Wheeze Detection Research Articles

Detection of Wheeze Sounds in Respiratory Disorders: A Deep Learning Approach

Respiratory disorders, including chronic obstructive pulmonary disease (COPD) and asthma, are major causes of death globally. Early diagnosis of these conditions is essential for effective treatment. Auscultation of the lungs is the traditional diagnostic method, which has drawbacks such as subjectivity and susceptibility to environmental interference. To overcome these limitations, this study presents a novel approach for wheeze detection using deep learning methods. This approach includes the usage of artificial data created by employing the open ICBHI dataset with the aim of improvement in generalization of learning models. Spectrograms that were obtained as the output of the Short-Time Fourier Transform analysis were employed in feature extraction. Two labeling approaches were used for model comparison. The first approach involved labeling after wheezing occurred, and the second approach assigned labels directly to the time steps where wheezing patterns are seen. Wheeze event detection was performed by constructing four RNN-based models (CNN-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU). It was observed that labeling wheeze events directly resulted in more precise detection, with exceptional performance exhibited by the CNN-BiLSTM model. This approach demonstrates the potential for improving respiratory disorders diagnosis and hence leading to improved patient care.

An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning.

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient's chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time-frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set-outperforming the deterministic time-frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.

Comparison of Moving Average and Differential Operation for Wheeze Detection in Spectrograms

Comparison of Moving Average and Differential Operation for Wheeze Detection in Spectrograms

StethAid: A Digital Auscultation Platform for Pediatrics.

(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.

A telemedicine software application for asthma severity levels identification using wheeze sounds classification

Early and precise knowledge of asthma severity levels may help in effective precautions, proper medication, and follow-up planning for the patients. Keeping this in view, we propose a telemedicine application that is capable of automatically identifying the severity level of asthma patients by using machine learning techniques. Respiratory sounds of 111 asthmatic patients were collected. The 111-patient dataset consisted of 34 mild, 36 moderate, and 41 severe levels. Data was collected from two auscultation locations, i.e., from the trachea and lower lung base. The first dataset was used for the testing and training (cross-validation) of classifiers while a second database was used for the validation of the system. Mel-frequency cepstral coefficient (MFCC) features were extracted to discriminate the severity levels. Then, ensemble and k-nearest neighbor (KNN) classifiers were used for classification. This was performed on both auscultation locations jointly and individually. The developed telemedicine application, based on MFCC features and classifiers, automatically detects wheeze and classifies it into a severity level. The extracted features showed significant differences (p < 0.05) for all severity levels. Based on the testing, training, and validation results, the performance of the ensemble and KNN classifiers were comparable. MFCC-based features classification provides maximum accuracy of 99%, 90%, and 89% for mild, moderate, and severe samples, respectively. The average rate of wheeze detection was observed to be 93%. The maximum accuracy of validation of the telemedicine application was found to be 57%, 72%, and 76% for mild, moderate, and severe levels, respectively.

Continuous Monitoring Versus Intermittent Auscultation of Wheezes in Patients Presenting With Acute Respiratory Distress

BackgroundAuscultation for an extended period of time using a wearable stethoscope enables objective computerized analysis and longitudinal assessment of lung sounds. However, this auscultation method differs from bedside auscultation in that clinicians are not present to optimize the quality of auscultation. No prior studies have compared these two auscultation methods. ObjectiveThe aim of this study was to compare intermittent auscultation using a conventional stethoscope with continuous auscultation using a wearable stethoscope for wheeze detection in patients who present with acute respiratory distress. MethodsPatients presenting to the emergency department with acute respiratory distress were enrolled. The Strados Remote Electronic Stethoscope Platform (RESP™) was used for continuous auscultation, and intermittent auscultation was performed using a U.S. Food and Drug Administration–cleared electronic stethoscope. A recording was made with an electronic stethoscope. Subsequently, continuous recording was made using RESP™, which continued until the patient was admitted or discharged from the emergency department. The number of captured wheezes in each recording was counted and validated by two board-certified physicians. ResultsFrom May 2018 to May 2019, 43 patients were enrolled in the study. Three patients were excluded from analysis due to incomplete audio recording data. The mean length of recording was 62.3 min for continuous auscultation and 0.7 min for intermittent auscultation; 77.5% (31 of 40) of intermittent recordings contained wheezes, in contrast to 85% (34 of 40) of continuous recordings. ConclusionsExtending the duration of auscultation using a wearable stethoscope in a noisy clinical environment showed comparable performance to standard of care intermittent auscultation in identifying patients who have wheezes.

