Pal, 2022 - Google Patents
A novel method for automatic separation of pulmonary crackles from normal breath soundsPal, 2022
View PDF- Document ID
- 16141666167923645086
- Author
- Pal R
- Publication year
External Links
Snippet
Pulmonary crackles are an important physiological parameter for evaluating lung condition of an individual and usually determined at auscultation by conventional stethoscope. The presence of crackles is generally an early indication of the disease and their number per …
- 206010011376 Crepitations 0 title abstract description 672
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B7/00—Instruments for auscultation
- A61B7/02—Stethoscopes
- A61B7/04—Electric stethoscopes
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0402—Electrocardiography, i.e. ECG
- A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B7/00—Instruments for auscultation
- A61B7/003—Detecting lung or respiration noise
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4818—Sleep apnoea
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02411—Detecting, measuring or recording pulse rate or heart rate of foetuses
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
- A61B5/053—Measuring electrical impedance or conductance of a portion of the body
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
- A61B5/168—Evaluating attention deficit, hyperactivity
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L17/00—Speaker identification or verification
- G10L17/26—Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Islam et al. | Multichannel lung sound analysis for asthma detection | |
| US10765399B2 (en) | Programmable electronic stethoscope devices, algorithms, systems, and methods | |
| Reichert et al. | Analysis of respiratory sounds: state of the art | |
| US11712198B2 (en) | Estimation of sleep quality parameters from whole night audio analysis | |
| Cavusoglu et al. | An efficient method for snore/nonsnore classification of sleep sounds | |
| US7479115B2 (en) | Computer aided diagnosis of lung disease | |
| Moussavi | Fundamentals of Respiratory System and Sounds Analysis | |
| Lin et al. | Automatic Wheezing Detection Based on Signal Processing of Spectrogram and Back‐Propagation Neural Network | |
| CN112971839B (en) | Heart sound classification method based on feedforward convolution neural network | |
| Dokur | Respiratory sound classification by using an incremental supervised neural network | |
| Mayorga et al. | Modified classification of normal lung sounds applying quantile vectors | |
| Balasubramanian et al. | Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds. | |
| McLane et al. | Comprehensive analysis system for automated respiratory cycle segmentation and crackle peak detection | |
| Pal et al. | Automatic breathing phase identification based on the second derivative of the recorded lung sounds | |
| Pal | A novel method for automatic separation of pulmonary crackles from normal breath sounds | |
| Phettom et al. | Automatic identification of abnormal lung sounds using time-frequency analysis and convolutional neural network | |
| Sofwan et al. | Normal and murmur heart sound classification using linear predictive coding and k-Nearest neighbor methods | |
| Gupta et al. | Correlating spirometry findings with auscultation sounds for diagnosis of respiratory diseases | |
| CN116108345B (en) | A second heart sound width split detection method based on parameter estimation | |
| Yamashita | Acoustic HMMs to detect abnormal respiration with limited training data | |
| Mondal et al. | Respiratory sounds classification using statistical biomarker | |
| Harman | Lung Sounds Ventilation Cycle Segmentation and Classify Healthy, Asthma and COPD | |
| Pouyani et al. | A Combined Model for Noise Reduction of Lung Sound Signals Based on Empirical Mode Decomposition and Artificial Neural Network | |
| Mondal et al. | Diagnosing of the lungs status using morphological anomalies of the signals in transformed domain | |
| Bandyopadhyaya et al. | Estimation of lung sound cycle span using spectro-temporal respiratory frequency evaluation |