Search

The objective of this study is to provide a framework to aid physicians in identifying early respiratory ailments as well as provide a means of monitoring medication compliancy for both the patient and physicians. To aid physicians identify abnormal sounds during auscultations such as crackle, this work proposes a multimedia approach in the form of audio display (AD) to enhance crackle sounds produced in respiration. This work utilize a two step AD approach in which the crackle sound is first separated from the rest of the vesicular sound and then either sonified or audified. To aid in monitoring use of medication this work proposes an environmental sound analysis (ESA) framework to autonomously quantify adherence to medication. This work employed traditional audio features to extract meaningful discriminatory information to identify the inhaler sounds from the environment with the aid of maximum relevance and minimum redundancy algorithm and the hidden markov model.

Objective: LVADs are surgically implanted mechanical pumps that improve survival rates of individuals with advanced heart failure. LVAD therapy is associated with high morbidity, which can be partially attributed to challenges with detecting LVAD complications before adverse events occur. Current methods used to monitor for complications with LVAD support require frequent clinical assessments at specialized LVAD centers. Analysis of recorded precordial sounds may enable real-time, remote monitoring of device and cardiac function for early detection of LVAD complications. The dominance of LVAD sounds in the precordium limits the utility of routine cardiac auscultation of LVAD recipients. In this work, we develop a signal processing pipeline to mitigate sounds generated by the LVAD. Methods: We collected in vivo precordial sounds from 17 LVAD recipients, and contemporaneous echocardiograms from 12 of these individuals, to validate heart valve closure timings. Results: We characterized various acoustic signatures of heart sounds extracted from in vivo recordings, and report preliminary findings linking fundamental heart sound characteristics and level of LVAD support. Conclusion: Mitigation of LVAD sounds from precordial sound recordings of LVAD recipients enables analysis of intrinsic heart sounds. Significance: These findings provide proof-of-concept evidence of the clinical utility of heart sound analysis for bedside and remote monitoring of LVAD recipients.

Background and Aim Objective measurements are not available for determining bowel sounds. The present study sought to evaluate the efficacy of a novel bowel sound monitoring system for perioperative use in patients undergoing gastric surgery. Methods The study enrolled 14 patients who underwent surgery for gastric cancer at Kochi Medical School from 2017 to 2018. Preoperative and postoperative bowel sounds were recorded using a newly developed real‐time analysis system in the operating theater and recovery room. Clinical information and bowel sound count data were obtained to compare preoperative and postoperative measures. Results The median preoperative and postoperative bowel sound counts across all patients were 1.4 and 2.5 counts per minute (cpm), respectively. In patients who underwent laparoscopic gastrectomy, the postoperative bowel sound count was significantly higher than that recorded preoperatively (2.3 vs. 1.6 cpm, P = 0.005). The findings also revealed a significant negative correlation between postoperative bowel sound count and operation time (r = −0.714, P = 0.003). Conclusions The real‐time bowel sound analysis system tested herein presents a promising diagnostic tool to quantitatively evaluate bowel movements associated with surgery. Our results suggested a need for shorter operation times for gastric procedures with respect to peristalsis recovery and supported the use of minimally invasive surgery., We prospectively evaluated bowel sound counts using newly developed analysis system in patients who underwent gastric surgery. A real‐time bowel sound analysis system is promising as a diagnostic tool to quantitatively evaluate bowel movement. In this study, analysis of bowel sounds in patients undergoing gastrointestinal surgery suggested a need for shorter operation times from the aspect of peristalsis recovery and supporting the use of minimally invasive surgery.

Assessment of swallowing function is often invasive or involves irradiation. Analysis of swallowing sounds is a noninvasive method for assessment of swallowing but is not used in daily medical practice. Dysphagia could be the first symptom that occurs in head and neck cancer. This study evaluated a method for the automatic detection and analysis of swallowing sounds in healthy subjects and in patients with pharyngolaryngeal cancer. A smartphone application, developed for automatic detection and analysis of swallowing sounds was developed and tested in 12 healthy volunteers and in 26 patients with pharyngolaryngeal cancer. Swallowing sounds were recorded with a laryngophone during a standardized meal (100 mL mashed potatoes, 100 mL water, and 100 mL yogurt). Swallowing number and duration were noted; the results were compared to a standard swallowing sound analysis using the software AUDACITY®. There were no statistically significant differences in swallowing number or duration between the two analysis methods for the three types of foods in healthy volunteers and only for water in patients. In healthy volunteers, the results of our automatic analysis were comparable with those obtained with the standard analysis. However, a better discrimination of swallowing sounds is necessary for the algorithm to obtain reliable results with thicker food in patients with head and neck cancer.

The aim of this study was to integrate the physical findings of drug-induced sleep endoscopy with snoring sound analysis in patients with obstructive sleep apnea/hypopnea syndrome (OSAS) and to compare the findings with previously published data. This was a prospective, non-randomized study. Participants were all candidates for surgical treatment of OSAS and formed three groups, retropalatal (RP) obstructions, retrolingual (RL) obstructions, and multilevel (ML) obstructions. At the time of DISE, recordings of concurrent snoring sounds were made. Mean pitch frequency, peak sound frequency, and fundamental frequency (Fo) components were determined. A total of 55 participants had mean age 46.2 ± 7.3 years, mean BMI 30.0 ± 3.7 kg/m2, and included 11 women (20%). Differences in mean pitch frequency, Fo, and peak sound frequency were all statistically significant between the RP and RL (p = 0.001), between ML and RL (p = 0.025) but were not significantly different between RP and ML. Mean pitch frequency of RP was lower than RL, and ML frequency was between RL and RP. The sound analysis graphics revealed RP waves with sharp peaks and lower frequencies and RL with smooth curves and higher frequencies. ML showed irregular patterns. Mean pitch frequency of RL was always above 400 Hz, whereas RP was below this value. It is feasible to apply sound analysis to determine the site of obstruction during DISE. Combining the data may help surgeons make more accurate assessments of the pattern of the disease.

