Automated lung sound analysis in patients with pneumonia.
@article{Murphy2004AutomatedLS, title={Automated lung sound analysis in patients with pneumonia.}, author={Raymond L. H. Murphy and Andrey Vyshedskiy and Verna-Ann Power-Charnitsky and Dhirendra S. Bana and Patricia M Marinelli and Anna Wong-Tse and Rozanne Paciej}, journal={Respiratory care}, year={2004}, volume={49 12}, pages={ 1490-7 } }
OBJECTIVE
To determine whether objectively detected lung sounds were significantly different in patients with pneumonia than those in asymptomatic subjects, and to quantify the pneumonia findings for teaching purposes.
METHODS
At a community teaching hospital we used a multi-channel lung sound analyzer to examine a learning sample of 50 patients diagnosed with pneumonia and 50 control subjects. Automated quantification and characterization of the lung sounds commonly recognized to be…
116 Citations
Developing a Reference of Normal Lung Sounds in Healthy Peruvian Children
- MedicineLung
- 2014
Lung sound extracted features varied significantly with child characteristics and lung site, and this work offers a novel, reproducible tool for sound analysis in real-world environments.
Towards an unsupervised device for the diagnosis of childhood pneumonia in low resource settings: Automatic segmentation of respiratory sounds
- Medicine2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- 2016
A novel concept of unsupervised tool for the diagnosis of childhood pneumonia that relies on the automated analysis of respiratory sounds as recorded by a point-of-care electronic stethoscope is presented and opens the doors to a new family of un supervised respiratory sound analyzers that could improve future versions of case management algorithms for the diagnoses of pneumonia in low-resources settings.
Digital auscultation as a diagnostic aid to detect childhood pneumonia: A systematic review
- MedicineJournal of global health
- 2022
Very limited evidence is found on the diagnostic performance of digital auscultation with automated analysis to diagnose pneumonia in children, irrespective of the disease condition or age of the participants.
Classify Respiratory Abnormality in Lung Sounds Using STFT and a Fine-Tuned ResNet18 Network
- Computer Science2022 IEEE Biomedical Circuits and Systems Conference (BioCAS)
- 2022
This work took a data-driven approach to classify abnormal lung sounds and compared the performance using three different feature extraction techniques, which are short-time Fourier transformation (STFT), Mel spectrograms, and Wav2vec, as well as three different classifiers, including pre-trained ResNet18, LightCNN, and Audio Spectrogram Transformer.
Automated methods for cough assessments and applications in screening paediatric respiratory diseases
- Medicine
- 2014
This thesis aims to demonstrate the efforts towards in-situ applicability of EMMARM, as to provide real-time information about the concrete mechanical properties of EMT and its applications in the oil industry.
A data mining approach for acoustic diagnosis of cardiopulmonary disease
- Medicine
- 2008
The results show that performing computerized lung auscultation offers a low-cost, non-invasive diagnostic procedure that gives doctors better clinical utility especially in situations when x-rays and CT scans are not available.
Validation of Automatic Wheeze Detection in Patients with Obstructed Airways and in Healthy Subjects
- MedicineThe Journal of asthma : official journal of the Association for the Care of Asthma
- 2008
The present findings demonstrate that the wheeze detection algorithm has good accuracy, sensitivity, specificity, negative predictive value and positive predictive value for wheez detection in regional analyses with a single sensor and multiple sensors.
Automated Analysis of Crackles in Patients with Interstitial Pulmonary Fibrosis
- MedicinePulmonary medicine
- 2011
Computer analysis of crackles at the bedside has the potential of aiding clinicians in diagnosing IPF more easily and thus helping to avoid medication errors.
Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis.
- MedicineRespiratory medicine
- 2011
An Automated System towards Diagnosis of Pneumonia using Pulmonary Auscultations
- Computer Science2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)
- 2019
An automated system for diagnosis of Pneumonia based on auscultations is proposed, which decomposes original signal into its constituent components known as intrinsic mode functions (IMFs) and extracts characteristic features by fusion of Mel frequency cepstral coefficients (MFCC) and time domain features.
References
SHOWING 1-10 OF 24 REFERENCES
Diagnosing pneumonia by physical examination: relevant or relic?
- MedicineArchives of internal medicine
- 1999
The degree of interobserver agreement was highly variable for different physical examination findings and the most valuable examination maneuvers in detecting pneumonia were unilateral rales and rales in the lateral decubitus position.
Clinical utility of chest auscultation in common pulmonary diseases.
- MedicineAmerican journal of respiratory and critical care medicine
- 1994
It is concluded that auscultatory differences exist among common pulmonary conditions and that statistical models based on auscULTatory data perform well in predicting diagnostic categories.
Does this patient have community-acquired pneumonia? Diagnosing pneumonia by history and physical examination.
- MedicineJAMA
- 1997
Some studies have shown that the absence of any vital sign abnormalities or any abnormalities on chest auscultation substantially reduces the likelihood of pneumonia to a point where further diagnostic evaluation may be unnecessary.
The prevalence of auscultatory crackles in subjects without lung disease.
- MedicineChest
- 1982
It is concluded that crackles, heard over the anterior chest during inspiration from low lung volumes, are not necessarily adventitious sounds.
The prevalence and character of crackles (rales) in young women without significant lung disease.
- MedicineThe American review of respiratory disease
- 1982
Examination of the quality, timing, and anatomic distribution of the crackles in apparently normal subjects suggests that they can often be distinguished from those resulting from diseases such as bronchitis, interstitial fibrosis, and congestive heart failure.
Separation of pulmonary disorders with two-dimensional discriminant analysis of crackles.
- MedicineClinical physiology
- 1996
Two-dimensional discriminant analysis of crackles has a better ability to separate pulmonary disorders than does a single-dimensional analysis, and can enhance the diagnostic power of acoustic pulmonary studies.
Influence of age on symptoms at presentation in patients with community-acquired pneumonia.
- MedicineArchives of internal medicine
- 1997
Respiratory and nonrespiratory symptoms are less commonly reported by older patients with pneumonia, even after controlling for the increased comorbidity and illness severity in these older patients.
Clinical findings associated with radiographic pneumonia in nursing home residents.
- MedicineThe Journal of family practice
- 2001
A simple clinical prediction rule can identify residents at very high risk of pneumonia and if validated in other studies, physicians could consider treating such residents without obtaining a chest radiograph.
Validation of an Automatic Crackle ( Rale ) Counter 1 , 2
- Medicine
- 2001
A computer-based system to count discontinuous sounds heard on chest auscultation, and compared audible, waveform, and computer crackle counts from subjects with and without cardiopulmonary disorders is compared.
Visual lung-sound characterization by time-expanded wave-form analysis.
- PhysicsThe New England journal of medicine
- 1977
Time-expanded wave form analysis provides reproducible visual displays that allow documentation of the differentiating features of lung sounds and enhances the diagnostic utility of the sounds.