Tracheal Breathing Research Articles

Design and Analysis of a Contact Piezo Microphone for Recording Tracheal Breathing Sounds.

Analysis of tracheal breathing sounds (TBS) is a significant area of study in medical diagnostics and monitoring for respiratory diseases and obstructive sleep apnea (OSA). Recorded at the suprasternal notch, TBS can provide detailed insights into the respiratory system's functioning and health. This method has been particularly useful for non-invasive assessments and is used in various clinical settings, such as OSA, asthma, respiratory infectious diseases, lung function, and detection during either wakefulness or sleep. One of the challenges and limitations of TBS recording is the background noise, including speech sound, movement, and even non-tracheal breathing sounds propagating in the air. The breathing sounds captured from the nose or mouth can interfere with the tracheal breathing sounds, making it difficult to isolate the sounds necessary for accurate diagnostics. In this study, two surface microphones are proposed to accurately record TBS acquired solely from the trachea. The frequency response of each microphone is compared with a reference microphone. Additionally, this study evaluates the tracheal and lung breathing sounds of six participants recorded using the proposed microphones versus a commercial omnidirectional microphone, both in environments with and without background white noise. The proposed microphones demonstrated reduced susceptibility to background noise particularly in the frequency ranges (1800-2199) Hz and (2200-2599) Hz with maximum deviation of 2 dB and 2.1 dB, respectively, compared to 9 dB observed in the commercial microphone. The findings of this study have potential implications for improving the accuracy and reliability of respiratory diagnostics in clinical practice.

Sealing-type Three-cavities Ventilation Joint: A New Device in Painless Bronchoscopy

Background: To observe the clinical effect and safety of the sealing-type three-cavities ventilation joint in painless bronchoscopy. To compare the respiratory mechanics between I-gel laryngeal mask and tracheal tube-controlled breath during bronchoscopy. Methods: 200 patients underwent bronchoscopy were recruited and randomly assigned to general anesthesia group (group Ⅰ, n = 100) and local anesthesia group (group Ⅱ, n = 100). General anesthesia group were divided into two groups, the I-gel laryngeal mask combined with sealing-type three-cavities ventilation joint group group(n=50) and the endotracheal tube combined with sealing-type three-cavities ventilation joint group(n=50). Patients in Group I were adopted by I-gel laryngeal mask or endotracheal tube with the sealing-type three-cavities ventilation joint after the induction anesthesia with remifentanil, propofol and succinylcholine. In group II, patients were anaesthetized with lidocaine and followed by 2mg/ kg propofol iv, and spontaneous respirations were retained with nasal cannula. All patients’ vital signs, endoscopic related adverse reactions and arterial blood gas analysis were recorded during the procedure. Results: Group I showed little changes of vital signs (P <0.05), and less adverse reaction such as the intraoperative hypoxia and intraoperative body movement (P <0.05), and no significant decrease of oxygen partial pressure (P <0.05). There is no significant difference in respiratory mechanics including tidal volume and airway pressure between two subgroups in group I(P>0.05). Conclusion: Sealing-type three-cavities ventilation joint prevents the oxygen deficit and makes it possible for us to examine patients through bronchoscope under general anesthesia without gas leakage. Moreover, sealing-type three-cavities ventilation joint provides safe and effective airway control while it does not change respiratory mechanics in endotracheal tube group compared with I-gel laryngeal mask makes endotracheal tube an alternative solution in bronchoscope. Take all these in consideration, sealing-type three-cavities ventilation joint proves to be a feasible method in bronchoscope.

Predicting Polysomnography Parameters from Anthropometric Features and Breathing Sounds Recorded during Wakefulness.

Background: The apnea/hypopnea index (AHI) is the primary outcome of a polysomnography assessment (PSG) for determining obstructive sleep apnea (OSA) severity. However, other OSA severity parameters (i.e., total arousal index, mean oxygen saturation (SpO2%), etc.) are crucial for a full diagnosis of OSA and deciding on a treatment option. PSG assessments and home sleep tests measure these parameters, but there is no screening tool to estimate or predict the OSA severity parameters other than the AHI. In this study, we investigated whether a combination of breathing sounds recorded during wakefulness and anthropometric features could be predictive of PSG parameters. Methods: Anthropometric information and five tracheal breathing sound cycles were recorded during wakefulness from 145 individuals referred to an overnight PSG study. The dataset was divided into training, validation, and blind testing datasets. Spectral and bispectral features of the sounds were evaluated to run correlation and classification analyses with the PSG parameters collected from the PSG sleep reports. Results: Many sound and anthropometric features had significant correlations (up to 0.56) with PSG parameters. Using combinations of sound and anthropometric features in a bilinear model for each PSG parameter resulted in correlation coefficients up to 0.84. Using the evaluated models for classification with a two-class random-forest classifier resulted in a blind testing classification accuracy up to 88.8% for predicting the key PSG parameters such as arousal index. Conclusions: These results add new value to the current OSA screening tools and provide a new promising possibility for predicting PSG parameters using only a few seconds of breathing sounds recorded during wakefulness without conducting an overnight PSG study.