Purpose Persistent unilateral vocal fold paralysis (UFVP) with glottal insufficiency often requires type I medialization thyroplasty (MT). Previous implants cannot be adjusted postoperatively if necessary. The newly developed APrevent® VOIS implant (VOIS) can provide postoperative re-adjustment to avoid revision MT. The objective of this pilot study is to evaluate the VOIS intraoperatively concerning voice improvement, surgical feasibility and device handling. Methods During routine MT, VOIS was applied short time in eight patients before the regular implantation of the Titanium Vocal Fold Medialization Implant (TVFMI™). In all patients, perceptual voice sound analysis using R(oughness)–B(reathiness)–H(oarseness)-scale, measurement of M(aximum)–P(honation)–T(ime) and glottal closure in videolaryngoscopy were performed before and after implanting VOIS/TVFMI™. Acoustic analyses of voice recordings were performed using freeware praat. Surgical feasibility, operative handling and device fitting of VOIS and TVFMI™ were assessed by the surgeon using V(isual)-A(nalog)-S(cale). Data were statistically analyzed with paired t test. Result All patients showed significant improvement of voice sound parameters after VOIS/TVFMI™ implantation. The mean RBH-scale improved from preoperative R = 2.1, B = 2.3, H = 2.5 to R = 0.6, B = 0.3, H = 0.8 after VOIS and R = 0.5, B = 0.3, H = 0.8 after TVFMI™ implantation. The mean MPT increased from preoperative 7.9 to 14.6 s after VOIS and 13.8 s after TVFMI™ implantation. VOIS/TVFMI™ achieved complete glottal closure in 7/8 patients. The satisfaction with intraoperative device fitting and device handling of VOIS was as good as that of TVFMI™. Conclusion The novel APrevent® VOIS implant showed similar intraoperative voice improvement compared to routinely used TVFMI™ without adverse device events and with safe device fitting.

Objectives To develop a novel device to predict systolic and diastolic blood pressure based on measured heart sound signals and evaluate its accuracy in comparison to intra-arterial blood pressure readings. Study design Prospective, observational pilot study. Setting PICU. Patients Critically ill children (0-18 yr) undergoing continuous blood pressure monitoring via radial artery intra-arterial catheters were enrolled in the study after informed consent. The study included medical, cardiac, and surgical PICU patients. Interventions Along with intra-arterial blood pressure, patient's heart sounds were recorded simultaneously by a highly sensitive sensor taped to the chest. Additional hardware included a data acquisition unit and laptop computer. Subsequently, advanced signal processing technologies were used to minimize random interfering signals and extract and separate S1 and S2 signals. A computerized model was then developed using artificial neural network systems to estimate blood pressure from the extracted heart sound analysis. Measurements and main outcomes We found a statistically significant correlation for systolic (r = 0.964; R = 0.928) and diastolic (r = 0.935; R = 0.868) blood pressure readings (n = 491) estimated by the novel heart-sound signal-based method and those recorded by intra-arterial catheters. The mean difference of the individually paired determinations of the blood pressure between the heart-sound-based method and intra-arterial catheters was 0.6 ± 7 mm Hg for systolic blood pressure and -0.06 ± 5 mm Hg for diastolic blood pressure, which was within the recommended range of 5 ± 8 mm Hg for any new blood pressure devices. Conclusions Our findings provide proof of concept that the heart-sound signal-based method can provide accurate, noninvasive blood pressure monitoring.

Background Skin cancer (SC), especially melanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities. Previously, it was demonstrated that diagnostics by dermoscopy are improved by applying an additional sonification (data to sound waves conversion) layer on DL algorithms. The aim of the study was to determine the impact of image quality on accuracy of diagnosis by sonification employing a rudimentary skin magnifier with polarized light (SMP). Methods Dermoscopy images acquired by SMP were processed by a first deep learning algorithm and sonified. Audio output was further analyzed by a different secondary DL. Study criteria outcomes of SMP were specificity and sensitivity, which were further processed by a F2-score, i.e. applying a twice extra weight to sensitivity over positive predictive values. Findings Patients (n = 73) fulfilling inclusion criteria were referred to biopsy. SMP analysis metrics resulted in a receiver operator characteristic curve AUC's of 0.814 (95% CI, 0.798–0.831). SMP achieved a F2-score sensitivity of 91.7%, specificity of 41.8% and positive predictive value of 57.3%. Diagnosing the same set of patients' lesions by an advanced dermoscope resulted in a F2-score sensitivity of 89.5%, specificity of 57.8% and a positive predictive value of 59.9% (P=NS). Interpretation DL processing of dermoscopic images followed by sonification results in an accurate diagnostic output for SMP, implying that the quality of the dermoscope is not the major factor influencing DL diagnosis of skin cancer. Present system might assist all healthcare providers as a feasible computer-assisted detection system. Fund Bostel Technologies. Trial Registration clinicaltrials.gov Identifier: NCT03362138