Remote Monitoring of Patient Respiration with Mask Attachment—A Pragmatic Solution for Medical Facilities

Remote monitoring of vital signs in infectious patients minimizes the risks of viral transmissions to healthcare professionals. Donning face masks could reduce the risk of viral transmissions and is currently practiced in medical facilities. An acoustic-sensing device was attached to face masks to assist medical facilities in remotely monitoring patients’ respiration rate and wheeze occurrence. Usability and functionality studies of the modified face mask were evaluated on 16 healthy participants. Participants were blindfolded throughout the data collection process. Respiratory rates of the participants were evaluated for one minute. The wheeze detection algorithm was assessed by playing 176 wheezes and 176 normal breaths through a foam mannequin. No discomfort was reported from the participants who used the modified mask. The mean error of respiratory rate was found to be 2.0 ± 1.3 breath per minute. The overall accuracy of the wheeze detection algorithm was 91.9%. The microphone sensor that was first designed to be chest-worn has been proven versatile to be adopted as a mask attachment. The current findings support and suggest the use of the proposed mask attachment in medical facilities. This application can be especially helpful in managing a sudden influx of patients in the face of a pandemic.

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

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

The impact of a digital wheeze detector on parental disease management of pre-school children suffering from wheezing\u2014a pilot study

BackgroundViral airway infections are a major reason for doctor’s visits at pre-school age, especially when associated with wheezing. While proper treatment requires adequate recognition of airway obstruction, caretakers are often struggling with this judgment, consequently leading to insufficient or late treatment and an unnecessary discomfort of the patient. Digital technologies may serve to support parental decision taking. The aim of the present pilot study is to acquire data on the feasibility of recruitment and observation procedures for a randomized controlled trial on the impact of a digital wheeze detector in a home management setting of pre-school wheezing.MethodsThis single-armed pilot study enrolled patients with a doctor’s diagnosis of wheezing aged 9 to 72 months. Participants were asked to use a digital wheeze detector (WheezeScan, Omron Healthcare, Japan) 2×/day for 30 days and record the child’s respiratory symptoms, detection of wheezing, and medication intake via an electronic diary (eDiary) app. Demographic and clinical data were collected at the recruitment visit. The asthma control test and the Parent Asthma Management Self-Efficacy Scale (PAMSES) were assessed both, at recruitment and follow-up.ResultsTwenty families were recruited and completed the monitoring. All but one completed the follow-up after 30 days. The recruitment procedures were feasible, and adherence to daily monitoring reached an average of 81%. The use of the wheeze detector was rated as uncomplicated. Parents detected wheezing without digital support in only 22/708 (3.1%) of the recorded events. By contrast, the wheeze detector indicated an airway obstruction in 140/708 (19.8%) of the recordings.ConclusionIn parallel to feasible recruitment procedures, we observed good usability of the wheeze detection device and high adherence to eDiary recording. The positive outcomes show that the WheezeScan may empower parents by increasing their capacity for wheeze detection. This deserves to be investigated in a larger randomized controlled trial.

Wheeze Recognition Algorithm for Remote Medical Care Device in Children: Validation Study.

BackgroundSince 2020, peoples’ lifestyles have been largely changed due to the COVID-19 pandemic worldwide. In the medical field, although many patients prefer remote medical care, this prevents the physician from examining the patient directly; thus, it is important for patients to accurately convey their condition to the physician. Accordingly, remote medical care should be implemented and adaptable home medical devices are required. However, only a few highly accurate home medical devices are available for automatic wheeze detection as an exacerbation sign.ObjectiveWe developed a new handy home medical device with an automatic wheeze recognition algorithm, which is available for clinical use in noisy environments such as a pediatric consultation room or at home. Moreover, the examination time is only 30 seconds, since young children cannot endure a long examination time without crying or moving. The aim of this study was to validate the developed automatic wheeze recognition algorithm as a clinical medical device in children at different institutions.MethodsA total of 374 children aged 4-107 months in pediatric consultation rooms of 10 institutions were enrolled in this study. All participants aged ≥6 years were diagnosed with bronchial asthma and patients ≤5 years had reported at least three episodes of wheezes. Wheezes were detected by auscultation with a stethoscope and recorded for 30 seconds using the wheeze recognition algorithm device (HWZ-1000T) developed based on wheeze characteristics following the Computerized Respiratory Sound Analysis guideline, where the dominant frequency and duration of a wheeze were >100 Hz and >100 ms, respectively. Files containing recorded lung sounds were assessed by each specialist physician and divided into two groups: 177 designated as “wheeze” files and 197 as “no-wheeze” files. Wheeze recognitions were compared between specialist physicians who recorded lung sounds and those recorded using the wheeze recognition algorithm. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files, and evaluated the influence of age and sex on the wheeze detection sensitivity.ResultsDetection of wheezes was not influenced by age and sex. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 96.6%, 98.5%, 98.3%, and 97.0%, respectively. Wheezes were automatically detected, and heartbeat sounds, voices, and crying were automatically identified as no-wheeze sounds by the wheeze recognition algorithm.ConclusionsThe wheeze recognition algorithm was verified to identify wheezing with high accuracy; therefore, it might be useful in the practical implementation of asthma management at home. Only a few home medical devices are available for automatic wheeze detection. The wheeze recognition algorithm was verified to identify wheezing with high accuracy and will be useful for wheezing management at home and in remote medical care.