Acoustic characterization of upper airway variations from wakefulness to sleep with respect to obstructive sleep apnea

The upper airway (UA) is in general thicker and narrower in obstructive sleep apnea (OSA) population than in normal. Additionally, the UA changes during sleep are much more in the OSA population. The UA changes can alter the tracheal breathing sound (TBS) characteristics. Therefore, we hypothesize the TBS changes from wakefulness to sleep are significantly correlated to the OSA severity; thus, they may represent the physiological characteristics of the UA. To investigate our hypothesis, we recorded TBS of 18 mild-OSA (AHI < 15) and 22 moderate/severe-OSA (AHI > 15) during daytime (wakefulness) and then during sleep. The power spectral density (PSD) of the TBS was calculated and compared within the two OSA groups and between wakefulness and sleep. The average PSD of the mild-OSA group in the low-frequency range (< 280Hz) was found to be decreased significantly from wakefulness to sleep (p-value < 10-4). On the other hand, the average PSD of the moderate/severe-OSA group in the high-frequency range (> 900Hz) increased marginally significantly from wakefulness to sleep (p-value <9 × 10-3). Our findings show that the changes in spectral characteristics of TBS from wakefulness to sleep correlate with the severity of OSA and can represent physiological variations of UA. Therefore, TBS analysis has the potentials to assist with diagnosis and clinical management decisions in OSA patients based on their OSA severity stratification; thus, obviating the need for more expensive and time-consuming sleep studies. Graphical abstract Tracheal breathing sound (TBS) changes from wakefulness to sleep and their correlation with Obstructive sleep apnea (OSA) were investigated in individuals with different levels of OSA severity. We also assessed the classification power of the spectral characteristics of these TBS for screening purposes. Consequently, we analyzed and compared spectral characteristics of TBS recorded during wakefulness (a combination of mouth and nasal TBS) to those during sleep for mild and moderate/severe OSA groups.

Removing of Snoring Segments from Tracheal Breathing Sounds using a Wavelet-based Algorithm.

Tracheal sounds represent information about the upper airway and respiratory airflow, however, they can be contaminated by the snoring sounds. The sound of snoring has spectral content in a wide range that overlaps with that of breathing sounds during sleep. For assessing respiratory airflow using tracheal breathing sound, it is essential to remove the effect of snoring. In this paper, an automatic and unsupervised wavelet-based snoring removal algorithm is presented. Simultaneously with full-night polysomnography, the tracheal sound signals of 9 subjects with different levels of airway obstruction were recorded by a microphone placed over the trachea during sleep. The segments of tracheal sounds that were contaminated by snoring were manually identified through listening to the recordings. The selected segments were automatically categorized based on including discrete or continuous snoring pattern. Segments with discrete snoring were analyzed by an iterative wave-based filtering optimized to separate large spectral components related to snoring from smaller ones corresponded to breathing. Those with continuous snoring were first segmented into shorter segments. Then, each short segments were similarly analyzed along with a segment of normal breathing extracted from the recordings during wakefulness. The algorithm was evaluated by visual inspection of the denoised sound energy and comparison of the spectral densities before and after removing snores, where the overall rate of detectability of snoring was less than 2%.Clinical Relevance- The algorithm provides a way of separating snoring pattern from the tracheal breathing sounds. Therefore, each of them can be analyzed separately to assess respiratory airflow and the pathophysiology of the upper airway during sleep.

Random forest analysis of tracheal breathing sounds for predicting obstructive sleep apnea

Random forest analysis of tracheal breathing sounds for predicting obstructive sleep apnea

Regularized logistic regression for obstructive sleep apnea screening during wakefulness using daytime tracheal breathing sounds and anthropometric information.

