving feature extraction. However, researchers have not thoroughly investigated the study of lung sounds (LSs) for pulmonary disease detection. This paper introduces a new framework that utilizes an MTAE model to detect lung diseases based on LS signals. The model integrates an autoencoder and a supervised classifier, simultaneously optimizing both classification accuracy and signal reconstruction. Furthermore, we propose a hybrid approach that combines an MTAE and a Support Vector Machine (MTAE-SVM) to enhance performance. We evaluated our model using LS signals from a publicly available database from King Abdullah University Hospital. The model attained an accuracy of 89.47% for four classes (normal, pneumonia, asthma, and chronic obstructive pulmonary disease) and 90.22% for three classes (normal, pneumonia, and asthma cases). Using the MTAE-SVM, the accuracy was further improved to 91.49% for four classes and 93.08% for three classes, respectively. The results indicate that the MTAE and MTAE-SVM have a considerable potential for detecting pulmonary diseases from lung sound signals. This could aid in the creation of more user-friendly and effective diagnostic tools.

Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning.

Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds.

Research on heart and lung sound separation method based on DAE–NMF–VMD

Auscultation is the most effective method for diagnosing cardiovascular and respiratory diseases. However, stethoscopes typically capture mixed signals of heart and lung sounds, which can affect the auscultation effect of doctors. Therefore, the efficient separation of mixed heart and lung sound signals plays a crucial role in improving the diagnosis of cardiovascular and respiratory diseases. In this paper, we propose a blind source separation method for heart and lung sounds based on deep autoencoder (DAE), nonnegative matrix factorization (NMF) and variational mode decomposition (VMD). Firstly, DAE is employed to extract highly informative features from the heart and lung sound signals. Subsequently, NMF clustering is applied to group the heart and lung sounds based on their distinct periodicities, achieving the separation of the mixed heart and lung sounds. Finally, variational mode decomposition is used for denoising the separated signals. Experimental results demonstrate that the proposed method effectively separates heart and lung sound signals and exhibits significant advantages in terms of standardized evaluation metrics when compared to contrast methods.

Automatic breathing phase identification based on the second derivative of the recorded lung sounds

This paper presents a new method for automatic identification of the inspiratory and expiratory breathing phases in lung sound recordings. Adventitious lung sounds (wheezes and crackles), superimposed on the breath sounds, are generally an early indication of the disease, and their timing in the breathing cycle (early/mid/late inspiratory or expiratory) has clinical significance for monitoring or diagnosing disease. Therefore, the identification of the phases of the breathing cycle is an essential step for clinical interpretation of pulmonary auscultation. The proposed algorithm is designed to be robust in the presence of adventitious lung sounds or where the breath sounds may be noisy compared to healthy lung sounds. The algorithm uses the Savitzky & Golay (SG) filter to estimate the second derivative of the lung sound signal then calculates its normalized absolute value. A threshold value is used to clip any large amplitude peaks and, following low-pas filtering, the breathing phases are visible in the plotted signal. A rule-based approach to locating peaks and troughs is then used to identify inspirations and expirations. The performance of the method is evaluated using four different datasets: (a) a longitudinal dataset recorded from 19 subjects with a diagnosis of idiopathic pulmonary fibrosis (IPF), (b) a cross-sectional dataset recorded from 55 subjects who were referred for high-resolution computed tomography (HRCT) scan of the chest for various clinical indications, (c) a longitudinal dataset recorded from 10 healthy subjects, and (d) an open access lung sounds dataset containing recordings from 41 subjects with wheeze (9 with chronic obstructive pulmonary fibrosis and 32 with asthma). On average for inspiratory phase identification the algorithm had a sensitivity of (mean (standard deviation)) 92.84 (12.88)%, positive predictive value of 90.64(15.96)%, and F1-score of 90.67 (12.53)%. For identification of the expiratory phase, the algorithm had an average sensitivity of 92.19 (13.44)%, an average positive predictive value of 91.55 (15.23)%, and an average F1-score of 90.89 (12.45)%. The method shows good potential for automatic identification of breathing phases in recorded lung sounds.

