Development and internal validation of an artificial intelligence-assisted bowel sounds auscultation system to predict early enteral nutrition-associated diarrhoea in acute pancreatitis: a prospective observational study.

Aims/Background An artificial intelligence-assisted prediction model for enteral nutrition-associated diarrhoea (ENAD) in acute pancreatitis (AP) was developed utilising data obtained from bowel sounds auscultation. This model underwent validation through a single-centre, prospective observational study. The primary objective of the model was to enhance clinical decision-making by providing a more precise assessment of ENAD risk. Methods The study enrolled patients with AP who underwent early enteral nutrition (EN). Real-time collection and analysis of bowel sounds were conducted using an artificial intelligence bowel sounds auscultation system. Univariate analysis, multicollinearity analysis, and logistic regression analysis were employed to identify risk factors associated with ENAD. The random forest algorithm was utilised to establish the prediction model, and partial dependence plots were generated to analyse the impact of risk factors on ENAD risk. Validation of the model was performed using the optimal model Bootstrap resampling method. Predictive performance was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and an area under the receiver operating characteristic (ROC) curve. Results Among the 133 patients included in the study, the incidence of ENAD was 44.4%. Six risk factors were identified, and the model's accuracy was validated through Bootstrap iterations. The prediction accuracy of the model was 81.10%, with a sensitivity of 84.30% and a specificity of 77.80%. The positive predictive value was 82.60%, and the negative predictive value was 80.10%. The area under the ROC curve was 0.904 (95% confidence interval: 0.817-0.997). Conclusion The artificial intelligence bowel sounds auscultation system enhances the assessment of gastrointestinal function in AP patients undergoing EN and facilitates the construction of an ENAD predictive model. The model demonstrates good predictive efficacy, offering an objective basis for precise intervention timing in ENAD management.

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.

BACKGROUND: The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve its predictive potential. OBJECTIVE: This study aims to objectively review the literature on AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its strengths, weaknesses, opportunities, and threats. METHODS: A scoping review on AI-assisted lung sound analysis in children with asthma was conducted across 4 major scientific databases (PubMed, MEDLINE Ovid, Embase, and Web of Science), supplemented by a gray literature search on Google Scholar, to identify relevant studies published from January 1, 2000, until May 23, 2023. The search strategy incorporated a combination of keywords related to AI, pulmonary auscultation, children, and asthma. The quality of eligible studies was assessed using the ChAMAI (Checklist for the Assessment of Medical Artificial Intelligence). RESULTS: The search identified 7 relevant studies out of 82 (9%) to be included through an academic literature search, while 11 of 250 (4.4%) studies from the gray literature search were considered but not included in the subsequent review and quality assessment. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Identified strengths were improved predictive accuracy of AI to allow for prompt and early diagnosis, personalized management strategies, and remote monitoring capabilities. Weaknesses were the heterogeneity between studies and the lack of standardization in data collection and interpretation. Opportunities were the potential of coordinated surveillance, growing data sets, and new ways of collaboratively learning from distributed data. Threats were both generic for the field of medical AI (loss of interpretability) but also specific to the use case, as clinicians might lose the skill of auscultation. CONCLUSIONS: To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning.

Towards a comprehensive bedside swallow screening protocol using cross-domain transformation and high-resolution cervical auscultation.