Obstructive sleep apnea (OSA) is a prevalent health problem. Developing a technology for quick OSA screening is momentous. In this study, we used regularized logistic regression to predict the OSA severity level of 199 individuals (116 males) with apnea/hypopnea index (AHI) ≥ 15 (moderate/severe OSA) and AHI < 5 (non-OSA) using their tracheal breathing sounds (TBS) recorded during daytime, while they were awake. The participants were guided to breathe through their nose, and then through their mouth at their deep breathing rate. The least absolute shrinkage and selection operator (LASSO) feature selection approach was used to select the discriminative features from the power spectra of the TBS and the anthropometric information. Using a five-fold cross-validation procedure, five different training sets and their corresponding blind-testing sets were formed. The average blind-testing classification accuracy over the five different folds was found to be 79.3% ± 6.1 with the sensitivity (specificity) of 82.2% ± 7.2% (75.8% ± 9.9%). The accuracy for the entire dataset was found to be 81.1% with sensitivity (specificity) of 84.4% (77.0%). The feature selection and classification procedures were intelligible and fast. The selected features were physiologically meaningful. Overall, the results show that TBS analysis can be used as a quick and reliable prediction of the presence and severity of OSA during wakefulness without a sleep study. Graphical abstract Wakefulness screening of obstructive sleep apnea using tracheal breathing sounds and anthropometric information by means of regularized logistic regression with the least absolute shrinkage and selection operator approach for feature selection and classification.

A Novel Decision Making Procedure during Wakefulness for Screening Obstructive Sleep Apnea using Anthropometric Information and Tracheal Breathing Sounds

Obstructive sleep apnea (OSA) is an underdiagnosed common disorder. Undiagnosed OSA, in particular, increases the perioperative morbidity and mortality risks for OSA patients undergoing surgery requiring full anesthesia. OSA screening using the gold standard, Polysomnography (PSG), is expensive and time-consuming. This study offers an objective and accurate tool for screening OSA during wakefulness by a few minutes of breathing sounds recording. Our proposed algorithm (AWakeOSA) extracts an optimized set (3–4) of breathing sound features specific to each anthropometric feature (i.e. age, sex, etc.) for each subject. These personalized group (e.g. age) classification features are then used to determine OSA severity in the test subject for that anthropomorphic parameter. Each of the anthropomorphic parameter classifications is weighted and summed to produce a final OSA severity classification. The tracheal breathing sounds of 199 individuals (109 with apnea/hypopnea index (AHI) < 15 as non-OSA and 90 with AHI ≥ 15 as moderate/severe-OSA) were recorded during wakefulness in the supine position. The sound features sensitive to OSA were extracted from a training set (n = 100). The rest were used as a blind test dataset. Using Random-Forest classification, the training dataset was shuffled 1200–6000 times to avoid any training bias. This routine resulted in 81.4%, 80.9%, and 82.1% classification accuracy, sensitivity, and specificity, respectively, on the blind-test dataset which was similar to the results for the out-of-bag-validation applied to the training dataset. These results provide a proof of concept for AWakeOSA algorithm as an accurate, reliable and quick OSA screening tool that can be done in less than 10 minutes during wakefulness.

Using tracheal breathing sounds and anthropometric information for screening obstructive sleep apnoea during wakefulness

Obstructive sleep apnoea (OSA) is a common yet underdiagnosed disorder. Undiagnosed OSA significantly increases perioperative morbidity and mortality for OSA patients undergoing surgery, requiring full anaesthesia. Tracheal breathing sounds characteristics during wakefulness have shown a high correlation with the apnoea–hypopnea index (AHI), while they are also affected by the anthropometric parameters, e.g., sex, age, etc. This study investigates the effects of the anthropometric parameters on our new quick objective OSA screening tool during wakefulness. Breathing sounds of 122 individuals (71 with AHI <15 as non-OSA and 51 with AHI > 15 as OSA) were recorded during wakefulness in the supine position. The spectra and bi-spectra of 81 (47 non-OSA) individuals’ signals, which were randomly selected, were analysed as a training dataset to extract the most significant features with the lowest sensitivity to the anthropometric parameters. Using a support vector machine (SVM) classifier, these features resulted in 72.1, 64.7 and 77.5% testing classification accuracy, sensitivity and specificity, respectively. We also investigated classifying subjects into subgroups related to each anthropometric parameter and incorporating a voting procedure. This routine resulted in 83.6, 74.5 and 90.1% testing classification accuracy, sensitivity and specificity, respectively. In conclusion, it is possible to positively utilise the anthropometric information to enhance the classification accuracy for a reliable OSA screening procedure during wakefulness.

