An Overview Of Breath Phase Detection – Techniques & Applications

The main aim of this study is to provide an overview on the state of the art techniques (acoustic and non-acoustic approaches) involved in breath phase detection and to highlight applications where breath phase detection is vital. Both acoustic and non-acoustic approaches are summarized in detail. The non-acoustic approach involves placement of sensors or flow measurement devices to estimate the breath phases, whereas the acoustic approach involves the use of sophisticated signal processing methods on respiratory sounds to detect breath phases. This article also briefly discusses the advantages and disadvantages of the acoustic and non-acoustic approaches of breath phase detection. The literature reveals that recent advancements in computing technology open avenues for researchers to apply sophisticated signal processing techniques and artificial intelligence algorithms to detect the breath phases in a non-invasive way. Future works that can be implemented after detecting the breath phases are also highlighted in this article

Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features

This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. Method: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods. Results and conclusion: All statistical comparisons exhibited a significant difference (p < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informativ

Wheeze Sound Analysis Using Computer-Based Techniques: A Systematic Review

Wheezes are high pitched continuous respiratory acoustic sounds which are produced as a result of airway obstruction. Computer-based analyses of wheeze signals have been extensively used for parametric analysis, spectral analysis, identification of airway obstruction, feature extraction and diseases or pathology classification. While this area is currently an active field of research, the available literature has not yet been reviewed. This systematic review identified articles describing wheeze analyses using computer-based techniques on the SCOPUS, IEEE Xplore, ACM, PubMed and Springer and Elsevier electronic databases. After a set of selection criteria was applied, 41 articles were selected for detailed analysis. The findings reveal that 1) computerized wheeze analysis can be used for the identification of disease severity level or pathology, 2) further research is required to achieve acceptable rates of identification on the degree of airway obstruction with normal breathing, 3) analysis using combinations of features and on subgroups of the respiratory cycle has provided a pathway to classify various diseases or pathology that stem from airway obstructio

Recommendations Related To Wheeze Sound Data Acquisition

In the field of computerized respiratory sounds,a reliable data set with a sufficient number of subjects is required for the development of wheeze detection algorithm or for further analysis.Validated and accurate data is a critical issue in the field of research.In this study,the protocol related to wheeze sound data acquisition is discussed.Previously,most articles focused on wheeze detection or its parametric analysis,but no consideration was given to data acquisition.Second major purpose of this study is to exhibit particulars of our dataset which was attained for future analysis.We compile a database with a sufficient and reliable number of cases with all essential details,in contrast to commercially available wheeze sound data used for research,freely available online data on websites and data used to train medical students for auscultation

Fuzzy Logic Controller Design for Intelligent Drilling System

An intelligent drilling system can be commercially very profitable in terms of reduction in crude material and labor involvement. The use of fuzzy logic based controller in the intelligent cutting and drilling operations has become a popular practice in the ever growing manufacturing industry. In this paper, a fuzzy logic controller has been designed to select the cutting parameter more precisely for the drilling operation. Specifically, different input criterion of machining parameters are considered such as the tool and material hardness, the diameter of drilling hole and the flow rate of cutting fluid. Unlikethe existing fuzzy logic based methods, which use only two input parameters, the proposed system utilizes more input parameters to provide spindle speed and feed rate information more precisely for the intelligent drilling operation

Adaptive Neuro-Fuzzy Inference System For Breath Phase Detection And Breath Cycle Segmentation

The monitoring of the respiratory rate is vital in several medical conditions,including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls.Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial.Objectives:This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system.Methods:The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated.The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation.To evaluate the performance of the proposed method,the root mean square error (RMSE) and correlation coefficient values were calculated and analysed,and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset.Results:The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance,revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069

