Pulmonary crackle characterization: approaches in the use of discrete wavelet transform regarding border effect, mother-wavelet selection, and subband reduction

Introduction Crackles are discontinuous, non-stationary respiratory sounds and can be characterized by their duration and frequency. In the literature, many techniques of filtering, feature extraction, and classification were presented. Although the discrete wavelet transform (DWT) is a well-known tool in this area, issues like signal border extension, mother-wavelet selection, and its subbands were not properly discussed. Methods In this work, 30 different mother-wavelets 8 subbands were assessed, and 9 border extension modes were evaluated. The evaluations were done based on the energy representation of the crackle considering the mother-wavelet and the border extension, allowing a reduction of not representative subbands. Results Tests revealed that the border extension mode considered during the DWT affects crackle characterization, whereas SP1 (Smooth-Padding of order 1) and ASYMW (Antisymmetric-Padding (whole-point)) modes shall not be used. After DWT, only 3 subbands (D3, D4, and D5) were needed to characterize crackles. Finally, from the group of mother-wavelets tested, Daubechies 7 and Symlet 7 were found to be the most adequate for crackle characterization. Discussion DWT can be used to characterize crackles when proper border extension mode, mother-wavelet, and subbands are taken into account.

Edema agudo del pulmón en una gestante / Acute pulmonary edema in a pregnant woman

El edema agudo del pulmón es la acumulación de líquido en los alvéolos pulmonares que impide la normal oxigenación de la sangre y ocasiona hipoxia tisular. Se trata de una urgencia médica que debe identificarse y tratarse rápidamente para evitar una alta mortalidad materna y perinatal. Con el objetivo de describir las características de un caso de edema agudo del pulmón en una gestante, se presentó el tema, ocurrido en el Hospital Docente Ginecobstétrico de Guanabacoa en el mes de febrero del año 2014 y su posterior evolución. La paciente de 39 años de edad, sin antecedentes patológicos personales con historia obstétrica de G5 P1 A3, que a las 36 semanas comenzó con cifras de presión arterial elevadas y acude al cuerpo de guardia por cefalea e hipertensión arterial. A la postre inicia con disnea, tos y expectoración espumosa diagnosticándose un edema agudo del pulmón, para lo cual se aplica tratamiento médico y la interrupción del embarazo por cesárea, obteniéndose recién nacido de 2100g y Apgar 9-9. La evolución de la paciente fue satisfactoria y el recién nacido, aunque bajo peso, evolucionó satisfactorio. El edema agudo del pulmón es una complicación obstétrica en la que siempre debemos pensar.

Acute pulmonary edema is the accumulation of fluid in the alveoli that prevents normal oxygenation of the blood and causes tissue hypoxia. This is a medical emergency that should be promptly identified and treated to avoid high maternal and perinatal mortality. The issue was presented in order to describe the features of a case of acute lung edema in a pregnant woman, who was treated at the Gynecobstetric Teaching Hospital in Guanabacoa from February 2014 and her subsequent evolution. The 39-year-old woman, with no personal medical history and with G5P1A3 obstetric history, began to have high blood pressure at 36 weeks and she came to the emergency room due to headache and hypertension. Eventually she started with dyspnea, cough, and frothy expectoration. Acute lung edema was diagnosed. Medical treatment and pregnancy termination by caesarean section was applied, resulting in a 2100g newborn with Apgar 9-9. This patient´s evolution was satisfactory and the newborn, although underweight, evolved satisfactory. Acute pulmonary edema is an obstetric complication which we should always keep in mind.

GMM y LDA aplicado a la detección de enfermedades pulmonares / GMM and LDA Applied to Lung Diseases Detection

El propósito de este artículo es presentar metodologías que pueden ser usadas para la valoración cuantitativa de los sonidos del pulmón, así como los indicadores de desórdenes respiratorios. En este contexto, se realizaron experimentos utilizando señales normales y anormales de la respiración (LS), las cuales fueron modeladas y evaluadas utilizando principalmente la base de datos RALE y señales de sujetos saludables y no saludables, logrando hasta un 98% de eficiencia. En la práctica médica la evaluación de enfermedades respiratorias involucra a la auscultación, pero la aplicación de métodos de análisis cuantitativos de señales podría mejorar estas valoraciones. En particular, se sugiere una metodología de evaluación acústica basada en representaciones de vectores acústicos MFCC (Coeficientes Cepstrales en Frecuencia Mel), GMM (Modelos Mezclados Gaussianos) y LDA (Análisis Discriminante Lineal). Estas técnicas podrían asistir en un análisis más amplio, identificación y diagnóstico de desórdenes pulmonares manifestados por sonidos respiratorios peculiares tales como sibilancias, crepitancias y asma, y distinguiéndolos de los sonidos respiratorios normales.

This study presents experimentally tested methods, which can be used for a quantitative assessment of respiratory sounds as the indicators of pulmonary disorders. In particular, conducted experiments considered both normal and abnormal lung sounds (LS). As a part of the RALE Database, signals were recorded from healthy subjects and those with respiratory disorders. Current medical practices including evaluation of respiratory diseases often involve qualitative and frequently subjective auscultation. However, the application of quantitative signal analysis methods could improve the assessments of these diseases. In particular, we utilized acoustic evaluation methodologies based on the MFCC (Mel frequency Cepstral Coefficients) acoustic vectors representation, GMM (Gaussian Mixed Models), and LDA (Linear Discriminant Analysis). To assure the validity of determined class models representing diagnostic classification, the LS signals were cross validated within sequential sets of respiratory cycles for a given subject as well as cross correlated within the specific groups of subjects representing particular conditions of normal or given class of abnormal pulmonary functions. Higher order MFCC vectors, including 9, 10 and 11 Gaussian mixtures, resulted in improved classification of the LS attributes, reached up to 98% of efficiency recognition. This documented automated classification of LS makes it suitable for a more efficient mass screening of respiratory disorders. In particular, the presence of peculiar sounds such as crackles and wheezes lead to more robust models thus reflecting the useful applicability of the presented diagnostic tool. These techniques can assist in broader analysis, identification, and diagnosis of pulmonary disorders manifested by peculiar auscultatory findings.