Vocal onset is the process occurring between the first detectable oscillatory glottal movement and the steady state vibration of the vocal folds. To some extent, the voice onset mirrors the voice offset. High speed imaging, photo-, electro-, flow- and ultrasonoglottography and sound analysis have been used in combinations to allow detailed qualitative insight into the phenomenon. Moreover, the instantaneous intraglottal pressure can be computed from the combined records of transglottal airflow and glottal area. A large number of vocal onsets of different types were analysed in various conditions of modal healthy phonation. Vocal folds (VF) vibration can start either from a closed glottis (hard onset) or from an open glottis (soft/breathy onset). In a soft onset, the amplitude of oscillations progressively increases over 2 to more than 30 cycles, before the first clear closed plateau is achieved. It is not possible to define whether the first movement of VF is towards medial or lateral. Hard, soft and breathy onsets can be clearly identified. Flow- and photoglottography are the most sensitive signals in detecting the first glottal movements in soft and breathy onsets. The shape of the EGG signal depends on the contact of the VF edges. The duration of the onset phase is to some extent related to VF adduction speed and peak expiratory flow. The ultrasound technique is sensitive, but lacks physiological interpretation. From the first onset-cycles on, the intraglottal pressure during the opening phase of the glottis exceeds that during the closing phase. During soft/breathy onsets with a sufficiently large number of cycles, when the vibrating mass increases, a trend appears toward a slight progressive decrease of the fundamental frequency of the oscillations, likely related to the increasing vibrating mass.

Background: Identification of the obstruction site in the upper airway may help in treatment selection for patients with sleep-disordered breathing. Because of limitations of existing techniques, there is a continuous search for more feasible methods. Snoring sound parameters were hypothesized to be po-tential predictors of the obstruction site. Therefore, this review aims to i) investigate the association between snoring sound parameters and the obstruction sites; and ii) analyze the methodology of re-ported prediction models of the obstruction sites. Methods: The literature search was conducted in PubMed, Embase.com, CENTRAL, Web of Science, and Scopus in collaboration with a medical librarian. Studies were eligible if they investigated the associa-tions between snoring sound parameters and the obstruction sites, and/or reported prediction models of the obstruction sites based on snoring sound. Results: Of the 1016 retrieved references, 28 eligible studies were included. It was found that the characteristic frequency components generated from lower-level obstructions of the upper airway were higher than those generated from upper-level obstructions. Prediction models were built mainly based on snoring sound parameters in frequency domain. The reported accuracies ranged from 60.4% to 92.2%. Conclusions: Available evidence points toward associations between the snoring sound parameters in the frequency domain and the obstruction sites in the upper airway. It is promising to build a prediction model of the obstruction sites based on snoring sound parameters and participant characteristics, but so far snoring sound analysis does not seem to be a viable diagnostic modality for treatment selection. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Aims Recent technological advances enable diagnosing of obstructive coronary artery disease (CAD) from heart sound analysis with a high negative predictive value. However, the prognostic impact of this approach remains unknown. To investigate the prognostic value of heart sound analysis as two scores, the Acoustic-score and the CAD-score, in patients with suspected CAD which is treated according to standard of care. Methods and results Consecutive patients with angina symptoms referred for coronary computed tomography angiography (CTA) were enrolled. The Acoustic-score was developed from eight acoustic CAD-related features. This score was combined with risk factors to generate the CAD-score. A cut-off score >20 was pre-specified for both scores to indicate disease. If coronary CTA raised suspicion of obstructive CAD, patients were referred to invasive angiography and revascularized when indicated. Of 1675 enrolled patients, 1464 (87.4%) were included in this substudy. The combined primary endpoint was all-cause mortality and myocardial infarction (n = 26). Follow-up was 3.1 (2.7–3.4) years. Of patients with primary endpoints, the Acoustic-score was >20 in 25 (96%); the CAD-score was >20 in 22 (85%). In an unadjusted Cox analysis of the primary endpoints, the hazard ratio for scores >20 under current standard clinical care was 12.6 (1.7–93.2) for the Acoustic-score and 5.4 (1.9–15.7) for the CAD-score. The CAD-score contained prognostic information even after adjusting for lipid-lowering therapy initiation, stenosis at CTA, and early revascularization. Conclusion Heart sound analysis seems to carry prognostic information and may improve initial risk stratification of patients with suspected CAD. Clinicaltrials.org ID NCT02264717.

Computer audition (CA) has been demonstrated to be efficient in healthcare domains for speech-affecting disorders (e.g., autism spectrum, depression, or Parkinson's disease) and body sound-affecting abnormalities (e. g., abnormal bowel sounds, heart murmurs, or snore sounds). Nevertheless, CA has been underestimated in the considered data-driven technologies for fighting the COVID-19 pandemic caused by the SARS-CoV-2 coronavirus. In this light, summarise the most recent advances in CA for COVID-19 speech and/or sound analysis. While the milestones achieved are encouraging, there are yet not any solid conclusions that can be made. This comes mostly, as data is still sparse, often not sufficiently validated and lacking in systematic comparison with related diseases that affect the respiratory system. In particular, CA-based methods cannot be a standalone screening tool for SARS-CoV-2. We hope this brief overview can provide a good guidance and attract more attention from a broader artificial intelligence community., 2 pages

This study aimed to verify the effect of anesthetic with ophthalmic eye drops tetracaine hydrochloride 1% and phenylephrine hydrochloride 0.1% in frenotomy in ankyloglossia in infants. The sample consisted of 24 children aged 2 to 6 months, indicated for frenotomy from February to August 2019, performed by the pediatric dentistry team of the Health Unit Service (SUS), in the city of Gurupi, Tocantins State, Brazil. We chose to compare the ophthalmic anesthetic with two anesthetics commonly used in frenotomies. The children were evaluated by sound recording at two moments of the surgical act by a mobile device with a calibrated "decibel meter" application. The mean intensities of infant crying in surgery with phenylephrine/tetracaine topical were significantly lower than the mean intensities when lidocaine injection (P=0.0096) was used, but the same statistical procedure applied, comparing topical Benzocaine versus lidocaine injection, showed no significant difference (P=0.35). The methods of noninvasive topical applications were better than invasive ones, the sound analysis showed that topical application of ophthalmic eye drops of tetracaine/phenylephrine proved to be the best analgesia in frenotomies.