Research on lung sound classification model based on dual-channel CNN-LSTM algorithm

ulmonary diseases have a significant impact on human health and life safety, and abnormalities in the lungs are a direct response to lung diseases. Establishing an effective lung sound classification model that can assist in diagnosis is of great significance for electronic auscultation.In addressing the issue of lung sound signal classification, this study introduces a deep learning classification model based on a dual-channel CNN-LSTM algorithm. Initially, Mel-scale Frequency Cepstral Coefficients (MFCC) are employed for feature extraction from the dataset, transforming lung sound signals into Mel spectrograms. On this foundation, a dual-channel algorithm classification model is constructed, with parallel Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) modules. The CNN module is designed to capture spatial dimension features of the input data, while the LSTM module focuses on temporal dimension features. These two feature sets are fused together, enabling the model to classify lung sounds and thereby assisting in diagnosing pulmonary diseases for healthcare practitioners. This experiment used the ICBHI2017 Challenge Lungs dataset and obtained 5054 pieces of data through data augmentation and sampling techniques.The results show that the accuracy, recall, and F1 score of this model reach 99.01%, 99.13%, and 0.9915, respectively, significantly superior to other models, highlighting its practical application value.

Elimination of heart sound from respiratory sound using adaptive variational mode decomposition for pulmonary diseases diagnosis

Lung sound (LS) signals are a vital source of information for the identification of pulmonary disorders. Heart sound (HS) is the most common contaminant of lung sounds during auscultation from the chest walls. This directly affects the efficiency of lung sound processing in diagnosing lung diseases. In this work, Adaptive Variational Mode Decomposition (AVMD) technique is proposed to remove heart sound contaminants from lung sounds. The proposed AVMD method initially breakdown the noisy lung sound signal into a collective of bandlimited modes called variational mode functions (VMF). Then, based on the frequency spectrum, the HS is filtered out from the LS. The real time lung sound data is collected from 95 participants and the performance of VMD technique is evaluated using the statistical metrics measures. Thus, the proposed topology exhibits Higher SNR (29.6587dB, lowest Root Mean Square (RMSE) of 0.0102, lowest normalized Mean Absolute Error (nMAE) of 0.0336, and highest percentage in correlation coefficient Factor (CCF) of 99.79% respectively. These experimental results are found to be superior and outperform all other recently proposed techniques.

HEART SOUND NOISE SEPARATION FROM LUNG SOUND BASED ON ENHANCED VARIATIONAL MODE DECOMPOSITION FOR DIAGNOSING PULMONARY DISEASES

Lung sound (LS) signals are vital for diagnosing pulmonary disorders. However, heart sound (HS) interferes with the analysis of LS, leading to the misdiagnosis of lung disorders. To address this issue, we propose an Enhanced Variational Mode Decomposition (E-VMD) technique to remove HS interference from LSs effectively. The E-VMD method automatically determines the mode number for signal decomposition based on the characteristics of variational mode functions (VMFs) such as normalized permutation entropy, kurtosis index, extreme frequency domain, and energy loss coefficient. The performance of the proposed denoising technique was evaluated using six performance metrics: Signal-to-noise ratio (SNR), root mean square error (RMSE), normalized mean absolute error (nMAE), correlation coefficient factor (CCF), CPU[Formula: see text], and CPU[Formula: see text]. In comparison to other denoising methods such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), singular spectrum analysis (SSA), and variational mode decomposition (VMD), the new E-VMD method demonstrates superior denoising outcome. The proposed method was evaluated using LS recorded from the outpatient department of Thoracic Medicine at Thanjavur Medical College and Hospital, Thanjavur. The obtained performance measures are as follows: RMSE: 0.02103 ± 0.00054, SNR: 28.52464 ± 0.00253, nMAE: 0.00009 ± 0.00056, CCF: 0.9962, CPU[Formula: see text]: 34.586, and CPU[Formula: see text]: 0.452 s. These results affirm the adaptability and robustness of the proposed method, even in the existence of HS noise. This method improves denoising accuracy and computational efficacy, making it a useful tool for improving the analysis of LS signals and assisting in medical diagnostics. This technique utilizes an electronic stethoscope, a common clinical device used by healthcare professionals for detecting lung disease.