High-resolution cervical auscultation (HRCA) is an emerging noninvasive and accessible option to assess swallowing by relying upon accelerometry and sound sensors. HRCA has shown tremendous promise and accuracy in identifying and predicting swallowing physiology and biomechanics with accuracies equivalent to trained human judges. These insights have historically been available only through instrumental swallowing evaluation methods, such as videofluoroscopy and endoscopy. HRCA uses supervised learning techniques to interpret swallowing physiology from the acquired signals, which are collected during radiographic assessment of swallowing using barium contrast. Conversely, bedside swallowing screening is typically conducted in non-radiographic settings using only water. This poses a challenge to translating and generalizing HRCA algorithms to bedside screening due to the rheological differences between barium and water. To address this gap, we proposed a cross-domain transformation framework that uses cycle generative adversarial networks to convert HRCA signals of water swallows into a domain compatible with the barium swallows-trained HRCA algorithms. The proposed framework achieved a cross-domain transformation accuracy that surpassed 90%. The authenticity of the generated signals was confirmed using a binary classifier to confirm the framework's capability to produce indistinguishable signals. This framework was also assessed for retaining swallow physiological and biomechanical properties in the signals by applying an existing model from the literature that identifies the opening and closure of the upper esophageal sphincter. The outcomes of this model showed nearly identical results between the generated and original signals. These findings suggest that the proposed transformation framework is a feasible avenue to advance HCRA towards clinical deployment for water-based swallowing screenings.

Intrapartum fetal monitoring practices in Norway: A population-based study.

OBJECTIVE: To describe intrapartum fetal monitoring methods used in all births in Norway in 2019-2020, assess adherence to national guidelines, investigate variation by women's risk status, and explore associations influencing monitoring practices. METHODS: A nationwide population-based study. We collected data about all pregnancies with a gestational age ≥ 22 weeks during 2019-2020 from the Medical Birth Registry of Norway. We used descriptive analyses, stratified for risk status, to examine fetal monitoring methods used in all deliveries. Univariable and multivariable logistic regression models were used to determine factors associated with monitoring with cardiotocography (CTG) in low-risk, straightforward births. RESULTS: In total, 14 285 (14%) deliveries were monitored with only intermittent auscultation (IA), 46214 (46%) with only CTG, and 33417 (34%) with IA and CTG combined. Four percent (2 067/50 533) of women with risk factors were monitored with IA only. Half (10589/21 282) of the low-risk women with straightforward births were monitored with CTG. Maternal and fetal characteristics, size of the birth unit and regional practices influenced use of CTG monitoring in this group. CONCLUSIONS: Most births are monitored with CTG only, or combined with IA. Half the women with low-risk pregnancies and straightforward births were monitored with CTG although national guidelines recommending IA.

Visual assessment of Korotkoff sounds improves the accuracy of a validated professional automated auscultatory blood pressure monitor KOROT P3 Accurate.

OBJECTIVE: A novel automated auscultatory upper-arm cuff blood pressure (BP) monitor for office use (KOROT P3 Accurate, previously InBody BPBIO480KV), which displays Korotkoff sound curves for each BP reading was recently developed. This study investigated whether the review of Korotkoff sound curves by healthcare professionals further improves the accuracy of the device by identifying unreliable BP readings. METHODS: Three observers assessed independently the morphology of Korotkoff sound curves of BP measurements obtained during an ISO 81060-2:2018 validation study, and classified them as of good, fair, or poor quality (low amplitude or sound intensity, aberrant morphology, background noise, signal artifact, auscultatory gap, irregular rhythm). The observers were blinded to the study BP measurements. RESULTS: Korotkoff sound curves of 255 BP readings obtained in 85 individuals were analyzed (mean age 57.3 ±â€Š15.0 years, 53 men). Of the SBP readings 80.4/12.2/7.4% were classified as good/fair/poor, and DBP 76.9/12.2/10.9%. Inter-observer agreement in detecting poor-quality curves was 84.7/83.1% (systolic/diastolic). Of poor-quality curves, 10.5/60.7% (systolic/diastolic) clustered in the same individuals. The validation criterion 1 [mean test-reference BP difference ≤5 ±â€Š8 (SD) mmHg] was satisfied for readings with good (0.1 ±â€Š4.9/0.3 ±â€Š3.8 mmHg, systolic/diastolic) and fair-quality curves (-0.4 ±â€Š6.4/0.2 ±â€Š5.0), but not for poor-quality ones (2.7 ±â€Š8.8/3.6 ±â€Š8.1). By excluding poor-quality readings (40 of 255), criterion 1 of the validation study was improved (0.2 ±â€Š4.9/0.2 ±â€Š3.9 versus 0.3 ±â€Š5.5/0.6 ±â€Š4.7 mmHg). CONCLUSION: The visual assessment of Korotkoff sounds generated during automated auscultatory BP measurement by the KOROT P3 Accurate professional monitor identifies unreliable readings and further improves the device accuracy.