Development of Automatic Wheeze Detection Algorithm for Children With Asthma

Asthma is a symptom of tracheal obstruction caused by bronchospasm, and it is among the most prevalent chronic obstructive pulmonary diseases. Auscultation is the most commonly used approach for the clinical diagnosis of asthma. However, recognizing wheezes through auscultation requires experienced physicians, and this approach is not sufficiently objective. Therefore, developing a method for recognizing wheezes objectively is crucial. Most studies have used the spectral features of lung sounds to detect wheezes; however, they have not achieved sufficiently high performance owing to the poor discrimination of spectral features. Several studies have attempted to extract wheezing features from lung sound spectrograms; however, their approaches were easily affected by variations in the wheezing frequency and background noise. The present study proposes a novel automatic wheeze detection algorithm for extracting lung sound features in the time–frequency domain and automatically detecting wheezes. The proposed algorithm applies canonical correlation analysis to successfully detect wheezing features in a lung sound spectrogram. Moreover, a neural network technique is used to effectively classify healthy and wheezing sounds. The experimental results indicated that the proposed algorithm showed excellent performance in detecting wheezing.

A wheeze recognition algorithm for practical implementation in children.

The detection of wheezes as an exacerbation sign is important in certain respiratory diseases. However, few highly accurate clinical methods are available for automatic detection of wheezes in children. This study aimed to develop a wheeze detection algorithm for practical implementation in children. A wheeze recognition algorithm was developed based on wheezes features following the Computerized Respiratory Sound Analysis guidelines. Wheezes can be detected by auscultation with a stethoscope and using an automatic computerized lung sound analysis. Lung sounds were recorded for 30 s in 214 children aged 2 months to 12 years and 11 months in a pediatric consultation room. Files containing recorded lung sounds were assessed by two specialist physicians and divided into two groups: 65 were designated as "wheeze" files, and 149 were designated as "no-wheeze" files. All lung sound judgments were agreed between two specialist physicians. We compared wheeze recognition between the specialist physicians and using the wheeze recognition algorithm and calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files to evaluate the influence of age on the wheeze detection sensitivity. The detection of wheezes was not influenced by age. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 100%, 95.7%, 90.3%, and 100%, respectively. The wheeze recognition algorithm could identify wheezes in sound files and therefore may be useful in the practical implementation of respiratory illness management at home using properly developed devices.

Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes

BackgroundManual auscultation to detect abnormal breath sounds has poor inter-observer reliability. Digital stethoscopes with artificial intelligence (AI) could improve reliable detection of these sounds. We aimed to independently test the abilities of AI developed for this purpose.MethodsOne hundred and ninety two auscultation recordings collected from children using two different digital stethoscopes (Clinicloud™ and Littman™) were each tagged as containing wheezes, crackles or neither by a pediatric respiratory physician, based on audio playback and careful spectrogram and waveform analysis, with a subset validated by a blinded second clinician. These recordings were submitted for analysis by a blinded AI algorithm (StethoMe AI) specifically trained to detect pathologic pediatric breath sounds.ResultsWith optimized AI detection thresholds, crackle detection positive percent agreement (PPA) was 0.95 and negative percent agreement (NPA) was 0.99 for Clinicloud recordings; for Littman-collected sounds PPA was 0.82 and NPA was 0.96. Wheeze detection PPA and NPA were 0.90 and 0.97 respectively (Clinicloud auscultation), with PPA 0.80 and NPA 0.95 for Littman recordings.ConclusionsAI can detect crackles and wheeze with a reasonably high degree of accuracy from breath sounds obtained from different digital stethoscope devices, although some device-dependent differences do exist.

