Exploring the link between a novel approach for computer aided lung sound analysis and imaging biomarkers: a cross-sectional study.

BACKGROUND: Computer Aided Lung Sound Analysis (CALSA) aims to overcome limitations associated with standard lung auscultation by removing the subjective component and allowing quantification of sound characteristics. In this proof-of-concept study, a novel automated approach was evaluated in real patient data by comparing lung sound characteristics to structural and functional imaging biomarkers. METHODS: Patients with cystic fibrosis (CF) aged > 5y were recruited in a prospective cross-sectional study. CT scans were analyzed by the CF-CT scoring method and Functional Respiratory Imaging (FRI). A digital stethoscope was used to record lung sounds at six chest locations. Following sound characteristics were determined: expiration-to-inspiration (E/I) signal power ratios within different frequency ranges, number of crackles per respiratory phase and wheeze parameters. Linear mixed-effects models were computed to relate CALSA parameters to imaging biomarkers on a lobar level. RESULTS: 222 recordings from 25 CF patients were included. Significant associations were found between E/I ratios and structural abnormalities, of which the ratio between 200 and 400 Hz appeared to be most clinically relevant due to its relation with bronchiectasis, mucus plugging, bronchial wall thickening and air trapping on CT. The number of crackles was also associated with multiple structural abnormalities as well as regional airway resistance determined by FRI. Wheeze parameters were not considered in the statistical analysis, since wheezing was detected in only one recording. CONCLUSIONS: The present study is the first to investigate associations between auscultatory findings and imaging biomarkers, which are considered the gold standard to evaluate the respiratory system. Despite the exploratory nature of this study, the results showed various meaningful associations that highlight the potential value of automated CALSA as a novel non-invasive outcome measure in future research and clinical practice.

Breath Measurement Method for Synchronized Reproduction of Biological Tones in an Augmented Reality Auscultation Training System.

An educational augmented reality auscultation system (EARS) is proposed to enhance the reality of auscultation training using a simulated patient. The conventional EARS cannot accurately reproduce breath sounds according to the breathing of a simulated patient because the system instructs the breathing rhythm. In this study, we propose breath measurement methods that can be integrated into the chest piece of a stethoscope. We investigate methods using the thoracic variations and frequency characteristics of breath sounds. An accelerometer, a magnetic sensor, a gyro sensor, a pressure sensor, and a microphone were selected as the sensors. For measurement with the magnetic sensor, we proposed a method by detecting the breathing waveform in terms of changes in the magnetic field accompanying the surface deformation of the stethoscope based on thoracic variations using a magnet. During breath sound measurement, the frequency spectra of the breath sounds acquired by the built-in microphone were calculated. The breathing waveforms were obtained from the difference in characteristics between the breath sounds during exhalation and inhalation. The result showed the average value of the correlation coefficient with the reference value reached 0.45, indicating the effectiveness of this method as a breath measurement method. And the evaluations suggest more accurate breathing waveforms can be obtained by selecting the measurement method according to breathing method and measurement point.

SonicGuard Sensor-A Multichannel Acoustic Sensor for Long-Term Monitoring of Abdominal Sounds Examined through a Qualification Study.

Auscultation is a fundamental diagnostic technique that provides valuable diagnostic information about different parts of the body. With the increasing prevalence of digital stethoscopes and telehealth applications, there is a growing trend towards digitizing the capture of bodily sounds, thereby enabling subsequent analysis using machine learning algorithms. This study introduces the SonicGuard sensor, which is a multichannel acoustic sensor designed for long-term recordings of bodily sounds. We conducted a series of qualification tests, with a specific focus on bowel sounds ranging from controlled experimental environments to phantom measurements and real patient recordings. These tests demonstrate the effectiveness of the proposed sensor setup. The results show that the SonicGuard sensor is comparable to commercially available digital stethoscopes, which are considered the gold standard in the field. This development opens up possibilities for collecting and analyzing bodily sound datasets using machine learning techniques in the future.

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study.

