In this study, respiratory sounds of asthmatic patients and healthy individuals are analyzed and classified to diagnose asthma. Normal and asthmatic breath sound signals are divided into segments which include a single respiration cycle as inspiration and expiration. Analyses of these sound segments are carried out by using both discrete wavelet transform (DWT) and wavelet packet transform (WPT). Each sound segment is decomposed into frequency sub-bands using DWT and WPT. Feature vectors are constructed by extracting statistical features from the subbands. Artificial neural network (ANN) is used to classify respiratory sound signals as normal and level of asthmatic diseases (mild asthma, moderate asthma and severe asthma). The classification results of DWT and WPT are compared with each other in terms of classification accuracy. Index Terms-respiratory sounds, discrete wavelet transform, wavelet packet transform, artificial neural network
Apneic-event based sleep disorders are very common and affect greatly the daily life of people. However, diagnosis of these disorders by detecting apneic events are very difficult. Studies show that analyzes of airflow signals are effective in diagnosis of apneic-event based sleep disorders. According to these studies, diagnosis can be performed by detecting the apneic episodes of the airflow signals. This work deals with detection of apneic episodes on airflow signals belonging to Apnea-ECG (Electrocardiogram) and MIT (Massachusetts Institute of Technology) BIH (Bastons’s Beth Isreal Hospital) databases. In order to accomplish this task, three representative feature sets namely classic feature set, amplitude feature set and descriptive model feature set were created. The performance of these feature sets were evaluated individually and in combination with the aid of the random forest classifier to detect apneic episodes. Moreover, effective features were selected by OneR Attribute Eval Feature Selection Algorithm to obtain higher performance. Selected 28 features for Apnea-ECG database and 31 features for MITBIH database from 54 features were applied to classifier to compare achievements. As a result, the highest classification accuracies were obtained with the usage of effective features as 96.21% for Apnea-ECG database and 92.23% for MIT-BIH database. Kappa values are also quite good (91.80 and 81.96%) and support the classification accuracies for both databases, too. The results of the study are quite promising for determining apneic events on a minute-by-minute basis.
This study presents detection of pulmonary disorders using different spectral analysis methods such as fast Fourier transform, autoregressive and the autoregressive moving average. Power spectral densities of the sounds were estimated through these methods. Feature vectors were constructed by extracting statistical features from the PSDs. Created feature vectors were used as inputs into the artificial neural networks. Then performances of spectral analysis methods were compared according to classification accuracies, sensitivities and specificities. In this aspect, the study is a comparative study of different spectral analysis methods.
The diagnosis of obstructive sleep apnea hypopnea syndrome (OSASH) and making decision of treatment necessity with positive airway pressure (PAP) therapy are time consuming and costly processes. There were different approaches in literature to accomplish these processes successfully and as soon as possible by using physiological signals with selected feature extraction and machine learning techniques. To reach fastest and true result, selection of optimal physiological signal(s), feature extraction and learning techniques is important. This study aimed to identify apnea hypopnea index (AHI) subgroups of 120 subjects and thus diagnose of OSASH and determine the need for PAP therapy by applying Multifractal Detrended Fluctuation Analysis (MDFA) as a feature extraction technique to only single channel nasal cannula airflow signals. After the extracted features from airflow signals with MDFA were gone through feature selection phase, the selected features were evaluated in Random Forest classifier. With the implementation of all processes, OSAHS patients were discriminated from healthy subjects with 95.83% accuracy, 96.88% sensitivity and 93.75% specificity. 93.75% sensitivities and 93.75%, 100% and 96.88% specificities were obtained for 15≤AHI (PAP therapy necessary), 5≤AHI<15 (require additional information for PAP therapy decision) and AHI <5 (not require PAP therapy) subgroups, respectively.
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