Since the historical article by Forgacs in 1978, many studies have clarified the changes of lung sounds due to airway narrowing as well as the mechanism of genesis of these sounds. Studies using bronchoprovocation have shown that an increase of the frequency and/or intensity of lung sounds was a common finding of airway narrowing and correlated well with lung function in bronchial asthma. Bronchoprovocation studies also showed that wheezing may not be as sensitive as changes in basic lung sounds in acute airway narrowing in adult asthmatics. In lung sound analysis, narrow airways cause an increase in the frequency of breath sounds and lung sound intensity, implying when the patient has higher than normal breath sounds, i.e., bronchial sounds, he or she may have airway narrowing. Recent studies reported that this increase of breath sounds suggested worsening of airway inflammation in rather stable patients with bronchial asthma. As it is difficult to detect subtle changes in lung sounds by auscultation alone, automated sound analysis will be expected.

The left ventricular assist device (LVAD) has emerged as a bridge or alternative to heart transplant in individuals with advanced heart failure. However, the LVAD recipient population currently faces high rehospitalization rates. Remote analysis of precordial sounds in LVAD recipients may improve aftercare through early detection of complications. Prior work on analyses of precordial sounds in LVAD recipients focused on identifying pump thrombosis. Here, we focus on analyzing intrinsic precordial sounds to provide insight into intrinsic cardiac function. We analyzed a dataset of patient-acquired recordings of precordial sounds in LVAD recipients. We developed a signal processing pipeline to separate LVAD-generated sounds from other precordial sounds, making heart sound analysis in LVAD recipients feasible. Unsupervised clustering of features extracted from LVAD-mitigated sounds revealed subgroups of subjects possessing heart sounds with distinct frequency characteristics. The results provide preliminary evidence for the potential utility of exploring heart sound analysis in LVAD recipients for remote monitoring.

The severity of obstructive sleep apnoea (OSA) is diagnosed with polysomnography (PSG), during which patients are monitored by over 20 physiological sensors overnight. These sensors often bother patients and may affect patients&rsquo, sleep and OSA. This study aimed to investigate a method for analyzing patient snore sounds to detect the severity of OSA. Using a microphone placed at the patient&rsquo, s bedside, the snoring and breathing sounds of 22 participants were recorded while they simultaneously underwent PSG. We examined some features from the snoring and breathing sounds and examined the correlation between these features and the snore-specific apnoea-hypopnea index (ssAHI), defined as the number of apnoea and hypopnea events during the hour before a snore episode. Statistical analyses revealed that the ssAHI was positively correlated with the Mel frequency cepstral coefficients (MFCC) and volume information (VI). Based on clustering results, mild snore sound episodes and snore sound episodes from mild OSA patients were mainly classified into cluster 1. The results of clustering severe snore sound episodes and snore sound episodes from severe OSA patients were mainly classified into cluster 2. The features of snoring sounds that we identified have the potential to detect the severity of OSA.

Severities of obstructive sleep apnea (OSA) estimated both for the overall sleep duration and for the time spent in rapid eye movement (REM) and non-rapid eye movement (NREM) sleep are important in managing the disease. The objective of this study is to investigate a method by which snore sounds can be analyzed to detect the presence of OSA in NREM and REM sleep.Using bedside microphones, snoring and breathing-related sounds were acquired from 91 patients with OSA (35 females and 56 males) undergoing routine diagnostic polysomnography studies. A previously developed automated mathematical algorithm was applied to label each snore sound as belonging to either NREM or REM sleep. The snore sounds were then used to compute a set of mathematical features characteristic to OSA and to train a logistic regression model (LRM) to classify patients into an OSA or non-OSA category in each sleep state. The performance of the LRM was estimated using a leave-one-patient-out cross-validation technique within the entire dataset. We used the polysomnography-based diagnosis as our reference method.The models achieved 80% to 86% accuracy for detecting OSA in NREM sleep and 82% to 85% in REM sleep. When separate models were developed for females and males, the accuracy for detecting OSA in NREM sleep was 91% in females and 88% to 89% in males. Accuracy for detecting OSA in REM sleep was 88% to 91% in females and 89% to 91% in males.Snore sounds carry sufficient information to detect the presence of OSA during NREM and REM sleep. Because the methods used include technology that is fully automated and sensors that do not have a physical connection to the patient, it has potential for OSA screening in the home environment. The accuracy of the method can be improved by developing sex-specific models.

Introduction: The use of left ventricular assist device (LVAD) has grown rapidly. Adverse events do continue to occur. In recent years, analysis of LVAD sound recordings emerged as a means to monitor pump function and detect pump thrombosis. The aim of this study was to characterize the sounds from HeartMate II and to evaluate the use of handheld iOS devices for sound recordings. Method: Signal analysis of LVAD sound recordings, with dedicated recording equipment and iOS devices, was performed. Two LVADs running in mock loop circuits were compared to an implanted LVAD. Spectral analysis and parametric signal models were explored to quantify the sound and potentially detect changes in it. Results: The sound recordings of two LVADs in individual mock loop circuits and a third one implanted in a patient appeared to be similar. Qualitatively, sound characteristics were preserved following changes in pump speed. Recordings using dedicated equipment showed that HeartMate II sound comprises low-frequency components corresponding to pump impeller rotation, as well as high-frequency components due to a pulse width modulation of the electric power to the pump. These different signal components interact and result in a complicated frequency spectrum. The iPhone and iPod recordings could not reproduce the sounds as well as the dedicated equipment. In particular, lower frequencies were affected by outside disturbances. Discussion: This article outlines a systematic approach to LVAD sound analysis using signal processing methods to quantify and potentially detect changes, and describes some of the challenges, for example, with the use of inexpensive recording devices.