Determining Breathing Sound Features Representative of Obstructive Sleep Apnea During Wakefulness with Least Sensitivity to Other Risk Factors

Obstructive sleep apnea (OSA) is one of the most common sleep disorders. OSA is also considered associated with some anthropometric risk factors such as sex, body mass index, age, etc. The anthropometric characteristics are used to screen for OSA during wakefulness when there is no time for a sleep study. However, in general they provide a poor specificity (~ 20%). Our proposed alternative screening OSA using tracheal breathing sound analysis during wakefulness has shown significantly higher specificity with a reasonable sensitivity. In this study, we investigated the effects of anthropometric characteristics on the breathing sounds and how they may affect the accuracy to detect OSA during wakefulness. Tracheal breathing sounds of 114 individuals [66 with apnea/hypopnea index (AHI) 15] were recorded during wakefulness in supine position. Spectra and bi-spectra of the signals of mild and severe OSA groups were analyzed to extract the most discriminative Tracheal breathing sounds features between the two groups. Then, the plausible effects of anthropometric characteristics on the breathing sounds were investigated. Results showed that different risk factors do affect the breathing sounds independent of OSA severity. In addition, our results suggest it is possible to find the best features with high sensitivity to OSA severity and least sensitivity to the confounding variables. A description of those features is provided in this paper. In conclusion, reducing the effects of the risk factors on the breathing sound features would result in a more reliable OSA screening tool.

Spectral and Higher Order Statistical Characteristics of Expiratory Tracheal Breathing Sounds During Wakefulness and Sleep in People with Different Levels of Obstructive Sleep Apnea

We investigated plausible changes in spectral and higher order statistical properties of tracheal respiratory sounds from wakefulness to sleep in relation to obstructive sleep apnea (OSA). Data consisted of expiratory sounds of 30 participants suspected of OSA during wakefulness and sleep, both recorded in supine position. Participants were divided into two groups of mild and severe OSA (15 in each group) based on their apnea/hypopnea index (AHI) per hour. Three different frequency-based features of their power spectra in addition to Kurtosis and Katz fractal dimension (KFD) were estimated from each normalized expiratory sound; they were compared within and between the groups. During wakefulness, the sounds average power at low-frequency components in severe group was lesser than that of the mild group. However, during sleep, the average power of high-frequency components in severe subjects was more than that of the mild group. The kurtosis value of both mild and severe OSA groups increased significantly from wakefulness to sleep using both mouth and nasal breathing sounds during wakefulness. The KFD increased significantly from wakefulness to sleep for both mild and severe OSA group using only nasal breathing sounds during wakefulness. These changes are indicative that the upper airway of severe OSA show more compliance and thickness compare to that of the mild OSA during both wakefulness and sleep and represent an increased stiffness during sleep. This implies a regional narrowing which cause both more compliance and stiffness simultaneously in different regions of the upper airway.

Erratum to: Obstructive Sleep Apnea Screening and Airway Structure Characterization During Wakefulness Using Tracheal Breathing Sounds

Erratum to: Obstructive Sleep Apnea Screening and Airway Structure Characterization During Wakefulness Using Tracheal Breathing Sounds

Obstructive Sleep Apnea Screening and Airway Structure Characterization During Wakefulness Using Tracheal Breathing Sounds

Screening for obstructive sleep apnea (OSA) disorder during wakefulness is challenging. In this paper, we present a set of tracheal breathing sounds characteristics with classification power for separating individuals with apnea/hypopnea index (AHI)≥10 (OSA group) from those with AHI≤5 (non-OSA group) during wakefulness. Tracheal breathing sound signals were recorded during wakefulness in supine position; subjects were instructed to have a few deep breaths through their nose, then through their mouth. Study participants were 147 individuals (80 males) referred to overnight polysomnography (PSG) assessment; their AHI scores were collected after their overnight-PSG study was completed. The signals were normalized; then, their power spectra were estimated. After conducting a multi-stage process for feature extraction and selection on a subset of training data, two spectral features showing significant differences between the two groups were selected for classification. These features showed a correlation of 0.42 with AHI. A 2-class support vector machine classifier with a linear kernel was used. Following this an exhaustive leave-two-out cross-validation was performed. The overall accuracies were 83.83 and 83.92% for training and testing datasets, respectively, while the overall sensitivity and specificity of the test datasets were 82.61 and 85.22%, respectively. We also applied the same method for anthropometric information (i.e., age, weight, etc.) as features, and they resulted in an overall accuracy of 77.6 and 76.2% for training and testing datasets, respectively. The results of this study show a superior classification power of respiratory sound features compared to anthropometric features for a quick screening of OSA during wakefulness. The relationship of the sound features and known morphological upper airway structure of OSA subjects are also discussed.