Lung Sound Recognition Method Based on Multi-Resolution Interleaved Net and Time-Frequency Feature Enhancement.

Air pollution and aging population have caused increasing rates of lung diseases and elderly lung diseases year by year. At the same time, the outbreak of COVID-19 has brought challenges to the medical system, which placed higher demands on preventing lung diseases and improving diagnostic efficiency to some extent. Artificial intelligence can alleviate the burden on the medical system by analyzing lung sound signals to help to diagnose lung diseases. The existing models for lung sound recognition have challenges in capturing the correlation between time and frequency information. It is difficult for convolutional neural network to capture multi-scale features across different resolutions, and the fusion of features ignores the difference of influences between time and frequency features. To address these issues, a lung sound recognition model based on multi-resolution interleaved net and time-frequency feature enhancement was proposed, which consisted of a heterogeneous dual-branch time-frequency feature extractor (TFFE), a time-frequency feature enhancement module based on branch attention (FEBA), and a fusion semantic classifier based on semantic mapping (FSC). TFFE independently extracts the time and frequency information of lung sounds through a multi-resolution interleaved net and Transformer, which maintains the correlation between time-frequency features. FEBA focuses on the differences in the influence of time and frequency information on prediction results by branch attention. The proposed model achieved an accuracy of 91.56% on the combined dataset, by an improvement of over 2.13% compared to other models.

Automated Diagnosis of Pulmonary Diseases Using Lung Sound Signals

Pulmonary diseases are the third biggest cause of deaths worldwide. A prominent method to detect these diseases is the observation of lung sound signals. There is an increasing need for an efficient technique that can automatically diagnose such diseases with high accuracy. In this paper, two popular public datasets are considered, and every lung sound signal is decomposed into 8 frequency bands using rectangular zero-phase filters. Features are extracted from every band, including energy, kurtosis, mean absolute deviation and Lp norm. The extracted features are utilized for classification using machine learning schemes. The proposed method achieves 99.9% accuracy for multi-class classification on the combined dataset, and for binary classification, we have evaluated normal signal versus pathogenic signal, which is found to be 100% accurate for most of the diseases. High accuracy is obtained for the individual datasets as well. Top 20 features selected using minimum redundancy maximum relevance algorithm also yield 98.6% accuracy. Therefore, the proposed method can be easily deployed in real-time systems.

Restoration of Lung Sound Signals Using a Hybrid Wavelet-Based Approach

A unique and ideal integration of wavelet-based total variation (WATV) and empirical Wiener denoising method is proposed in this article to significantly enhance the signal-to-noise ratio (SNR) while preserving the characteristics of a lung sound signal. While individual wavelet-based denoising filters based on a single basis function have been employed in the past, the outcome has been unsatisfactory because only significant (signal) wavelet coefficients are considered for denoising analysis. The new wavelet-based empirical Wiener (WATV-Wiener) hybrid technique, proposed here, takes into account both significant and insignificant (noise) wavelet coefficients of the noisy signal. An intensive analysis of selecting and fine-tuning the WATV-Wiener filter parameters is presented here through the simulation studies. The WATV-Wiener filter applied here onto different 1-D lung sound signals of different noise levels has led to an optimal root mean square error (RMSE) compared with seven other state-of-the-art filters reported in the literature. The optimal parameters achieved through our simulation studies led to a 3–20-dB improvement in SNR, and the average SNR was improved by 4–30 dB in our experiment. We also observed that the WATV-Wiener filter is less sensitive to the variation of SNR values of the input signal. Furthermore, the WATV-Wiener filter obtains similar SNR performance between continuous piecewise signal (wheeze) and noncontinuous piecewise signal (crackle) in both simulation and experimental studies.