Cough hypersensitivity affects mainly postmenopausal women. Pathogenesis remains unknown in their case; therefore, an optimal treatment is unavailable. Only male guinea pigs are used in basic cough research and exclusion of females leads to gender bias. Nowadays, the efforts of grant agencies aim to support projects which take gender into account and involve both sexes to reduce gender bias. The aim of our pilot study is to assess the suitability of female guinea pig model in cough research. Cough response to citric acid (0.4 M) was obtained in female and male guinea pigs (n = 12 each). Reproducibility of cough response was tested four times a week apart. The cough was detected from the airflow traces and cough sound analysis. Our initial results show that cough response of female guinea pigs is higher than in males. Variability of females is also higher than in males. Further investigations are necessary to validate this model in different conditions to achieve gender equality in cough studies.

OBJECTIVE In this study, we evaluated used aluminum tuning forks (TFs) for fundamental frequencies (FF), overtones, and decay times. MATERIALS AND METHODS In total, 15 used (1 C1, 11 C2, and 3 C3) and 1 unused (C2) TFs were tuned, and the recorded sound data were analyzed using the Praat sound analysis program. RESULTS It was found that FFs of the recorded sounds produced by the used C2-TFs presented a high variability from 0.19% to 74.15% from the assumed FFs, whereas this rate was smaller (1.49%) in the used C3-TFs. Further, decay times of the used C2-TFs varied from 5.41 to 40.97 s. CONCLUSION This study, as the first of its kind in the literature, reported that some of the used aluminum TFs lost their physical properties that are important for clinical TF tests. It could be said that this is a phenomenon related to metal fatigue, which is common in aluminum products due to the cyclic load.

Background Heart failure (HF) is a type of cardiovascular disease caused by abnormal cardiac structure and function. Early screening of HF has important implication for treatment in a timely manner. Heart sound (HS) conveys relevant information related to HF; this study is therefore based on the analysis of HS signals. The objective is to develop an efficient tool to identify subjects of normal, HF with preserved ejection fraction and HF with reduced ejection fraction automatically. Methods We proposed a novel HF screening framework based on gated recurrent unit (GRU) model in this study. The logistic regression-based hidden semi-Markov model was adopted to segment HS frames. Normalized frames were taken as the input of the proposed model which can automatically learn the deep features and complete the HF screening without de-nosing and hand-crafted feature extraction. Results To evaluate the performance of proposed model, three methods are used for comparison. The results show that the GRU model gives a satisfactory performance with average accuracy of 98.82%, which is better than other comparison models. Conclusion The proposed GRU model can learn features from HS directly, which means it can be independent of expert knowledge. In addition, the good performance demonstrates the effectiveness of HS analysis for HF early screening.

Background and objectives: The objective of this study was to contribute to the evaluation of the newborn (NB) cry as a means of communication and diagnosis. Materials and Methods: The study implied the recording of the spontaneous cry of 101 NBs with no intrapartum events (control sample), and of 72 NBs with nuchal cord (study sample) from the &ldquo, Bega&rdquo, University Clinic of Obstetrics&ndash, Gynecology and Neonatology of Timisoara, Romania. The sound analysis was based upon: Imagistic highlighting methods, descriptive statistics, and data mining techniques. Results: The differences between the cry of NBs with no intrapartum events and that of NBs affected by nuchal cord are statistically significant regarding the volume unit meter (VUM) (p = 0.0021) and the peak point meter (PPM) (p = 0.041). Conclusions: While clinically there are no differences between the two groups, the cry recorded from the study group (nuchal cord group) shows distinctive characteristics compared to the cry recorded from the control group (eventless intrapartum NBs group).

Introduction: Asthma is a common childhood respiratory disorder characterized by wheeze, cough and respiratory distress responsive to bronchodilator therapy. Asthma severity can be determined by su...

The knee is the largest joint in the human body. Unfortunately, some hips or knee joints suffer on inflammation, misalignment, degeneration, trauma as well as diseases like arthritis or osteoporosis. Modern medicine can measure the joint condition or, if the joint is worn out, even exchange the joint with an implant. Endoprosthetic implants are artificial devices that replaces a weak body part such as osteoarthritic knee or hip joints. The lifespan of joint endoprostheses are also limited and depend on several factors, and it varies for each patient. In most cases total knee or hip endoprostheses need to be replaced after approximately 15 to 20 years, but some implants need an exchange after a few years due to several causes. Current methods to examine the condition of joint endoprostheses and natural joints are X-ray, Computed tomography (CT) and Magnetic Resonance Imaging (MRI). In rare cases implant integrated sensors were used. The usage of these methods and the analysis of the assessed data require medical and data experts. However, a vague estimation of the joint condition can also be performed by external vibration and sound analysis of the endoprosthesis and natural joint during movements. This paper describes several approaches of external vibration and sound analysis as a survey.