INTRODUCTION: Mitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for large-scale MR screening. Cardiac auscultation with electronic stethoscope and artificial intelligence (AI) can be a fast and economical modality for assessing MR severity. Our objectives are (1) to establish a deep neural network (DNN)-based cardiac auscultation method for assessing the severity of MR; and (2) to quantitatively measure the performance of the developed AI-based MR assessment method by virtual clinical trial. METHODS AND ANALYSIS: In a cross-sectional design, phonocardiogram will be recorded at the mitral valve auscultation area of outpatients. The enrolled patients will be checked by echocardiography to confirm the diagnosis of MR or no MR. Echocardiographic parameters will be used as gold standard to assess the severity of MR, classified into four levels: none, mild, moderate and severe. The study consists of two stages. First, an MR-related cardiac sound database will be created on which a DNN-based MR severity classifier will be trained. The automatic MR severity classifier will be integrated with the Smartho-D2 electronic stethoscope. Second, the performance of the developed smart device will be assessed in an independent clinical validation data set. Sensitivity, specificity, precision, accuracy and F1 score of the developed smart MR assessment device will be evaluated. Agreement on the performance of the smart device between cardiologist users and patient users will be inspected. The interpretability of the developed model will also be studied with statistical comparisons of occlusion map-guided variables among the four severity groups. ETHICS AND DISSEMINATION: The study protocol was approved by the Medical Ethics Committee of Huzhou Central Hospital, China (registration number: 202302009-01). Informed consent is required from all participants. Dissemination will be through conference presentations and peer-reviewed journals. TRIAL REGISTRATION NUMBER: ChiCTR2300069496.

Frequent POCUS and auscultation for an earlier diagnosis of takotsubo syndrome and unraveling of its pathophysiology: The possible crucial role of LVOTO.

There is ample literature associating LVOTO with hypertension, AMI, LV hypertrophy, sigmoid septum, HCM, and TTS, particularly in midde aged/elderly/postmenopausal women, suggestive of a causal role for LVOTO in the pathophysiology of TTS. Although there is significant evidence that TTS is triggered by a sudden autonomic sympathetic nervous system surge and/or elevated blood-ridden catecholamines, the exact pathophysiologic trajectory leading to the clinical expression of the disease is still being debated. This review expounds on the possibility that LVOTO is a causal early component of this trajectory, and proposes that TTS is a malady within the broad spectrum of the myocardial ischemic injury/stunned myocardium states. The postulated underlying mechanism by which LVOTO causes TTS is a sudden abterload rise, with resultant oxygen/energy supply/demand mismatch, leading to a transient myocardial ischemia/injury myocardial stunning state. This needs to be explored painstakingly, and this review includes some suggestions for such undertaking. Ellucidation of the pathophysiology of TTS, and possible proof about a mechanistic role of LVOTO, may ensure that our current pharmacological and device panoply is adequate for the management of TTS.

Fetal Heart Rate Auscultation, 4th Edition.

Intermittent auscultation (IA) is an evidence-based method of fetal surveillance during labor for birthing people with low-risk pregnancies. It is a central component of efforts to reduce the primary cesarean rate and promote vaginal birth (American College of Obstetricians and Gynecologists, 2019; Association of Women's Health, Obstetric and Neonatal Nurses, 2022a). The use of intermittent IA decreased with the introduction of electronic fetal monitoring, while the increased use of electronic fetal monitoring has been associated with an increase of cesarean births. This practice monograph includes information on IA techniques; interpretation and documentation; clinical decision-making and interventions; communication; education, staffing, legal issues; and strategies to implement IA.

Accurate detection of Korotkoff sounds reveals large discrepancy between intra-arterial systolic pressure and simultaneous noninvasive measurement of blood pressure with brachial cuff sphygmomanometry.

Cardiovascular disease is the number 1 cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. Noninvasive cuff-based automated monitoring is now the dominant method for BP measurement and irrespective of whether the oscillometric or the auscultatory method is used, all are calibrated according to the Universal Standard (ISO 81060-2:2019), which requires two trained operators to listen to Korotkoff K1 sounds for SBP and K4/K5 sounds for DBP. Hence, Korotkoff sounds are fundamental to the calibration of all NIBP devices. In this study of 40 lightly sedated patients, aged 64.1 ±â€Š9.6 years, we compare SBP and DBP recorded directly by intra-arterial fluid filled catheters to values recorded from the onset (SBP-K) and cessation (DBP-K) of Korotkoff sounds. We demonstrate that whilst DBP-K measurements are in good agreement, with a mean difference of -0.3 ±â€Š5.2 mmHg, SBP-K underestimates true intra-arterial SBP (IA-SBP) by an average of 14 ±â€Š9.6 mmHg. The underestimation arises from delays in the re-opening of the brachial artery following deflation of the brachial cuff to below SBP. The reasons for this delay are not known but appear related to the difference between SBP and the pressure under the cuff as blood first begins to flow, as the cuff deflates. Linear models are presented that can correct the underestimation in SBP resulting in estimates with a mean difference of 0.2 ±â€Š7.1 mmHg with respect to intra-arterial SBP.

Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms.

One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary markedly in their ability to detect and interpret pathological patterns. Moreover, echo is still causing cost difficulties for low- and middle-income countries. Here, we developed a deep learning-based attention transformer model to automate the detection of heart murmurs caused by CHD at an early stage of life using cost-effective and widely available phonocardiography (PCG). PCG recordings were obtained from 942 young patients at four major auscultation locations, including the aortic valve (AV), mitral valve (MV), pulmonary valve (PV), and tricuspid valve (TV), and they were annotated by experts as absent, present, or unknown murmurs. A transformation to wavelet features was performed to reduce the dimensionality before the deep learning stage for inferring the medical condition. The performance was validated through 10-fold cross-validation and yielded an average accuracy and sensitivity of 90.23 % and 72.41 %, respectively. The accuracy of discriminating between murmurs' absence and presence reached 76.10 % when evaluated on unseen data. The model had accuracies of 70 %, 88 %, and 86 % in predicting murmur presence in infants, children, and adolescents, respectively. The interpretation of the model revealed proper discrimination between the learned attributes, and AV channel was found important (score 0.75) for the murmur absence predictions while MV and TV were more important for murmur presence predictions. The findings potentiate deep learning as a powerful front-line tool for inferring CHD status in PCG recordings leveraging early detection of heart anomalies in young people. It is suggested as a tool that can be used independently from high-cost machinery or expert assessment.

Breath characteristics and adventitious lung sounds in healthy and asthmatic horses.

BACKGROUND: Standard thoracic auscultation suffers from limitations, and no systematic analysis of breath sounds in asthmatic horses exists. OBJECTIVES: First, characterize breath sounds in horses recorded using a novel digital auscultation device (DAD). Second, use DAD to compare breath variables and occurrence of adventitious sounds in healthy and asthmatic horses. ANIMALS: Twelve healthy control horses (ctl), 12 horses with mild to moderate asthma (mEA), 10 horses with severe asthma (sEA) (5 in remission [sEA-], and 5 in exacerbation [sEA+]). METHODS: Prospective multicenter case-control study. Horses were categorized based on the horse owner-assessed respiratory signs index. Each horse was digitally auscultated in 11 locations simultaneously for 1 hour. One-hundred breaths per recording were randomly selected, blindly categorized, and statistically analyzed. RESULTS: Digital auscultation allowed breath sound characterization and scoring in horses. Wheezes, crackles, rattles, and breath intensity were significantly more frequent, higher (P < .001, P < .01, P = .01, P < .01, respectively) in sEA+ (68.6%, 66.1%, 17.7%, 97.9%, respectively), but not in sEA- (0%, 0.7%, 1.3%, 5.6%) or mEA (0%, 1.0%, 2.4%, 1.7%) horses, compared to ctl (0%, 0.6%, 1.8%, -9.4%, respectively). Regression analysis suggested breath duration and intensity as explanatory variables for groups, wheezes for tracheal mucus score, and breath intensity and wheezes for the 23-point weighted clinical score (WCS23). CONCLUSIONS AND CLINICAL IMPORTANCE: The DAD permitted characterization and quantification of breath variables, which demonstrated increased adventitious sounds in sEA+. Analysis of a larger sample is needed to determine differences among ctl, mEA, and sEA- horses.

Exploring classical machine learning for identification of pathological lung auscultations.