Noise Reduction of Lung Sounds based on Singular Spectrum Analysis combined with Discrete Cosine Transform

Lung sound signals are sensitive to environmental noise. Much research has been conducted on the enhancement of pulmonary sound. This study investigated the normal lung sounds as bronchovesicular (BV) and vesicular (V) signals and proposed a novel denoising method called SSA-DCT. Using Singular Spectrum Analysis (SSA), the noise-related components were separated from respiratory information components. An algorithm was also proposed to determine the information and noise time interval, and by applying Discrete Cosine Transform (DCT), the signal energy was attenuated in the noise intervals. Moreover, the concept of safety component, which is secure against noise, was introduced. Then, an algorithm for automatic identification of BV and V signals using the safety component was presented. The error of this algorithm at SNR of 10 dB was 5%. Lung sounds were recorded from 12 healthy subjects using four channels over the posterior chest wall. The signals were recorded in an acoustic laboratory and then contaminated with additive white Gaussian noise with different levels of SNR. The respiratory signals were also recorded in a relatively quiet environment with real ambient noise and denoised by the proposed method. The proposed method was compared with Coiflet wavelet decomposition with hard SureShrink thresholding. The denoising performance of both methods was evaluated using qualitative and quantitative approaches. The SSA-DCT method (e.g., at an SNR level of 10 dB) with average segmental SNR improvements of 2.52 and 3.44 dB for the BV and V signals, respectively, is significantly superior to the wavelet analysis with average segmental SNR improvements of 0.89 and 1.53 dB for the BV and V signals, respectively.

A comparative study of the spectrogram, scalogram, melspectrogram and gammatonegram time-frequency representations for the classification of lung sounds using the ICBHI database based on CNNs.

In lung sound classification using deep learning, many studies have considered the use of short-time Fourier transform (STFT) as the most commonly used 2D representation of the input data. Consequently, STFT has been widely used as an analytical tool, but other versions of the representation have also been developed. This study aims to evaluate and compare the performance of the spectrogram, scalogram, melspectrogram and gammatonegram representations, and provide comparative information to users regarding the suitability of these time-frequency (TF) techniques in lung sound classification. Lung sound signals used in this study were obtained from the ICBHI 2017 respiratory sound database. These lung sound recordings wereconverted into images of spectrogram, scalogram, melspectrogram and gammatonegram TF representations respectively. The four types of images were fed separately into the VGG16, ResNet-50 and AlexNet deep-learning architectures. Network performances were analyzed and compared based on accuracy, precision, recall and F1-score. The results of the analysis on the performance of the four representations using these three commonly used CNN deep-learning networks indicate that the generated gammatonegram and scalogram TF images coupled with ResNet-50 achieved maximum classification accuracies.

A temporal dependency feature in lower dimension for lung sound signal classification

Respiratory sounds are expressed as nonlinear and nonstationary signals, whose unpredictability makes it difficult to extract significant features for classification. Static cepstral coefficients such as Mel-frequency cepstral coefficients (MFCCs), have been used for classification of lung sound signals. However, they are modeled in high-dimensional hyperspectral space, and also lose temporal dependency information. Therefore, we propose shifted delta -cepstral coefficients in lower-subspace (SDC-L) as a novel feature for lung sound classification. It preserves temporal dependency information of multiple frames nearby same to original SDC, and improves feature extraction by reducing the hyperspectral dimension. We modified EMD algorithm by adding a stopping rule to objectively select a finite number of intrinsic mode functions (IMFs). The performances of SDC-L were evaluated with three machine learning techniques (support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF)) and two deep learning algorithms (multilayer perceptron (MLP) and convolutional neural network (cNN)) and one hybrid deep learning algorithm combining cNN with long short term memory (LSTM) in terms of accuracy, precision, recall and F1-score. We found that the first 2 IMFs were enough to construct our feature. SVM, MLP and a hybrid deep learning algorithm (cNN plus LSTM) outperformed with SDC-L, and the other classifiers achieved equivalent results with all features. Our findings show that SDC-L is a promising feature for the classification of lung sound signals.