STUDY OBJECTIVES: Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries information about apnea/hypopnea and sleep/wake status. We hypothesized that image analysis of all-night TS recordings by a deep neural network (DNN) would be capable of detecting breathing events and classifying sleep/wake status. The aim of this study is to develop a DNN-based system for sleep apnea testing and validate it using a large sampling of polysomnography (PSG) data. METHODS: PSG examinations for the evaluation of sleep-disordered breathing (SDB) were performed for 1,852 patients: 1,548 PSG records were used to develop the system, and the remaining 304 records were used for validation. TS spectrogram images were obtained every 60 seconds and labeled with the PSG scoring results (breathing event and sleep/wake status), then introduced to DNN learning. Two different DNNs were trained for breathing status and sleep/wake status, respectively. RESULTS: A DNN with convolutional layers showed the best performance for discriminating breathing status. The same DNN structure was trained for sleep/wake discrimination. In the validation study, the DNN analysis was capable of discriminating the sleep/wake status with reasonable accuracy. The diagnostic sensitivity, specificity, and area under the receiver operating characteristic curves for diagnosis of SDB with apnea-hypopnea index of > 5, 15, and 30 were 0.98, 0.76, and 0.99; 0.97, 0.90, and 0.99; and 0.92, 0.94, and 0.98, respectively. CONCLUSIONS: The developed system using the TS DNN analysis has a good performance for SDB testing. CITATION: Nakano H, Furukawa T, Tanigawa T. Tracheal sound analysis using a deep neural network to detect sleep apnea. J Clin Sleep Med. 2019;15(8): 1125–1133.

Newborn transition is a phase of complex change involving lung fluid clearance and lung aeration. We aimed to use a digital stethoscope (DS) to assess the change in breath sound characteristics over the first 2 h of life and its relationship to mode of delivery. A commercially available DS was used to record breath sounds of term newborns at 1-min and 2-h post-delivery via normal vaginal delivery (NVD) or elective caesarean section (CS). Sound analysis was conducted, and two comparisons were carried out: change in frequency profiles over 2 h, and effect of delivery mode. There was a significant drop in the frequency profile of breath sounds from 1 min to 2 h with mean (SD) frequency decreasing from 333.74 (35.42) to 302.71 (47.19) Hz, p 0.001, and proportion of power (SD) in the lowest frequency band increasing from 0.27 (0.11) to 0.37 (0.15), p 0.001. At 1 min, NVD infants had slightly higher frequency than CS but no difference at 2 h.Conclusion: We were able to use DS technology in the transitioning infant to depict significant changes to breath sound characteristics over the first 2 h of life, reflecting the process of lung aeration.What is Known:• Lung fluid clearance and lung aeration are critical processes that facilitate respiration and mode of delivery can impact this• Digital stethoscopes offer enhanced auscultation and have been used in the paediatric population for the assessment of pulmonary and cardiac soundsWhat is New:• This is the first study to use digital stethoscope technology to assess breath sounds at birth• We describe a change in breath sound characteristics over the first 2 h of life and suggest a predictive utility of this analysis to predict the development of respiratory distress in newborns prior to the onset of symptoms.

International audience; Sound analysis by the cochlea relies on frequency tuning of mechanosensory hair cells along a tonotopic axis. To clarify the underlying biophysical mechanism, we have investigated the micromechanical properties of the hair cell's mechanoreceptive hair bundle within the apical half of the rat cochlea. We studied both inner and outer hair cells, which send nervous signals to the brain and amplify cochlear vibrations, respectively. We find that tonotopy is associated with gradients of stiffness and resting mechanical tension, with steeper gradients for outer hair cells, emphasizing the division of labor between the two hair-cell types. We demonstrate that tension in the tip links that convey force to the mechano-electrical transduction channels increases at reduced Ca 2+. Finally, we reveal gradients in stiffness and tension at the level of a single tip link. We conclude that mechanical gradients of the tip-link complex may help specify the characteristic frequency of the hair cell.

Efficient screening for severe obstructive sleep apnea (OSA) is important for children with snoring before time-consuming standard polysomnography. This retrospective cross-sectional study aimed to compare clinical variables, home snoring sound analysis, and home sleep pulse oximetry on their predictive performance in screening severe OSA among children who habitually snored. Study 1 included 9 (23%) girls and 30 (77%) boys (median age of 9 years). Using univariate logistic regression models, 3% oxygen desaturation index (ODI3) ≥ 6.0 events/h, adenoidal-nasopharyngeal ratio (ANR) ≥ 0.78, tonsil size = 4, and snoring sound energy of 801–1000 Hz ≥ 22.0 dB significantly predicted severe OSA in descending order of odds ratio. Multivariate analysis showed that ODI3 ≥ 6.0 events/h independently predicted severe pediatric OSA. Among several predictive models, the combination of ODI3, tonsil size, and ANR more optimally screened for severe OSA with a sensitivity of 91% and a specificity of 94%. In Study 2 (27 (27%) girls and 73 (73%) boys, median age, 7 years), this model was externally validated to predict severe OSA with an accuracy of 76%. Our results suggested that home sleep pulse oximetry, combined with ANR, can screen for severe OSA more optimally than ANR and tonsil size among children with snoring.

The evolution of hearing in terrestrial animals has resulted in remarkable adaptations enabl ing exquisitely sensitive sound detection by the ear and sophistic ated sound analysis by the brain . In this review, we examine several such characteristics, using examples from insects and vertebrates . We focus on two strong and interdependent forces that have been shaping the auditory systems across taxa: the physical environment of auditory transducers on the small, subcellular, scale and the evolutionary environment within which hearing takes place, on a larger, sensory - ecological scale. We discuss briefly acoustical feature selectivity and invariance in the central auditory system, highlighting a major difference betwe en insects and vertebrates , as well as a major similarity. By doing so within a sensory ecological framework, we aim to empha size general principles underlying acute sensitivity to airborne sounds.