The use of machine learning in biomedical research has surged in recent years thanks to advances in devices and artificial intelligence. Our aim is to expand this body of knowledge by applying machine learning to pulmonary auscultation signals. Despite improvements in digital stethoscopes and attempts to find synergy between them and artificial intelligence, solutions for their use in clinical settings remain scarce. Physicians continue to infer initial diagnoses with less sophisticated means, resulting in low accuracy, leading to suboptimal patient care. To arrive at a correct preliminary diagnosis, the auscultation diagnostics need to be of high accuracy. Due to the large number of auscultations performed, data availability opens up opportunities for more effective sound analysis. In this study, digital 6-channel auscultations of 45 patients were used in various machine learning scenarios, with the aim of distinguishing between normal and abnormal pulmonary sounds. Audio features (such as fundamental frequencies F0-4, loudness, HNR, DFA, as well as descriptive statistics of log energy, RMS and MFCC) were extracted using the Python library Surfboard. Windowing, feature aggregation, and concatenation strategies were used to prepare data for machine learning algorithms in unsupervised (fair-cut forest, outlier forest) and supervised (random forest, regularized logistic regression) settings. The evaluation was carried out using 9-fold stratified cross-validation repeated 30 times. Decision fusion by averaging the outputs for a subject was also tested and found to be helpful. Supervised models showed a consistent advantage over unsupervised ones, with random forest achieving a mean AUC ROC of 0.691 (accuracy 71.11%, Kappa 0.416, F1-score 0.675) in side-based detection and a mean AUC ROC of 0.721 (accuracy 68.89%, Kappa 0.371, F1-score 0.650) in patient-based detection.

AI diagnosis of heart sounds differentiated with super StethoScope.

In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide. Although the number of effective treatment options has increased in recent years, the lack of effective screening methods is provoking continued high mortality and rehospitalization rates. Appropriately, auscultation has been the primary option for screening such patients, however, challenges arise due to the variability in auscultation skills, the objectivity of the clinical method, and the presence of sounds inaudible to the human ear. To address challenges associated with the current approach towards auscultation, the hardware of Super StethoScope was developed. This paper is composed of (1) a background literature review of bioacoustic research regarding heart disease detection, (2) an introduction of our approach to heart sound research and development of Super StethoScope, (3) a discussion of the application of remote auscultation to telemedicine, and (4) results of a market needs survey on traditional and remote auscultation. Heart sounds and murmurs, if collected properly, have been shown to closely represent heart disease characteristics. Correspondingly, the main characteristics of Super StethoScope include: (1) simultaneous collection of electrocardiographic and heart sound for the detection of heart rate variability, (2) optimized signal-to-noise ratio in the audible frequency bands, and (3) acquisition of heart sounds including the inaudible frequency ranges. Due to the ability to visualize the data, the device is able to provide quantitative results without disturbance by sound quality alterations during remote auscultations. An online survey of 3648 doctors confirmed that auscultation is the common examination method used in today's clinical practice and revealed that artificial intelligence-based heart sound analysis systems are expected to be integrated into clinicians' practices. Super StethoScope would open new horizons for heart sound research and telemedicine.

New Perspectives on Non-invasive Blood Pressure Measurement.

Noninvasive blood pressure (NIBP) devices are calibrated against validated auscultation sphygmomanometers using Korotkoff sounds. This study aimed to investigate the timing of Korotkoff sounds in relation to pulse appearance in the brachial artery and values of intra-arterial blood pressure. Experiments were carried out on 15 participants, (14 males, 64.3 ± 10.4 years; one female, 86 yo), undergoing coronary angiography. A conventional occluding cuff, with a microphone for Korotkoff sounds, was placed on the upper arm (on the brachial artery). Intra-arterial blood pressure (IABP) was measured below the cuff with a fluid-filled catheter inserted via the radial artery and an external transducer. Finger photoplethysmography was used to measure brachial pulse wave velocity (PWV). Korotkoff sounds were processed electronically and custom algorithms identified the cuff pressure (CP) at which the first and last Korotkoff sounds were heard. PWV and max slope of the IABP pressure pulse were recorded to estimate arterial stiffness. The brachial artery closed at a CP of 132.0 ± 17.1 mmHg. Systolic and diastolic blood pressure (SBP and DBP) were 147.6 ± 14.3 and 72.7 ± 10.1 mmHg; mean pressure (MP, 100.1 ± 10.4 mmHg) was similar to MP derived from the peak of the oscillogram (98.5 ± 13.6 mmHg). Difference between IABP and CP recorded at first and last occurrence of Korotkoff sounds were, SBP: 19.0 ± 8.3 (range 2-29) mmHg, DBP: 4.0 ± 4.3 (range 2-12) mmHg. SBP derived from the onset of Korotkoff sounds can underestimate IABP by up to 19 mmHg. Since Korotkoff sounds are the recommended method mandated by the universal standard for the validation of blood pressure measuring devices, these errors are propagated through to all NIBP measurement devices irrespective of whether they use auscultatory or oscillometric methods.