Disease control in patients with asthma and respiratory symptoms (wheezing, cough) during sleep

IntroductionThe Global Initiative for Asthma (GINA)-defined criteria for asthma control include questions about daytime symptoms, limitation of activity, nocturnal symptoms, need for reliever treatment and patients’ satisfaction. Patients with nocturnal symptoms like wheezing and cough often suffer from lower sleep quality and impaired daytime performance. The lack of an appropriate method for standardized and objective monitoring of respiratory symptoms leads to difficulties in asthma management. The aim of this study is to present a new method for automated wheeze and cough detection during sleep and to assess the actual level of asthma control by the Asthma Control Test (ACT).MethodsRespiratory symptoms like wheezing and cough were recorded with the LEOSound-Monitor for one night in 55 asthmatic patients in their individual domestic setting. Patients were asked to assess their level of asthma subjectively with the ACT. The study consisted of 37 women and 18 men, with a mean age of 41 years, and a mean BMI of 27 kg/m2. Most of the patients had been taking an ICS/LABA combination and would resort to a SABA as their rescue medication.Results60% of the participants were classed as having controlled, and 40% were classed as having partially- or uncontrolled asthma. During sleep wheezing was found in 8 of the 55 asthma patients (14.5%) and coughing was found in 30 patients (54.5%). The median ACT score in wheezing-patients was 14, while in non-wheezing patients it was 21. Uncontrolled asthma was found in 6 of the 8 wheezing-patients. Coughing versus non-coughing patients did not show a significant difference in the ACT-score (20, 22 respectively).ConclusionWheezing is a sign of uncontrolled asthma. The ACT-score in wheezing patients is worse compared to patients without wheezing. LEOSound proofed to be a useful tool in providing an objective evaluation of respiratory symptoms, like coughing and wheezing. In clinical practice, this may allow an improvement in asthma therapy.

Evaluation of Mel-Frequency Cepstrum for Wheeze Analysis.

Monitoring of wheezes is an integral part of managing Chronic Respiratory Diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD). Recently, there is a growing interest in automatic detection of wheezes and the use of Mel-Frequency Cepstral Coefficients (MFCC) have been shown to achieve encouraging detection performance. While the successful use of MFCC for identifying wheezes has been demonstrated, it is not clear which MFCC coefficients are actually useful for detecting wheezes. The objective of this paper is to characterize and study the effectiveness of individual coefficients in discriminating between wheezes and normal respiratory sounds. The coefficients have been evaluated in terms of histogram dissimilarity and linear separability. Further, a comparison between the use of single coefficient against other combinations of coefficients is also presented. The results demonstrate MFCC-2 coefficient to be significantly more effective than all the other coefficients in discriminating between wheezes and normal respiratory sounds sampled at 8000 Hz.

Evaluation of features for classification of wheezes and normal respiratory sounds.

Chronic Respiratory Diseases (CRDs), such as Asthma and Chronic Obstructive Pulmonary Disease (COPD), are leading causes of deaths worldwide. Although both Asthma and COPD are not curable, they can be managed by close monitoring of symptoms to prevent worsening of the condition. One key symptom that needs to be monitored is the occurrence of wheezing sounds during breathing since its early identification could prevent serious exacerbations. Since wheezing can happen randomly without warning, a long-term monitoring system with automatic wheeze detection could be extremely helpful to manage these respiratory diseases. This study evaluates the discriminatory ability of different types of feature used in previous related studies, with a total size of 105 individual features, for automatic identification of wheezing sound during breathing. A linear classifier is used to determine the best features for classification by evaluating several performance metrics, including ranksum statistical test, area under the sensitivity-–specificity curve (AUC), F1 score, Matthews Correlation Coefficient (MCC), and relative computation time. Tonality index attained the highest effect size, at 87.95%, and was found to be the feature with the lowest p-value when ranksum significance test was performed. Third MFCC coefficient achieved the highest AUC and average optimum F1 score at 0.8919 and 82.67% respectively, while the highest average optimum MCC was obtained by the first coefficient of a 6th order LPC. The best possible combination of two and three features for wheeze detection is also studied. The study concludes with an analysis of the different trade-offs between accuracy, reliability, and computation requirements of the different features since these will be highly useful for researchers when designing algorithms for automatic wheeze identification.