OBJECTIVES Sonouroflowmetry represents a novel method for estimating urinary flow parameters. The aim of this study was to compare the urinary flow parameters acquired using sonouroflowmetry with those of standard uroflowmetry in healthy female volunteers. METHODS Thirty-six healthy female volunteers (aged 25-54 years) were subjected to standard uroflowmetry. Simultaneously, subjects dialed a dedicated number on a mobile phone and kept recording until urination was finished. Sound data were analyzed and compared to the uroflowmetry data. Of 218 recordings, 183 were included in the final analysis. Thirty-four measurements were excluded for voided volume

This chapter aims to give a perspective on how the study of the heart sound relation with blood pressure has evolved. The use of heart sound as a surrogate of the BP has been used with more emphasis on the detection of pulmonary hypertension patients, considering the frequency content, amplitude, and split the second heart sound and its subcomponents, which arise following the closure of the corresponding heart valves. Estimation of BP using the analysis of heart sound is characterized by the simplicity of the equipment used to obtain data, which after analysis allows to achieve promising results that until now were only obtained with techniques requiring far more complex and expensive equipment. The main objective of this chapter is to understand how heart sound analysis may be used to estimate blood pressure and which methods are employed to detect pulmonary hypertension.

Introduction: The HeartMate 3™ has shown lower rates of adverse events compared to previous devices due to the design and absence of mechanical bearings. For previous devices, sound analysis emerged as a way to assess pump function. The aims of this study were to determine if sound analysis can be applied to the HeartMate 3 in vivo and in vitro and to evaluate an electronic stethoscope. Method: Sound recordings were performed with microphones and clinical accessible electronic stethoscope. The recordings were studied in both the time and the frequency domains. Recordings from four patients were performed to determine if in vivo and in vitro recordings are comparable. Results: The results show that it is possible to detect sound from HeartMate 3 and the sound spectrum is clear. Pump frequency and frequency of the pulsatile mode are easily determined. Frequency spectra from in vitro and in vivo recordings have the same pattern, and the major proportion (96.7%) of signal power is located at the pump speed frequency ±40 Hz. The recordings from the patients show low inter-individual differences except from location of peaks originating from pump speed and harmonics. Electronic stethoscopes could be used for sound recordings, but the dedicated equipment showed a clearer sound spectrum. Discussion: The results show that acoustic analysis can also be performed with the HeartMate 3 and that in vivo and in vitro sound spectrum is similar. The frequency spectra are different from previous devices, and methods for assessing pump function or thrombosis need further evaluation.

In modern medicine, every cardiac assessment or respiratory check-up includes an audio auscultation during which one the medical specialist listens to sounds from the patient body with different tools (stethoscope, sonography). This shows how important sound analysis is for heart and lungs disease detection. During the IeBRI 2017 challenge, a database of 920 records acquired from 126 subject, was used to find a method that predicted if a respiratory cycle contains, or not, adventitious sounds like crackles, wheezes or both of them. The team which submits the best results reached around 50% of correct detection. Using a machine learning approach with a boosted decisional tree model and more audio features leads to the same results. A new approach consists in creating a new model at the patient level, which is able to decide if a patient sounds sick or not, by taking as input the predicted results of the first classification model. This new model permits to reach 85% of good predictions and could be used as a tool for helping doctors to make better diagnosis.

In calf rearing, bovine respiratory disease (BRD) is a major animal health challenge. Farmers incur severe economic losses due to BRD. Additional to economic costs, outbreaks of BRD impair the welfare of the animal and extra expertise and labour are needed to treat and care for the infected animals. Coughing is recognised as a clinical manifestation of BRD. Therefore, the monitoring of coughing in a calf house has the potential to detect cases of respiratory infection before they become too severe, and thus to limit the impact of BRD on both the farmer and the animal. The objective of this study was to develop an algorithm for detection of coughing sounds in a calf house. Sounds were recorded in four adjacent compartments of one calf house over two time periods (82 and 96 days). There were approximately 21 and 14 calves in each compartment over the two time-periods, respectively. The algorithm was developed using 445 min of sound data. These data contained 664 different cough references, which were labelled by a human expert. It was found that, during the first time period in all 3 of the compartments and during the second period in 2 out of 4 compartments, the algorithm worked very well (precision higher than 80%), while in the 2 other cases the algorithm worked well but the precision was less (66.6% and 53.8%). A relation between the number of calves diagnosed with BRD and the detected coughs is shown. ispartof: BIOSYSTEMS ENGINEERING vol:173 pages:45-56 status: published

Objective: Globally, tuberculosis (TB) remains one of the most deadly diseases. Although several effective diagnosis methods exist, in lower income countries clinics may not be in a position to afford expensive equipment and employ the trained experts needed to interpret results. In these situations, symptoms including cough are commonly used to identify patients for testing. However, self-reported cough has suboptimal sensitivity and specificity, which may be improved by digital detection. Approach: This study investigates a simple and easily applied method for TB screening based on the automatic analysis of coughing sounds. A database of cough audio recordings was collected and used to develop statistical classifiers. Main results: These classifiers use short-term spectral information to automatically distinguish between the coughs of TB positive patients and healthy controls with an accuracy of 78% and an AUC of 0.95. When a set of five clinical measurements is available in addition to the audio, this accuracy improves to 82%. By choosing an appropriate decision threshold, the system can achieve a sensitivity of 95% at a specificity of approximately 72%. The experiments suggest that the classifiers are using some spectral information that is not perceivable by the human auditory system, and that certain frequencies are more useful for classification than others. Significance: We conclude that automatic classification of coughing sounds may represent a viable low-cost and low-complexity screening method for TB.