An open auscultation dataset for machine learning-based respiratory diagnosis studies.

Machine learning enabled auscultating diagnosis can provide promising solutions especially for prescreening purposes. The bottleneck for its potential success is that high-quality datasets for training are still scarce. An open auscultation dataset that consists of samples and annotations from patients and healthy individuals is established in this work for the respiratory diagnosis studies with machine learning, which is of both scientific importance and practical potential. A machine learning approach is examined to showcase the use of this new dataset for lung sound classifications with different diseases. The open dataset is available to the public online.

Novel Approach Combining Shallow Learning and Ensemble Learning for the Automated Detection of Swallowing Sounds in a Clinical Database.

Cervical auscultation is a simple, noninvasive method for diagnosing dysphagia, although the reliability of the method largely depends on the subjectivity and experience of the evaluator. Recently developed methods for the automatic detection of swallowing sounds facilitate a rough automatic diagnosis of dysphagia, although a reliable method of detection specialized in the peculiar feature patterns of swallowing sounds in actual clinical conditions has not been established. We investigated a novel approach for automatically detecting swallowing sounds by a method wherein basic statistics and dynamic features were extracted based on acoustic features: Mel Frequency Cepstral Coefficients and Mel Frequency Magnitude Coefficients, and an ensemble learning model combining Support Vector Machine and Multi-Layer Perceptron were applied. The evaluation of the effectiveness of the proposed method, based on a swallowing-sounds database synchronized to a video fluorographic swallowing study compiled from 74 advanced-age patients with dysphagia, demonstrated an outstanding performance. It achieved an F1-micro average of approximately 0.92 and an accuracy of 95.20%. The method, proven effective in the current clinical recording database, suggests a significant advancement in the objectivity of cervical auscultation. However, validating its efficacy in other databases is crucial for confirming its broad applicability and potential impact.

Wearing surgical face mask has no significant impact on auscultation assessment.

Objective: During the COVID-19 pandemic, universal mask-wearing became one of the main public health interventions. Because of this, most physical examinations, including lung auscultation, were done while patients were wearing surgical face masks. The aim of this study was to investigate whether mask wearing has an impact on pulmonologist assessment during auscultation of the lungs. Methods: This was a repeated measures crossover design study. Three pulmonologists were instructed to auscultate patients with previously verified prolonged expiration, wheezing, or crackles while patients were wearing or not wearing masks (physician and patients were separated by an opaque barrier). As a measure of pulmonologists' agreement in the assessment of lung sounds, we used Fleiss kappa (K). Results: There was no significant difference in agreement on physician assessment of lung sounds in all three categories (normal lung sound, duration of expiration, and adventitious lung sound) whether the patient was wearing a mask or not, but there were significant differences among pulmonologists when it came to agreement of lung sound assessment. Conclusion: Clinicians and health professionals are safer from respiratory infections when they are wearing masks, and patients should be encouraged to wear masks because our research proved no significant difference in agreement on pulmonologists' assessment of auscultated lung sounds whether or not patients wore masks.

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.

Comparisons of an automated oscillometric device with a hybrid manual auscultatory device for the Korea National Health and Nutrition Examination Survey.