(Invited) Paper Based Wearable Standalone Wheezing Sensor - Not a Low Dimensional System

We present a sensor made from paper using a Do-It-Yourself (DIY) integration strategy that can be attached to the human chest like a stethoscope for real-time asthma symptom monitoring. The sensor is designed such that it resonates around the dominant frequency of wheezing (a common symptom of asthma). This helps the sensor produce large output signals, thus it can be directly integrated with a microprocessor without the need for signal amplification circuits. Matched filtering is used to extract and detect features from the acquired chest sounds for wheeze detection. The sensor successfully detects wheezing from the human chest, even when subjected to background noise and other sounds made to imitate the lungs. The sensor is connected to a smartphone via Bluetooth, enabling signal processing and further integration into digital medical electronic systems based on the Internet of Things (IoT). Bending, cyclic pressure, heat, and sweat tests are performed on the sensor to evaluate its performance under the harsh conditions of real-life scenarios.

System-Level Power Consumption Analysis of the Wearable Asthmatic Wheeze Quantification

Long-term quantification of asthmatic wheezing envisions an m-Health sensor system consisting of a smartphone and a body-worn wireless acoustic sensor. As both devices are power constrained, the main criterion guiding the system design comes down to minimization of power consumption, while retaining sufficient respiratory sound classification accuracy (i.e., wheeze detection). Crucial for assessment of the system-level power consumption is the understanding of trade-off between power cost of computationally intensive local processing and communication. Therefore, we analyze power requirements of signal acquisition, processing, and communication in three typical operating scenarios: (1) streaming of uncompressed respiratory signal to a smartphone for classification, (2) signal streaming utilizing compressive sensing (CS) for reduction of data rate, and (3) respiratory sound classification onboard the wearable sensor. Study shows that the third scenario featuring the lowest communication cost enables the lowest total sensor system power consumption ranging from 328 to 428 μW. In such scenario, 32-bit ARM Cortex M3/M4 cores typically embedded within Bluetooth 4 SoC modules feature the optimal trade-off between onboard classification performance and consumption. On the other hand, study confirms that CS enables the most power-efficient design of the wearable sensor (216 to 357 μW) in the compressed signal streaming, the second scenario. In such case, a single low-power ARM Cortex-A53 core is sufficient for simultaneous real-time CS reconstruction and classification on the smartphone, while keeping the total system power within budget for uncompressed streaming.

Asthmatic Wheeze Detection From Compressively Sensed Respiratory Sound Spectra.

Quantification of wheezing by a sensor system consisting of a wearable wireless acoustic sensor and smartphone performing respiratory sound classification may contribute to the diagnosis, long-term control, and lowering treatment costs of asthma. In such battery-powered sensor system, compressive sensing (CS) was verified as a method for simultaneously cutting down power cost of signal acquisition, compression, and communication on the wearable sensor. Matching real-time CS reconstruction algorithms, such as orthogonal matching pursuit (OMP), have been demonstrated on the smartphone. However, their lossy performance limits the accuracy of wheeze detection from CS-recovered short-term Fourier spectra (STFT), when using existing respiratory sound classification algorithms. Thus, here we present a novel, robust algorithm tailored specifically for wheeze detection from the CS-recovered STFT. The proposed algorithm identifies occurrence and tracks multiple individual wheeze frequency lines using hidden Markov model. The algorithm yields 89.34% of sensitivity, 96.28% of specificity, and 94.91% of accuracy on Nyquist-rate sampled respiratory sounds STFT. It enables for less than 2% loss of classification accuracy when operating over STFT reconstructed by OMP, at the signal compression ratio of up to 4 $\times$ (classification from only 25% signal samples). It features execution speed comparable to referent algorithms, and offers good prospects for parallelism.

Feature extraction techniques for low-power ambulatory wheeze detection wearables.

Presence of wheezes in breathing sounds has been associated with several respiratory and pulmonary diseases. In this paper we present a novel low-complexity wheeze detection method based on frequency contour tracking for automatic wheeze detection. Two hardware friendly variants of the algorithm have also been proposed. Applying the proposed feature extraction algorithm we achieved very high classification accuracy (> 99%) at considerably low computational complexity (3×-6×) compared to earlier methods and the power consumption of the proposed method is shown to be significantly less (70×-100×) compared to `record and transmit' strategy in wearable devices.