Exploration of acoustical features reflecting bowel motion using simultaneous measurements of X-ray fluoroscopy and bowel sounds.

In clinical practice, bowel sounds are often used to assess bowel motility. However, the mechanism of bowel-sound occurrence is unknown. Furthermore, there is no objective evidence indicating a relationship between bowel motility and bowel sounds, and diagnoses have been based on empirically established criteria. In this study, simultaneous X-ray fluoroscopy and bowel-sound measurements were used to reveal the mechanism of bowel-sound occurrence. The results indicate that the flow of luminal contents may cause bowel sounds. Additionally, on the basis of the hypothesis that bowel motility recovers with the postoperative course, bowel-sound features that reflect bowel motion were explored, revealing that the current diagnosis indices are appropriate.

Constrained Synthetic Sampling for Augmentation of Crackle Lung Sounds.

Crackles are explosive breathing patterns caused by lung air sacs filling with fluid and act as an indicator for a plethora of pulmonary diseases. Clinical studies suggest a strong correlation between the presence of these adventitious auscultations and mortality rate, especially in pediatric patients, underscoring the importance of their pathological indication. While clinically important, crackles occur rarely in breathing signals relative to other phases and abnormalities of lung sounds, imposing a considerable class imbalance in developing learning methodologies for automated tracking and diagnosis of lung pathologies. The scarcity and clinical relevance of crackle sounds compel a need for exploring data augmentation techniques to enrich the space of crackle signals. Given their unique nature, the current study proposes a crackle-specific constrained synthetic sampling (CSS) augmentation that captures the geometric properties of crackles across different projected object spaces. We also outline a task-agnostic validation methodology that evaluates different augmentation techniques based on their goodness of fit relative to the space of original crackles. This evaluation considers both the separability of the manifold space generated by augmented data samples as well as a statistical distance space of the synthesized data relative to the original. Compared to a range of augmentation techniques, the proposed constrained-synthetic sampling of crackle sounds is shown to generate the most analogous samples relative to original crackle sounds, highlighting the importance of carefully considering the statistical constraints of the class under study.

Improving Valvular Pathologies and Ventricular Dysfunction Diagnostic Efficiency Using Combined Auscultation and Electrocardiography Data: A Multimodal AI Approach.

Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography. An independent cohort of 103 patients was used for clinical validation. These patients were screened for severe aortic stenosis (AS), severe mitral regurgitation (MR), and left ventricular dysfunction (LVD) with ejection fractions ≤ 40%. Optimal neural networks were identified by a fourfold cross-validation training process using heart sounds and various ECG leads, and their outputs were combined using a stacking technique. This composite sensor model had high diagnostic efficiency (area under the receiver operating characteristic curve (AUC) values: AS, 0.93; MR, 0.80; LVD, 0.75). Notably, the contribution of individual sensors to disease detection was found to be disease-specific, underscoring the synergistic potential of the sensor fusion approach. Thus, machine learning models that integrate auscultation and ECG can efficiently detect conditions typically diagnosed via imaging. Moreover, this study highlights the potential of multimodal artificial intelligence applications.

Performance of the Aktiia optical blood pressure measurement device in the elderly: a comparison with double blinded auscultation in different body positions.

BACKGROUND: Accurate blood pressure (BP) measurement is essential for the correct diagnosis and management of hypertension (HTN) especially in the elderly population. As with of all BP devices, the accuracy of cuffless devices must be verified. This study (NCT04027777) aimed to evaluate the performance of a wrist cuffless optical BP device in an elderly population cohort in different body positions with auscultation as the reference measurement. DESIGN AND METHODS: Patients aged 65-85 years with different BP categories but without diabetes were recruited. After an initial calibration based on auscultatory measurements, BP estimation from the Aktiia Bracelet (Aktiia SA, Switzerland) were compared to reference double-blinded auscultatory measurements in sitting, standing and lying positions on four separate visits distributed over one month. In the absence of a universal standard for cuffless BP device at the time of the study, modified ISO81060-2 criteria were used for performance analysis. RESULTS: Thirty-five participants were included in the analysis fulfilling the inclusion requirements of ISO 81060-2. A total of 469 paired measurements were obtained with overall 83% acceptance rate. Differences (mean ± SD)   between Aktiia Bracelet and auscultation for systolic BP were -0.26 ± 9.96 mmHg for all body positions aggregated (sitting 1.23 ± 7.88 mmHg, standing -1.81 ± 11.11 mmHg, lying -1.8 ± 9.96 mmHg). Similarly, differences for diastolic BP were -0.75 ± 7.0 mmHg (0.2 ± 5.55 mmHg, -5.35 ± 7.75 mmHg and -0.94 ± 7.47 mmHg, respectively). Standard deviation of the averaged differences per subject for systolic/diastolic BP was 3.8/2.5 mmHg in sitting and 4.4/3.7 mmHg for all body positions aggregated. CONCLUSIONS: Overall, this study demonstrates a similar performance of the Aktiia Bracelet compared to auscultation in an elderly population in body positions representative of daily activities. The use of more comfortable, non-invasive, and non-occlusive BP monitors during long periods may facilitate e-health and may contribute to better management of HTN, including diagnosis and treatment of HTN, in the elderly.