This study evaluated an oscillometric device (OD), Microlife WatchBP Office AFIB, and a hybrid manual auscultatory device (AD), Greenlight 300TM, to determine a suitable blood pressure (BP) measurement device for the Korea National Health and Nutrition Examination Survey in a mercury-free context. Adhering to the 2018 Universal Standard's suggested consensus, the study involved 800 subjects (mean age 51.2 ± 17.5 years; 44.3% male), who underwent triplicate BP measurements following 5 min of rest in a randomized order (OD-first: 398 participants; AD-first: 402 participants). BP difference was calculated as OD value minus AD value, with results stratified by measurement sequence. The overall BP difference and tolerable error probability were -1.1 ± 6.5/-2.6 ± 4.9 mmHg and 89.2%/92.5% for systolic/diastolic BP (SBP/DBP), respectively. Lin's concordance correlation coefficient was 0.907/0.844 for SBP/DBP (OD-first/AD-first: 0.925/0.892 for SBP, 0.842/0.845 for DBP). The overall agreement for hypertension (BP ≥ 140 and/or 90 mmHg) was 0.71 (p < 0.0001), and the OD underestimated the overall hypertension prevalence by 5.1%. Analysis of the AD-first data revealed a lower level of agreement compared to the OD-first data; however, the observed blood pressure difference adhered to Criterion 1 of the 2018 Universal Standard. Microlife met the Criterion 1 of 2018 Universal Standard but underestimated the prevalence of hypertension. The BP discrepancy increased with higher BP levels, male sex, and smaller AC. With increasing age, the discrepancy decreased for SBP and increased for DBP.

UK student midwives' theoretical knowledge, confidence, and experience of intermittent auscultation of the fetal heart rate during labour: An online cross-sectional survey.

AIM: This study aimed to explore student midwives' theoretical knowledge of intrapartum intermittent auscultation, their confidence in, and their experience of this mode of fetal monitoring. DESIGN AND SETTING: An online cross-section survey with closed and open questions. Descriptive statistics were used to analyse participants' intermittent auscultation knowledge, confidence, and experience. Reflexive thematic analysis was used to identify patterns within the free text about participants' experiences. PARTICIPANTS: Undergraduate midwifery students (n = 303) from Nursing and Midwifery Council-approved educational institutions within the United Kingdom. FINDINGS: Most participants demonstrated good theoretical knowledge. They had witnessed the technique being used in clinical practice, and when performed, the practice was reported to be in line with national guidance. In closed questions, participants reported feeling confident in their intermittent auscultation skills; however, these data contrasted with free-text responses. CONCLUSION: This cross-sectional survey found that student midwives possess adequate knowledge of intermittent auscultation. However, reflecting individual clinical experiences, their confidence in their ability to perform intermittent auscultation varied. A lack of opportunity to practice intermittent auscultation, organisational culture, and midwives' preferences have caused student midwives to question their capabilities with this essential clinical skill, leaving some with doubt about their competency close to registration.

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.

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.

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.

Evaluation of arteriovenous fistula for hemodialysis with a new generation digital stethoscope: a pilot study.

BACKGROUND AND AIMS: The management of complications of arteriovenous fistula (AVF) for hemodialysis, principally stenosis, remains a major challenge for clinicians with a substantial impact on health resources. Stenosis not infrequently preludes to thrombotic events with the loss of AVF functionality. A functioning AVF, when listened by a stethoscope, has a continuous systolic-diastolic low-frequency murmur, while with stenosis, the frequency of the murmur increases and the duration of diastolic component decreases, disappearing in severe stenosis. These evidences are strictly subjective and dependent from operator skill and experience. New generation digital stethoscopes are able to record sound and subsequently dedicated software allows to extract quantitative variables that characterize the sound in an absolutely objective and repeatable way. The aim of our study was to analyze with an appropriate software sounds from AVFs taken by a commercial digital stethoscope and to investigate the potentiality to develop an objective way to detect stenosis. METHODS: Between September 2022 and January 2023, 64 chronic hemodialysis (HD) patients were screened by two blinded experienced examiners for recognized criteria for stenosis by Doppler ultrasound (DUS) and, consequently, the sound coming from the AVFs using a 3 M™ Littmann® CORE Digital Stethoscope 8570 in standardized sites was recorded. The sound waves were transformed into quantitative variables (amplitude and frequency) using a sound analysis software. The practical usefulness of the core digital stethoscope for a quick identification of an AVF stenosis was further evaluated through a pragmatic trial. Eight young nephrologist trainees underwent a simple auscultatory training consisting of two sessions of sound auscultation focusing two times on a "normal" AVF sound by placing the digital stethoscope on a convenience site of a functional AVF. RESULTS: In 48 patients eligible, all sound components displayed, alone, a remarkable diagnostic capacity. More in detail, the AUC of the average power was 0.872 [95% CI 0.729-0.951], while that of the mean normalized frequency was 0.822 [95% 0.656-0.930]. From a total of 32 auscultations (eight different block sequences, each one comprising four auscultations), the young clinicians were able to identify the correct sound (stenosis/normal AVF) in 25 cases, corresponding to an overall accuracy of 78.12% (95% CI 60.03-90.72%). CONCLUSIONS: The analysis of sound waves by a digital stethoscope permitted us to distinguish between stenotic and no stenotic AVFs. The standardization of this technique and the introducing of data in a deep learning algorithm could allow an objective and fast method for a frequent monitoring of AVF.