Automated Detection of Pulmonary Diseases From Lung Sound Signals Using Fixed-Boundary-Based Empirical Wavelet Transform

In this letter, a promising method is proposed to automatically detect pulmonary diseases (PDs) from lung sound (LS) signals. The modes of the LS signal are evaluated using empirical wavelet transform with fixed boundary points. The time-domain (Shannon entropy) and frequency-domain (peak amplitude and peak frequency) features have been extracted from each mode. The classifiers, such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LGBM), have been chosen to detect PDs using the features of LS signals automatically. The performance of the proposed method has been evaluated using LS signals obtained from a publicly available database. The detection accuracy values, such as 80.35, 83.27, 99.34, and 77.13%, have been obtained using the LGBM classifier with fivefold cross validation for normal versus asthma, normal versus pneumonia, normal versus chronic obstructive pulmonary disease (COPD), and normal versus pneumonia versus asthma versus COPD classification schemes. For the normal versus pneumonia versus asthma classification scheme, the proposed method has achieved an accuracy value of 84.76%, which is higher than that of the existing approaches using LS signals.

Embedded system design for classification of COPD and pneumonia patients by lung sound analysis.

Chronic obstructive pulmonary disease (COPD) and pneumonia are lethal pulmonary illnesses with equivocal nature of abnormal pulmonic acoustics. Using lung sound signals, the classification of pulmonary abnormalities is a difficult task. A standalone system was conceived for screening COPD and Pneumonia patients through signal processing and machine learning methodologies. The proposed system will assist practitioners and pulmonologists in the accurate classification of disease. In this research work, ICBHI's and self-collected lung sound (LS) databases are used to investigate COPD and pneumonia patient. In this scheme, empirical mode decomposition (EMD), discrete wavelet transform (DWT), and analysis of variance (ANOVA) techniques are employed for segmentation, noise elimination, and feature selection, respectively. To overcome the inherent limitation of ICBHI's LS database, the adaptive synthetic (ADASYN) sampling technique is used to eradicate class imbalance. Lung sound features are used to train fine Gaussian support vector machine (FG-SVM) for classification of COPD, pneumonia, and heathy healthy subjects. This machine learning scheme is implemented on low cost and portable Raspberry pi 3 model B+ (Cortex-A53 (ARMv8) 64-bit SoC @ 1.4GHz through hardware-supported language. Resultant hardware is capable of screening COPD and pneumonia patients accurately and assist health professionals.

Acquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases.

PurposeLung diseases are the third leading cause of death worldwide. Stethoscope-based auscultation is the most commonly used, non-invasive, inexpensive, and primary diagnostic approach for assessing lung conditions. However, the manual auscultation-based diagnosis procedure is prone to error, and its accuracy is dependent on the physician’s experience and hearing capacity. Moreover, the stethoscope recording is vulnerable to different noises that can mask the important features of lung sounds which may lead to misdiagnosis. In this paper, a method for the acquisition of lung sound signals and classification of the top 7 lung diseases has been proposed for improving the efficacy of auscultation diagnosis of pulmonary disease.MethodsAn electronic stethoscope has been constructed for signal acquisition. Lung sound signals were then collected from people with COPD, upper respiratory tract infections (URTI), lower respiratory tract infections (LRTI), pneumonia, bronchiectasis, bronchiolitis, asthma, and healthy people. Lung sounds were analyzed using a wavelet multiresolution analysis. To choose the most relevant features, feature selection using one-way ANOVA was performed. The classification accuracy of various machine learning classifiers was compared, and the Fine Gaussian SVM was chosen for final classification due to its superior performance. Model optimization was accomplished through the application of Bayesian optimization techniques.ResultsA test classification accuracy of 99%, specificity of 99.2%, and sensitivity of 99.04%, have been achieved for the 7 lung diseases using the optimized Fine Gaussian SVM classifier.ConclusionOur experimental results demonstrate that the proposed method has the potential to be used as a decision support system for the classification of lung diseases, especially in those areas where the expertise and the means are limited.

