Amalia Setyati

Cough Sound Analysis - A new tool for diagnosing Pneumonia

Pneumonia kills over 1,800,000 children annually throughout the world. Prompt diagnosis and proper treatment are essential to prevent these unnecessary deaths. Reliable diagnosis of childhood pneumonia in remote regions is fraught with difficulties arising from the lack of field-deployable imaging and laboratory facilities as well as the scarcity of trained community healthcare workers. In this paper, we present a pioneering class of enabling technology addressing both of these problems. Our approach is centered on automated analysis of cough and respiratory sounds, collected via microphones that do not require physical contact with subjects. We collected cough sounds from 91 patients suspected of acute respiratory illness such as pneumonia, bronchiolitis and asthma. We extracted mathematical features from cough sounds and used them to train a Logistic Regression classifier. We used the clinical diagnosis provided by the paediatric respiratory clinician as the gold standard to train and validate our classifier against. The methods proposed in this paper could separate pneumonia from other diseases at a sensitivity and specificity of 94% and 75% respectively, based on parameters extracted from cough sounds alone. Our method has the potential to revolutionize the management of childhood pneumonia in remote regions of the world.

Perbedaan Kadar Interferon Gamma pada Tuberkulosis Anak

Latar belakang . Tuberkulosis (TB) adalah penyakit akibat infeksi kuman Mycobacterium tuberculosis, suatu bakteri batang Gram positif. Salah satu sitokin yang diproduksi sel Th1 adalah interferon gamma (IFN-γ) yang berperan penting dalam mengeliminasi bakteri Mycobacterium tuberculosis. Terjadinya gangguan atau penurunan aktivitas sel Th1 dan sitokinnya yaitu IFN-γ cukup bermakna dalam memengaruhi mekanisme pertahanan tubuh terhadap penyakit TB. Manifestasi klinis penyakit TB terjadi karena adanya defisiensi imun, terutama imunitas selular. Tujuan . Mengkaji perbedaan kadar interferon gamma dilihat dari derajat lesi paru pada pasien TB anak. Metode . Penelitian cross sectional dilakukan di RSUP Dr. Sardjito and RSUD Sleman selama bulan Desember 2014. Subyek penelitian adalah anak kurang dari 15 tahun yang terdiagnosis TB menggunakan skor TB IDAI. Produksi interferon-gamma diukur dengan metode ELISA dan perbedaan kadarnya dibandingkan dengan derajat lesi paru. Hasil . Berdasarkan derajat lesi paru, kadar IFN-γ pada kasus tuberkulosis anak dengan lesi paru minimal (8,37±3,25) lebih tinggi daripada kasus dengan lesi paru sedang (3,52±1,75), dan lesi paru luas (4,83±2,78). Kesimpulan . Ada perbedaan rerata kadar IFN-γ serum TB anak berdasarkan derajat lesi paru minimal, sedang, dan luas, walaupun secara statistik tidak bermakna.

HMM Based Cough Sound Analysis for Classifying Pneumonia and Asthma in Pediatric Population

Separating pediatric asthma from pediatric pneumonia is one of the major issues in remote areas. These diseases have overlapping symptoms, but require drastically different treatments. Existing guidelines for pneumonia classification in resource poor regions from The World Health Organization call for the use of bronchodilator test to separate asthma from pneumonia. However, bronchodilator is an expensive test to conduct and not easily available in remote areas. In this study, we propose an innovative and novel technique using cough sound analysis to separate pneumonia cases from asthma. In the work of this paper we analyzed cough sound data from 20 subjects (10 pneumonia and 10 asthma patients). Using mathematical features of cough sounds, an HMM classifier was trained to identify pneumonic cough and asthmatic cough. Then by computing Pneumonic Cough Index each patient was classified as either into pneumonia or asthma. Proposed method achieved an accuracy of 90% (sensitivity = 100% and specificity = 80%) in classifying pneumonia and asthma patients. Our results indicate that cough sound carry critical information which can be used to separate asthma patients from pneumonia. Proposed technique in this paper shows potential to become an alternative for bronchodilator test in the resource poor areas of the world.

Hidden Markov model of cough from pediatric patients with respiratory infections

Cough is one of the early symptoms of the respiratory tract infections. Cough sound may indicate the physiology of respiratory tract impairment due to the infections. Inflammation, obstruction and excessive mucus may generate specific types of cough sound. In pediatric population, their cough sound may relate to the etiology of the respiratory diseases. Therefore, cough sound is very useful to support the diagnosis. In the physical examination, physicians may assess cough by listening to several episode of cough sounds. This process is similar to the way human recognize speeches. In this paper we present our work on the development of cough model using a Hidden Markov Model (HMM). The data for this work were collected from pediatric population diagnosed as pneumonia and asthma. Our developed model achieved the accuracy of 82.7% and 52.6% for pneumonia and asthma, respectively. It shows that HMM can be used to model different types of cough from respiratory diseases.