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.

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.

Lung anomaly detection from respiratory sound database (sound signals).

Chest or upper body auscultation has long been considered a useful part of the physical examination going back to the time of Hippocrates. However, it did not become a prevalent practice until the invention of the stethoscope by Rene Laennec in 1816, which made the practice suitable and hygienic. Pulmonary disease is a kind of sickness that affects the lungs and various parts of the respiratory system. Lung diseases are the third largest cause of death in the world. According to the World Health Organization (WHO), the five major respiratory diseases, namely chronic obstructive pulmonary disease (COPD), tuberculosis, acute lower respiratory tract infection (LRTI), asthma, and lung cancer, cause the death of more than 3 million people each year worldwide. Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (Fr-WCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed Fr-WCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed Fr-WCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed Fr-WCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963(96.3%), 0.932,(93.2%) and 0.948(94.8%), respectively.

Heart sounds: Past, present, and future from a technological and clinical perspective - a systematic review.

The high prevalence of cardiac diseases around the world has created a need for quick, easy and cost effective approaches to diagnose heart disease. The auscultation and interpretation of heart sounds using the stethoscope is relatively inexpensive, requires minimal to advanced training, and is widely available and easily carried by healthcare providers working in urban environments or medically underserved rural areas. Since René-Théophile-Hyacinthe Laennec's simple, monoaural design, the capabilities of modern-day, commercially available stethoscopes and stethoscope systems have radically advanced with the integration of electronic hardware and software tools, however these systems are largely confined to the metropolitan medical centers. The purpose of this paper is to review the history of stethoscopes, compare commercially available stethoscope products and analytical software, and discuss future directions. Our review includes a description of heart sounds and how modern software enables the measurement and analysis of time intervals, teaching auscultation, remote cardiac examination (telemedicine) and, more recently, spectrographic evaluation and electronic storage. The basic methodologies behind modern software algorithms and techniques for heart sound preprocessing, segmentation and classification are described to provide awareness.

Masking of Korotkoff sounds used in blood pressure measurement through auscultation.

Measurement of blood pressure (BP) through manual auscultation and the observation of Korotkoff sounds (KSs) remains the gold standard in BP methodology. Critical to determining BP levels via auscultation is the determination of KS audibility. While absolute sound level audibility is well researched, the problem has not been approached from the point of view of psychoacoustic masking of the sounds. Here, during manual auscultation of BP, a direct comparison is made between what an observer perceives as audible and the electronic analysis of audibility level determined from masking of sound signal levels. KSs are collected during auscultation with an electronic stethoscope, which allows simultaneously observing sound audibility and recording the sound electronically. By time-segmenting the recorded sound around Korotkoff peaks into a test segment and a masking segment, performing Fourier transforms on the segments, and comparing frequency-band sound energy levels, signal-to-noise ratios of a sound to its masking counterpart can be defined. Comparing these ratios to difference limen in the psychoacoustic masking literature, an approximate threshold for sound audibility is obtained. It is anticipated that this approach could have profound effects on future development of automated auscultation BP measurements.