Objectives To evaluate the accuracy of sonouroflowmetry in recording urinary flow parameters and voided volume. Methods A total of 25 healthy male volunteers (age 18–63 years) were included in the study. All participants were asked to carry out uroflowmetry synchronous with recording of the sound generated by the urine stream hitting the water level in the urine collection receptacle, using a dedicated cell phone. From 188 recordings, 34 were excluded, because of voided volume

It is known that acoustic heart sounds carry significant information about the mechanical activity of the heart. In this paper, we present a novel type of cardiac monitoring based on heart sound analysis. Specifically, we study two morphological features and their associations with physiological changes from the baseline state. The framework is demonstrated on recordings during laparoscopic surgeries of 15 patients. Insufflation, which is performed during laparoscopic surgery, provides a controlled, externally induced cardiac stress, enabling an analysis of each patient with respect to their own baseline. We demonstrate that the proposed features change during cardiac stress, and the change is more significant for patients with cardiac problems. Furthermore, we show that other well-known ECG morphology features are less sensitive in this specific cardiac stress experiment.

We present a multi-feature approach to the detection of cough and adventitious respiratory sounds. After the removal of near-silent segments, a vector of event boundaries is obtained and a proposed set of 126 features is extracted for each event. Evaluation was performed on a data set comprised of internal audio recordings from 18 patients. The best performance (F-measure = 0.69 ± 0.03; specificity = 0.90 ± 0.01) was achieved when merging wheezes and crackles into a single class of adventitious respiratory sounds.

The pattern of body weight gain during the commercial growing of broiler chickens is important to understand growth and feed conversion ratio of each flock. The application of sound analysis techniques has been widely studied to measure and analyze the amplitude and frequency of animal sounds. Previous studies have shown a significant correlation (P ≤ 0.001) between the frequency of vocalization and the age and weight of broilers. Therefore, the aim of this study was to identify and validate a model that describes the growth rate of broiler chickens based on the peak frequency of their vocalizations and to explore the possibility to develop a tool capable of automatically detecting the growth of the chickens based on the frequency of their vocalizations during the production cycle. It is part of an overall goal to develop a Precision Livestock Farming tool that assists farmers in monitoring the growth of broiler chickens during the production cycle. In the present study, sounds and body weight were continuously recorded in an intensive broiler farm during 5 production cycles. For each cycle the peak frequencies of the chicken vocalizations were used to estimate the weight and then they were compared with the observed weight of the birds automatically measured using on farm automated weighing devices. No significant difference is shown between expected and observed weights along the entire production cycles; this trend was confirmed by the correlation coefficient between expected and observed weights (r = 96%, P value ≤ 0.001). The identified model used to predict the weight as a function of the peak frequency confirmed that bird weight might be predicted by the frequency analysis of the sounds emitted at farm level. Even if the precision of the weighing method based on sounds investigated in this study has to be improved, it gives a reasonable indication regarding the growth of broilers opening a new scenario in monitoring systems in broiler houses.

Vocalisations are commonly expressed by gregarious animals, including cattle, as a form of short- and long-distance communication. They can provide conspecifics with meaningful information about the physiology, affective state and physical attributes of the caller. In cattle, calls are individually distinct meaning they assist animals to identify specific individuals in the herd. Consequently, there is potential for these vocalisations to be acoustically analysed to make inferences about how individual animals or herds are coping with their external surroundings, and then act on these signals to improve feed conversion efficiency, reproductive efficiency and welfare. In the case of dairy farming, where herd sizes are expanding and farmers are becoming more reliant on technologies to assist in the monitoring of cattle, the study of vocal behaviour could provide an objective, cost effective and non-invasive alternative to traditional measures of welfare. The vocalisations of cattle in response to calf separation, social isolation and painful husbandry procedures, alongside changes to feeding and oestrous activity are here reviewed. For future application of sound technology, research is first necessary to analyse the acoustic structure of cattle vocalisations and determine the specific information they encode. This review draws together the latest research in field of cattle bioacoustics highlighting how the source–filter theory and affective state dimensional approach can be adopted to decode this information and improve on-farm management.

In recent years, acoustical analysis of the swallowing mechanism has received considerable attention and because of many damages of invasive methods, it is preferred. This paper proposes acoustic-based method to separate dysphagia patients with reflux disorder from normal persons. In this work, we have used swallowing sound of 22 individuals (11 normal and 11 abnormal). Swallowing sound signals were recorded with sound recorder over the trachea and ambient noise was removed and spectral features were extracted from the sounds. Classification is done by non-linear support vector machines, using leave-one-out. According to the experimental results, the system can classify 66.1% of total swallow signals correctly (signal accuracy) and 95.7% of the total subject in a group of healthy and dysphagia patients (subject accuracy). The experimental results show that the proposed system can provide concrete features for clinicians to diagnose dysphagia in reflux patients.

Separating pediatric asthma from pediatric pneumonia is one of the major issues in remote areas. These diseases have overlapping symptoms, but require drastically different treatments. Existing guidelines for pneumonia classification in resource poor regions from The World Health Organization call for the use of bronchodilator test to separate asthma from pneumonia. However, bronchodilator is an expensive test to conduct and not easily available in remote areas. In this study, we propose an innovative and novel technique using cough sound analysis to separate pneumonia cases from asthma. In the work of this paper we analyzed cough sound data from 20 subjects (10 pneumonia and 10 asthma patients). Using mathematical features of cough sounds, an HMM classifier was trained to identify pneumonic cough and asthmatic cough. Then by computing Pneumonic Cough Index each patient was classified as either into pneumonia or asthma. Proposed method achieved an accuracy of 90% (sensitivity = 100% and specificity = 80%) in classifying pneumonia and asthma patients. Our results indicate that cough sound carry critical information which can be used to separate asthma patients from pneumonia. Proposed technique in this paper shows potential to become an alternative for bronchodilator test in the resource poor areas of the world.