A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection

Sleep apnea (SA) in the form of Obstructive sleep apnea (OSA) is becoming the most common respiratory disorder during sleep, which is characterized by cessations of airflow to the lungs. These cessations in breathing must last more than 10 seconds to be considered an apnea event. Apnea events may occur 5 to 30 times an hour and may occur up to four hundred times per night in those with severe SA [1]. Nowadays, polysomnography (PSG) is a standard testing procedure to diagnose OSA which includes the monitoring of the breath airflow, respiratory movement, and oxygen saturation (SpO 2 ), body position, electroencephalography (EEG), electromyography (EMG), electrooculography (EOG), and electrocardiography (ECG). Therefore, a final diagnosis decision is obtained by means of medical examination of these recordings [2]. However, new simplified diagnostic methods and continuous screening of OSA is needed in order to have a major benefit of the treatment on OSA outcomes. In this regard, a portable monitoring system is developed to facilitate the self-administered sleep tests in familiar surroundings environment closer to the patients’ normal sleep habits. With only three data channels: tracheal breathing sounds, ECG and SpO 2 signals, a patient does not need hospitalization and can be diagnosed and receive feedback at home, which eases follow-up and retesting after treatment.

Estimation of Respiratory Rates Using the Built-in Microphone of a Smartphone or Headset.

This paper proposes accurate respiratory rate estimation using nasal breath sound recordings from a smartphone. Specifically, the proposed method detects nasal airflow using a built-in smartphone microphone or a headset microphone placed underneath the nose. In addition, we also examined if tracheal breath sounds recorded by the built-in microphone of a smartphone placed on the paralaryngeal space can also be used to estimate different respiratory rates ranging from as low as 6 breaths/min to as high as 90 breaths/min. The true breathing rates were measured using inductance plethysmography bands placed around the chest and the abdomen of the subject. Inspiration and expiration were detected by averaging the power of nasal breath sounds. We investigated the suitability of using the smartphone-acquired breath sounds for respiratory rate estimation using two different spectral analyses of the sound envelope signals: The Welch periodogram and the autoregressive spectrum. To evaluate the performance of the proposed methods, data were collected from ten healthy subjects. For the breathing range studied (6-90 breaths/min), experimental results showed that our approach achieves an excellent performance accuracy for the nasal sound as the median errors were less than 1% for all breathing ranges. The tracheal sound, however, resulted in poor estimates of the respiratory rates using either spectral method. For both nasal and tracheal sounds, significant estimation outliers resulted for high breathing rates when subjects had nasal congestion, which often resulted in the doubling of the respiratory rates. Finally, we show that respiratory rates from the nasal sound can be accurately estimated even if a smartphone's microphone is as far as 30cm away from the nose.

Sleep Stage Detection Using Tracheal Breathing Sounds: A Pilot Study.

Sleep stage detection is needed in many sleep studies and clinical assessments. Generally, sleep stages are identified using spectral analysis of electrocephologram (EEG) and electrooculogram (EOG) signals. This study, for the first time, has investigated the feasibility of detecting sleep stages using tracheal breathing sounds, and whether the change of breathing sounds due to sleeping stage differs at different periods of sleeping time; the motivation was seeking an alternative technique for sleep stage identification. The tracheal breathing sounds of 12 individuals, who were referred for full overnight polysomnography (PSG) assessment, were recorded using a microphone placed over the suprasternal notch, and analyzed using higher order statistical analysis. Five noise-and-snore-free breathing cycles from wakefulness, REM and Stage II of sleep were selected from each subject for analysis. Data of the REM and Stage II were selected from beginning, middle and close to end of sleeping time. Hurst exponent was calculated from the bispectra of the inspiratory sounds of each subject at each sleeping stage in different periods of sleeping time. The participants' sleep stage were determined by sleep lab technologists during the PSG study using EEG and EOG signals. The results show separate and non-overlapping clusters for wakefulness, REM and Stage II for each subject. Thus, using a simple linear classifier, we were able to classify REM and Stage II of each subject with 100% accuracy. In addition, the results show that the same pattern existed as long as the REM and Stage II segments were close (less than 3h) to each other in terms of time.