Accuracy of blood pressure measurements is essential in the diagnosis and the follow-up of patients with high blood pressure. As with any blood pressure measuring device, a validation is necessary. In this study including a elderly population, we compared values obtained by the cuffless Aktiia Bracelet (Aktiia SA, Switzerland) after an initial calibration with the reference auscultatory method during four separate study days distributed over one month. We show that the accuracy of the Aktiia Bracelet is similar to auscultation. The accuracy varies depending on the position in which the measurement is performed. Overall, the accuracy is not modified by a higher age category. The use of a cuffless device in the elderly population characterized by high prevalence of hypertension may facilitate the follow-up of blood pressure with more comfort and minimal constraints.

Hamman's syndrome - with audio file recording Hamman's sign.

A boy in his late adolescence, with no history of airway disease or medication use, presented with acute history of non-exertional chest pain increased on coughing and deep inspiration accompanied by dysphonia and odynophagia in the last 1 day. He had a notable history of viral fever with non-productive cough 2 weeks prior, which resolved spontaneously. Examination revealed stable haemodynamic parameters. Palpable non-tender crepitus was felt in left anterior chest wall, axilla and both sides of the neck. Auscultation revealed Hamman's sign. ECG showed high voltage complexes and 2-dimensional echocardiogram (2D ECHO) showed normal biventricular function. CXR was evident of subcutaneous emphysema, pneumopericardium and Naclerio's sign clinching the diagnosis of pneumomediastinum. CT findings were consistent with a diagnosis of Hamman's syndrome. Patient was admitted for observation and treated with high-flow oxygen. He improved symptomatically and was discharged on the fourth day of admission.

Heart Sound Classification Network Based on Convolution and Transformer.

Electronic auscultation is vital for doctors to detect symptoms and signs of cardiovascular diseases (CVDs), significantly impacting human health. Although progress has been made in heart sound classification, most existing methods require precise segmentation and feature extraction of heart sound signals before classification. To address this, we introduce an innovative approach for heart sound classification. Our method, named Convolution and Transformer Encoder Neural Network (CTENN), simplifies preprocessing, automatically extracting features using a combination of a one-dimensional convolution (1D-Conv) module and a Transformer encoder. Experimental results showcase the superiority of our proposed method in both binary and multi-class tasks, achieving remarkable accuracies of 96.4%, 99.7%, and 95.7% across three distinct datasets compared with that of similar approaches. This advancement holds promise for enhancing CVD diagnosis and treatment.

Transformer-Based Network for Accurate Classification of Lung Auscultation Sounds.

Respiratory diseases are a major cause of death worldwide, affecting a significant proportion of the population with lung function abnormalities that can lead to respiratory illnesses. Early detection and prevention are critical to effective management of these disorders. Deep learning algorithms offer a promising approach for analyzing complex medical data and aiding in early disease detection. While transformer-based models for sequence classification have proven effective for tasks like sentiment analysis, topic classification, etc., their potential for respiratory disease classification remains largely unexplored. This paper proposes a classifier utilizing the transformer-encoder block, which can capture complex patterns and dependencies in medical data. The proposed model is trained and evaluated on a large dataset from the International Conference on Biomedical Health Informatics 2017, achieving state-of-the-art results with a mean sensitivity of 70.53%, mean specificity of 84.10%, mean average score of 77.32%, and mean harmonic score of 76.10%. These results demonstrate the model's effectiveness in diagnosing respiratory diseases while taking up minimal computational resources.