A Novel Method for Automatic Identification of Breathing State

Niu, Jie; Cai, Maolin; Shi, Yan; Ren, Shuai; Xu, Weiqing; Gao, Wei; Luo, Zujin; Reinhardt, Joseph M.

doi:10.1038/s41598-018-36454-5

Cited by 17 publications

(22 citation statements)

References 30 publications

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“…In the study, the inspiration moment can be estimated by calculating Equation (16) using the inspiration template matrices given in Figure 9 and Equation (16). The inspiration moments (shown in red arrows) were recorded at 40 kHz subgroup sample frequency and shown in Figure 10.…”

Section: Lung Motion Signaturementioning

confidence: 99%

“…Understanding respiration sound characteristics has been one of the earliest and popular methods of detecting early respiratory illnesses with abnormal lung sounds [ 14 , 15 , 16 , 17 ]. It is understood that the coordinated contraction of respiratory muscles such as the diaphragm and external intercostals increases the ribcage and the chest’s rising [ 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the respiratory motion can also be estimated by analysis of electrocardiogram (ECG) variations. The heart rate increases during inspiration; during expiration, it decreases again [ 16 ]. Thus, an integrated approach of using respiratory sounds and cardiovascular physiological effects simultaneously can help model the breathing patterns, such as respiratory rate, tidal volume, and diaphragmatic activation, more precisely.…”

Section: Introductionmentioning

confidence: 99%

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Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

George

Moon

Lee

2021

Sensors

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Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

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Section: Lung Motion Signaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

George

Moon

Lee

2021

Sensors

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“…The audio signal is segmented in frames of 150 s with an overlap of 10 s. The developed system employs four features from the audio signal excerpt of the trachea. These are the signal power, as well as three complexity metrics: the Shannon information [13], the Tsallis entropy [14][15][16] and the zero-crossing ratio [17]. The architecture of the system comprises of 5 independent apnea/hypopnea detectors.…”

Section: Breathing Activity Detection and Apnea/hypopnea Detection Frmentioning

confidence: 99%

Breathing sound detector as a means to identify possible apneic periods from tracheal sound recordings

Potirakis

Korompili

Kokkalas

et al. 2020

Proceedings of 1st International Electronic Conference on Applied Sciences

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Tracheal sound represents an easily acquired signal, particularly popular in the evolution of smartphone-based systems for Sleep Apnea Syndrome diagnosis. The syndrome is characterized by partial or complete breath cessation for at least 10 s. The developed algorithms mainly rely on neural networks focusing on the extraction of the apneic episodes' count per sleeping hour, defined as the Apnea/Hypopnea Index. Though reported highly accurate, neural networks may be severely affected by the inter-and intra-patient breathing sound variability. Alternatively, breathing detection algorithms can contribute in identifying the dominant sound patterns within the apnea events. In this work, we employ zero-crossing rate, signal power, Tsallis entropy and Shannon information to discriminate breathing from silent frames. These features are extracted independently by tracheal sound recordings from 178 patients undergoing a hospital sleep study. Apneas correspond to silent periods detected by at least one of the four features, while hypopneas correspond to periods of reduced signal power. The algorithm presents increased sensitivity (80.45%) in identifying apnea/hypopnea events (32,824 out of 40,800). Despite the non-negligible number of false positive detections, the proposed algorithm proves the dominance of the described sound pattern during apnea/hypopnea episodes.

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“…Linear prediction (LP) [13] and Mel frequency cepstral coefficients (MFCC) has been used for analysis and classification of normal and adventitious lung sounds [14,15,16,17,18]. Considering the non-stationarity in the lung sound signals, researchers have used multiresolution approaches of signal processing in [19,20,21]. The techniques based on wavelet transform have been employed to analyze and classify lung sound signals into the normal and adventitious category [22,23,24].…”

Section: Introductionmentioning

confidence: 99%

Automated classification of human lung sound signals using phase space representation of intrinsic mode function

Khan¹,

Palodiya²,

Poluboyina³

2021

Preprint

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Bronchiectasis and chronic obstructive pulmonary disease (COPD) are common human lung diseases. In general, the expert pulmonologistcarries preliminary screening and detection of these lung abnormalities by listening to the adventitious lung sounds. The present paper is an attempt towards the automatic detection of adventitious lung sounds ofBronchiectasis,COPD from normal lung sounds of healthy subjects. For classification of the lung sounds into a normaland adventitious category, we obtain features from phase space representation (PSR). At first, the empirical mode decomposition (EMD) is applied to lung sound signals to obtain intrinsic mode functions (IMFs). The IMFs are then further processed to construct two dimensional (2D) and three dimensional (3D) PSR. The feature space includes the 95% confidence ellipse area and interquartile range (IQR) of Euclidian distances computed from 2D and 3D PSRs, respectively. The process is carried out for the first four IMFs correspondings to normal and adventitious lung sound signals. The computed features depicta significant ability to discriminate the two categories of lung sound signals.To perform classification, we use the least square support vector machine with two kernels, namely, polynomial and radial basis function (RBF).Simulation outcomes on ICBHI 2017 lung sound dataset show the ability of the proposed method in effectively classifying normal and adventitious lung sound signals. LS-SVM is employing RBF kernel provides the highest classification accuracy of 97.67 % over feature space constituted by first, second, and fourth IMF.

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A Novel Method for Automatic Identification of Breathing State

Cited by 17 publications

References 30 publications

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

Breathing sound detector as a means to identify possible apneic periods from tracheal sound recordings

Automated classification of human lung sound signals using phase space representation of intrinsic mode function

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