Unveiling the potential of phase portrait-based recurrence network: a revelation through lung sound analysis

Abstract The work reported in this article introduces the novel concept of phase portrait-based recurrence network (RN) analysis in the digital auscultation of expiratory wheeze (ES) and vesicular (VS), with its potential revealed through machine learning techniques (MLTs). The time-series lung sound signals of ES and VS, subjected to power spectral density analysis, revealed information regarding the morphology of the respiratory tract responsible for the generation of signature frequency components. Having constructed the complex network using Pearson’s linear correlation coefficient ($P_{a,b}$) and employing the graph features for classification by principal component analysis (PCA), it is understood that the graph features thus obtained are incapable of classifying the two signals. Hence, a novel method, recurrence network, of constructing a network from the phase portrait of the time series is employed to deduce the network features. The MLTs, K-nearest neighbour (KNN) and PCA, are found to give better classification when RN topological features are used. When PCA separates the two signals with 84.7% total variance between the principal components, KNN yields 100% prediction accuracy. Thus, the study unveils the potential of RN over $P_{a,b}$-based complex network in classifying the lung sound signals ES and VS and thereby opening the possibility of employing the technique in digital auscultation, a best-suited one for the time of the widespread pandemic coronavirus disease-2019.

Lung sound signal denoising using discrete wavelet transform and artificial neural network

Computerized analysis of Lung Sound (LS) is a promising method for assessing pulmonary function. However, the LS signal is severely contaminated by background noise from various sources. Conventional denoising methods may not be practical due to the noisy nature of the LS as well as its spectral overlap with different noise sources. This paper proposes an adaptive technique based on Discrete Wavelet Transform and Artificial Neural Network (DWT-ANN) to filtrate LS signals in a noisy environment. This new method mixes the multi-resolution property of DWT with ANN as a nonlinear adaptive filter. In this research, separate models for signal denoising with different SNRs (0, 5, 10, and 15 dB) were designed. Then a single model was introduced as a combined model to eliminate any information about input signals SNR before the denoising process. The results showed that the combined model, in addition to having a close performance to the individual models, could perform the denoising process well in the range of −2 to 20 dB, which is outside the range that the model has been trained with. In addition, comparing the results of our proposed method with the DWT method, it was observed that the SNR of the denoised signal was significantly enhanced. At SNR = 0 dB, this improvement is 9.18 compared to only 3.90 using the DWT method.

Crackle and wheeze detection in lung sound signals using convolutional neural networks.

Respiratory diseases are among the leading causes of death worldwide. Preventive measures are essential to avoid and increase the odds of a successful recovery. An important screening tool is pulmonary auscultation, an inexpensive, noninvasive and safe method to assess the mechanics and dynamics of the lungs. On the other hand, it is a difficult task for a human listener since some lung sound events have a spectrum of frequencies outside of the human hearing ability. Thus, computer assisted decision systems might play an important role in the detection of abnormal sounds, such as crackle or wheeze sounds. In this paper, we propose a novel system, which is not only able to detect abnormal lung sound events, but it is also able to classify them. Furthermore, our system was trained and tested using the publicly available ICBHI 2017 challenge dataset, and using the metrics proposed by the challenge, thus making our framework and results easily comparable. Using a Mel Spectrogram as an input feature for our convolutional neural network, our system achieved results in line with the current state of the art, an accuracy of 43%, and a sensitivity of 51%.

Heart and Lung Sound Measurement Using an Esophageal Stethoscope with Adaptive Noise Cancellation.

In surgeries where general anesthesia is required, the auscultation of heart and lung sounds is essential to provide information on the patient’s cardiorespiratory system. Heart and lung sounds can be recorded using an esophageal stethoscope; however, there is huge background noise when this device is used in an operating room. In this study, a digital esophageal stethoscope system was designed. A 3D-printed case filled with Polydimethylsiloxane material was designed to hold two electret-type microphones. One of the microphones was placed inside the printed case to collect the heart and lung sound signals coming out from the patient through the esophageal catheter, the other was mounted on the surface of the case to collect the operating room sounds. A developed adaptive noise canceling algorithm was implemented to remove the operating room noise corrupted with the main heart and lung sound signals and the output signal was displayed on software application developed especially for this study. Using the designed case, the noise level of the signal was reduced to some extent, and by adding the adaptive filter, further noise reduction was achieved. The designed system is lightweight and can provide noise-free heart and lung sound signals.