A survey on automated wheeze detection systems for asthmatic patients

The purpose of this paper is to present an evidence of automated wheeze detection system by a survey that can be very beneficial for asthmatic patients. Generally, for detecting asthma in a patient, stethoscope is used for ascertaining wheezes present. This causes a major problem nowadays because a number of patients tend to delay the interpretation time, which can lead to misinterpretations and in some worst cases to death. Therefore, the development of automated system would ease the burden of medical personnel. A further discussion on automated wheezes detection system will be presented later in the paper. As for the methodology, a systematic search of articles published as early as 1985 to 2012 was conducted. Important details including the hardware used, placement of hardware, and signal processing methods have been presented clearly thus hope to help and encourage future researchers to develop commercial system that will improve the diagnosing and monitoring of asthmatic patients

Analysis Of Wheeze Sounds During Tidal Breathing According To Severity Levels In Asthma Patients

This study aimed to statistically analyze the behavior of time-frequency features in digital recordings of wheeze sounds obtained from patients with various levels of asthma severity (mild, moderate, and severe), and this analysis was based on the auscultation location and/or breath phase. Method: Segmented and validated wheeze sounds were collected from the trachea and lower lung base (LLB) of 55 asthmatic patients during tidal breathing maneuvers and grouped into nine different datasets. The quartile frequencies F25, F50, F75, F90 and F99, mean frequency (MF) and average power (AP) were computed as features, and a univariate statistical analysis was then performed to analyze the behavior of the time-frequency features. Results: All features generally showed statistical significance in most of the datasets for all severity levels [v2 ¼ 6.021–71.65, p < 0.05, g2 ¼ 0.01–0.52]. Of the seven investigated features, only AP showed statistical significance in all the datasets. F25, F75, F90 and F99 exhibited statistical significance in at least six datasets [v2 ¼ 4.852–65.63, p < 0.05, g2 ¼ 0.01–0.52], and F25, F50 and MF showed statistical significance with a large g2 in all trachea-related datasets [v2 ¼ 13.54–55.32, p < 0.05, g2 ¼ 0.13–0.33]. Conclusion: The results obtained for the time-frequency features revealed that (1) the asthma severity levels ofn patients can be identified through a set of selected features with tidal breathing, (2) tracheal wheeze sounds are more sensitive and specific predictors of severity levels and (3) inspiratory and expiratory wheeze sounds are almost equally informativ

Erosion of esophageal stent into left main bronchus causing airway compromise

Covered or uncovered self-expanding metal stents are currently used for the palliative treatment of neoplastic esophageal strictures or compressions and esophageal leaks or fistulas due to malignancies. Erosion of esophageal stents into the respiratory tract is a rare complication and that too has been reported mostly as an early complication within few days or weeks. Here, we present the case of a 31-year-old female, who presented with a late complication of an esophageal stent eroding into the left main bronchus causing respiratory distress. She was stented for a benign corrosive esophageal stricture following caustic soda ingestion 3 years ago. She underwent a thoracotomy and closure of esophagobronchial fistula along with laparoscopic esophagectomy and gastric pull through. Postoperatively, patient developed an anastomotic leak which was corrected by placing a temporary stent

A performance comparison of wheeze feature extraction methods for asthma severity levels classification

Asthma is a chronic disease that requires monitoring and treatment throughout the patient's lifetime. The common adventitious sounds related to asthma are wheezes. A study that has classified the severity of asthma using wheezes are still lacking in the field, therefore, the purpose of this work is to compare feature extraction methods for the classification of asthma severity level. Three types of features opted are mel frequency cepstral coefficients (MFCC); short time energy (STE); auto-regressive model and k-nearest neighbor (KNN) classifier is used in representing the performance of the feature used. Based on the overall performance between the features, MFCC features and KNN classifier shows the best and the highest performance with 95.92%, 96.33% and 98.42% average accuracy, sensitivity and specificity value obtained compared to STE that only obtained the highest average accuracy, sensitivity and specificity value of 84.94%, 87.33% and 95% respectively while AR features only obtained the highest average accuracy, sensitivity and specificity value of 49.43%, 52.17%, and 82.79% respectively