A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms

Jaber, Mustafa Musa; Khalil, Sura; Shakeel, P. Mohamed; Burhanuddin, M. A.; Mohammed, Mohammed Abdulameer; Yussof, Salman

doi:10.1016/j.measurement.2020.107883

Cited by 18 publications

(13 citation statements)

References 38 publications

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“…Ensemble learning has been used in many different applications such as classification of birdsong [10], credit risk evaluation [11], to improve deep learning performance [12], a telemedicine tool framework for lung sounds classification [13].…”

Section: Mehmet Safa Bi̇ngöl Mechatronic Engineering Department Facultmentioning

confidence: 99%

Classification of Mushroom Data Set by Ensemble Methods

Yıldırım¹

2024

RIiM

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Due to disease of mushrooms, it is very important to classify mushrooms for predicting the best quality mushrooms. There are many methods to analyse the main parts of mushrooms. For above mentioned descriptions; in this simulation study five types of classification algorithm are employed to predict the structure of mushrooms. Mushroom dataset is used to predict the classes of mushrooms. The results are improved that these methods will be used to predict exact mushrooms features and classifications in real time approaches.

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Section: Mehmet Safa Bi̇ngöl Mechatronic Engineering Department Facultmentioning

confidence: 99%

Classification of Mushroom Data Set by Ensemble Methods

Yıldırım¹

2024

RIiM

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“…7(a)). Typically, respiration rates (inhalation and exhalation) has been detected using lung sound [40][41][42][43][44][45][46][47]. However, lung sounds are relatively small and has noises from heartbeat and blood vessels.…”

Section: • Respiration Rate Detection Algorithmmentioning

confidence: 99%

Development of a Novel Wireless Multi-Channel Stethograph System for Monitoring Cardiovascular and Cardiopulmonary Diseases

Zhang

Maddipatla

Narakathu

et al. 2021

Preprint

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A multi-channel stethograph system was designed and developed as an electronic auscultation system for graphic recording of heart, lung and trachea (HLT) sounds non-invasively through a set of 16 acoustic sensors. The multi-channel stethograph system was fabricated by placing 16 microphone based acoustic sensor in a CNC machined Delrin® housing cases that are covered using diaphragms. Among the 16 acoustic sensors, 14 were positioned in a memory foam pad and 2 were placed directly on the heart and trachea to acquire sounds simultaneously from the lungs, heart and trachea. The sounds acquired from the 16 acoustic sensors were processed through a custom designed and fabricated 16-channel PCB for signal conditioning. A National Instruments (NI) 9205 data acquisition device (DAQ) along with a NI 9191 wireless chassis was used to acquire and wirelessly transmit the data from the 16-channel PCB to a Wi-Fi enabled device such as a PC/tablet. A custom LabVIEW program was developed on a Wi-Fi enabled PC/tablet to record the data from the DAQ. In addition, a MATLAB program was developed to convert the recorded data from the acoustic sensors into 16 audio files (for audio playback) and plot the waveforms in time and frequency domain as well as spectrogram for visual examination of any abnormal patterns in inhalation and exhalation. This provides critical information on the presence of wheezes, crackles and rhonchi sounds as well as abnormal heartbeat and respiration rate which helps in analyzing the condition of heart and lungs. The graphically displayed HLT sounds will help physicians in the clinical diagnosis and monitoring of lung and heart disorders, particularly chronic obstructive pulmonary disease (COPD), asthma, pneumonia, and congestive heart failure by providing objective evidence.

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“…The distance from a sample point to the hyper plane is (19) To maximize the distance, is minimized. The training target of SVM is shown as (20) A Lagrangian is selected for optimization: (21) By setting the derivatives of L to zero with respect to ω and b, ω is obtained as follows: (22) The training target is reformulated as follows: (23) To solve the non-separable case, the regularization factors C are introduced and reformulated Eq. ( 23): (24) To reduce the operational complexity of the inner products, the kernel functions are used to replace the inner product: (25) The regular method used to obtain the coefficients is the sequential minimal optimization (SMO) algorithm [39].…”

Section: A Support Vector Machinementioning

confidence: 99%

“…The model of the BP neural network is defined as follows: (27) if the nonlinear function η( ) is defined as (28) the network is regarded as a two-layer-network. If the number of layers increases, the function η( ) has the same form as (23). The structure of a two-layer BP neural network is shown in Figure 11, which is divided into an input layer, a middle layer, and an output layer.…”

Section: B Bp Neural Networkmentioning

confidence: 99%

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Detection of Respiratory Sounds Based on Wavelet Coefficients and Machine Learning

et al. 2020

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Respiratory sounds reveal important information of the lungs of patients. However, the analysis of lung sounds depends significantly on the medical skills and diagnostic experience of the physicians and is a time-consuming process. The development of an automatic respiratory sound classification system based on machine learning would, therefore, be beneficial. In this study, 705 respiratory sound signals (240 crackles, 260 rhonchi, and 205 normal respiratory sounds) were acquired from 130 patients. We found that similarities between the original and wavelet decomposed signals reflected the frequency of the signals. The Gaussian kernel function was used to evaluate the wavelet signal similarity. We combined the wavelet signal similarity with the relative wavelet energy and wavelet entropy as the feature vector. A 5-fold cross-validation was applied to assess the performance of the system. The artificial neural network model, which was applied, achieved the classification accuracy and classified the respiratory sound signals with an accuracy of 85.43%.

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A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms

Cited by 18 publications

References 38 publications

Classification of Mushroom Data Set by Ensemble Methods

Classification of Mushroom Data Set by Ensemble Methods

Development of a Novel Wireless Multi-Channel Stethograph System for Monitoring Cardiovascular and Cardiopulmonary Diseases

Detection of Respiratory Sounds Based on Wavelet Coefficients and Machine Learning

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