Serkan Nural's research while affiliated with Hatay Antakya State Hospital and other places

Publications (10)

Preprint
Full-text available
Auscultation is a method for diagnosis of especially internal medicine diseases such as cardiac, pulmonary and cardio-pulmonary by listening the internal sounds from the body parts. It is the simplest and the most common physical examination in the assessment processes of the clinical skills. In this study, the lung and heart sounds are recorded sy...
Preprint
Full-text available
Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the HilbertHuang Transform to the...
Article
Full-text available
Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the Hilbert-Huang Transform to the...
Article
Full-text available
Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the HilbertHuang Transform to the...
Article
The second order difference plot (SODP) is a nonlinear signal analysis method that visualizes two consecutive data points for many types of biomedical signals. The proposed method is based on analysing quantization of 3D-space which is originated using three consecutive data points in signal. The obtained 3D-SODP space was segmented into 3-10 space...
Conference Paper
Full-text available
Asthma is one of the most common chronic complaints estimated to affect about 300 million people worldwide. The auscultation sounds including lung sounds and pathological breathing sounds are significant diagnostic tools for chronic respiratory diseases. 10s of lung sounds, recorded from 12-channels with right and left focal points of posterior and...
Conference Paper
Full-text available
Chronic Obstructive Pulmonary Disease (COPD) is a completely untreatable disease that results in exposure of lungs to harmful dusts, gases or micro particles. In general practice, diagnosis of the COPD needs to be concretized with spirometry test and lung chest X-rays after auscultation of lung sounds [1]. In this study, it is aimed to diagnose the...
Article
Full-text available
Auscultation is a method for diagnosis of especially internal medicine diseases such as cardiac, pulmonary and cardio-pulmonary by listening the internal sounds from the body parts. It is the simplest and the most common physical examination in the assessment processes of the clinical skills. In this study, the lung and heart sounds are recorded sy...
Conference Paper
Full-text available
Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the Hilbert-Huang Transform to the l...
Conference Paper
Full-text available
The second order difference plot (SODP) is a data visualization method of two consecutive wave points that is inspired by the Chaos Theory for many erratic nonlinear biomedical signals. The proposed method plots three consecutive wave points in 3D space. The obtained 3D-SODP space is segmented into 3-10 sections plane sections using spheres and cub...

Citations

... The highest frequency of interest was determined to be 150 Hz, and therefore, taking into account Nyquist's theorem, 500 Hz was chosen as the sampling frequency, which is equivalent to a sampling time of 2 ms [7]. On the contrary, Altan et al. preferred using Hilbert-Huang Transform to find out features from lung sound [8]. ...
... Asthma is one of the minority classes in the ICBHI datset with only two sound recordings, making deep learning approaches unreliable for its inclusion in most classification methods used in [2], [10], [12]. Recently, Altan et al. [13] has shown an interesting result on asthma classification by utilizing lung sound data from their own recorded database. Hilbert transform (HHT) based time and frequency domain feature extraction process followed by a deep belief network (DBN) has been utilized and an accuracy rate of 84.61% has been achieved for binary class classification (asthma vs healthy). ...
... Using deep belief network (DBN) along with the features extracted from the modes, the authors had achieved 93.67% classification accuracy. For COPD categorization, Altan et al. [15] had suggested another framework that uses 3 dimensional second order difference plots (3D-SODP) to extract features from lung sound data and then fed them to DBN to distinguish two extreme severities of COPD namely COPD0 and COPD4. They achieved 95.84% accuracy rate for two class severity classification which is the first work on lung sound based COPD severity detection. ...
... The upper layers of the DBN represent features that are more abstract whereas the lower layers of the DBN learn simple features [1]- [3]. The DBN was implemented to electroencephalography signal to detect the brain activities of the patients in stroke [4]; electrocardiogram signal to detect coronary artery disease [5] and arrhythmia [6]; and lung sounds to diagnose asthma [7], to separate the patients with chronic obstructive pulmonary disease (COPD) from particularly the smokers who are potential COPD patients [8]. The DBN trains the model considering the capabilities of reaching the global minimum for the supervised training and high classification performances with fast, greedy, and layerwise pre-training [3]. ...
... In this study, 3 different open source data sets [7][8][9] were used and the results were examined. The audio data were separated as 1s time data and their attributes were extracted. ...
... [12] Tager enerji operatörü, kısa-zamanlı Fourier dönüşümü değerlerinden elde ettikleri göğüs seslerinden hesapladıkları kürtosis, entropi, ortalama çaprazlama değerleri yardımıyla, Altan vd. hırıltı seslerinin üç boyutlu ikinci derece fark haritalarını bölütlenmesini derin öğrenme algoritmaları kullanarak kronik obstruktif akciğer rahatsızlığının seviyelerinin teşhisini [13], Ulukaya vd. [14] önerdikleri Q değişkeni ayarlanabilir dalgacık dönüşümünü baz alan oranlı genişleme dalgacık dönüşümüyle çıkardıkları özniteliklerle hırıltı seslerinin tespitini ve astım hastalığının analizini gerçekleştirmişlerdir. ...