Breathing sounds spectral and higher order statistics changes from wakefulness to sleep in apneic and non-apneic people.

Breathing sounds analysis conveys valuable information in relation to obstructive sleep apnea (OSA) during both sleep and wakefulness. In this study, we investigated whether the breathings sounds spectral and higher order statistics characteristics (HOS) change from wakefulness to sleep, and more importantly whether this change is associated with severity of OSA. Tracheal breathing sounds of 6 individuals with severe OSA and 6 non-OSA individuals during wakefulness and stage 2 of sleep, both in supine position, were used in this study. The sounds were recorded simultaneously with full overnight polysomnography (PSG) assessment. First, the sounds of 5 noise-free breathing cycles were extracted and sequestered into inspiratory and expiratory phase segments manually for each study subject. After normalizing each sound segment to its energy, spectral and HOS features were calculated. Several features including the median bispectral frequency (MBF), spectral bandwidth (BW) and bispectrum Harmonic Mean (HM) were found to change statistically significantly from wakefulness to sleep mostly in severe OSA group but not as much in non-OSA group. The most prominent and consistent change between the two groups of OSA and non-OSA was observed in MBF; it changed from wakefulness to sleep in the two groups in an opposite manner; this observation is congruent with the hypothesis that the upper airway in OSA population has an increased non-homogeneity.

Identification of Obstructive Sleep Apnea patients from tracheal breath sound analysis during wakefulness in polysomnographic studies.

Obstructive Sleep Apnea (OSA) is currently diagnosed by a full nocturnal polysomnography (PSG), a very expensive and time-consuming method. In previous studies we were able to distinguish patients with OSA through formant frequencies of breath sound during sleep. In this study we aimed at identifying OSA patients from breath sound analysis during wakefulness. The respiratory sound was acquired by a tracheal microphone simultaneously to PSG recordings. We selected several cycles of consecutive inspiration and exhalation episodes in 10 mild-moderate (AHI<;30) and 13 severe (AHI>=30) OSA patients during their wake state before getting asleep. Each episode's formant frequencies were estimated by linear predictive coding. We studied several formant features, as well as their variability, in consecutive inspiration and exhalation episodes. In most subjects formant frequencies were similar during inspiration and exhalation. Formant features in some specific frequency band were significantly different in mild OSA as compared to severe OSA patients, and showed a decreasing correlation with OSA severity. These formant characteristics, in combination with some anthropometric measures, allowed the classification of OSA subjects between mild-moderate and severe groups with sensitivity (specificity) up to 88.9% (84.6%) and accuracy up to 86.4%. In conclusion, the information provided by formant frequencies of tracheal breath sound recorded during wakefulness may allow identifying subjects with severe OSA.

Applying cybernetic technology to diagnose human pulmonary sounds.

Chest auscultation is a crucial and efficient method for diagnosing lung disease; however, it is a subjective process that relies on physician experience and the ability to differentiate between various sound patterns. Because the physiological signals composed of heart sounds and pulmonary sounds (PSs) are greater than 120Hz and the human ear is not sensitive to low frequencies, successfully making diagnostic classifications is difficult. To solve this problem, we constructed various PS recognition systems for classifying six PS classes: vesicular breath sounds, bronchial breath sounds, tracheal breath sounds, crackles, wheezes, and stridor sounds. First, we used a piezoelectric microphone and data acquisition card to acquire PS signals and perform signal preprocessing. A wavelet transform was used for feature extraction, and the PS signals were decomposed into frequency subbands. Using a statistical method, we extracted 17 features that were used as the input vectors of a neural network. We proposed a 2-stage classifier combined with a back-propagation (BP) neural network and learning vector quantization (LVQ) neural network, which improves classification accuracy by using a haploid neural network. The receiver operating characteristic (ROC) curve verifies the high performance level of the neural network. To expand traditional auscultation methods, we constructed various PS diagnostic systems that can correctly classify the six common PSs. The proposed device overcomes the lack of human sensitivity to low-frequency sounds and various PS waves, characteristic values, and a spectral analysis charts are provided to elucidate the design of the human-machine interface.

Design of a Signal Conditioning Device for Remote Breath and Swallowing Sounds Recording

Design of a Signal Conditioning Device for Remote Breath and Swallowing Sounds Recording