by Keyword: Time-frequency analysis
Lozano-García, M., Davidson, C. M., Jané, R., (2019). Analysis of tracheal and pulmonary continuous adventitious respiratory sounds in asthma Engineering in Medicine and Biology Society (EMBC) 41st Annual International Conference of the IEEE , IEEE (Berlín, Germany) , 4930-4933
Continuous adventitious sounds (CAS) are commonly observed in obstructive pulmonary diseases and are of great clinical interest. However, their evaluation is generally subjective. We have previously developed an automatic CAS segmentation and classification algorithm for CAS recorded on the chest surface. The aim of this study is to establish whether these pulmonary CAS can be identified in a similar way using a tracheal microphone. Respiratory sounds were originally recorded from 25 participants using five contact microphones, four on the chest and one on the trachea, during three progressive respiratory maneuvers. In this work CAS component detection was performed on the tracheal channel using our automatic algorithm based on the Hilbert spectrum. The tracheal CAS detected were then compared to the previously analyzed pulmonary CAS. The sensitivity of CAS identification was lower at the tracheal microphone, with CAS that appeared simultaneously in all four pulmonary recordings more likely to be identified in the tracheal recordings. These observations could be due to the CAS being obscured by the lower SNR present in the tracheal recordings or not being transmitted through the airways to the trachea. Further work to optimize the algorithm for the tracheal recordings will be conducted in the future.
JTD Keywords: Microphones, Lung, Diseases, Time-frequency analysis, Spectrogram, Sensitivity
Castillo, Y., Blanco, D., Whitney, J., Mersky, B., Jané, R., (2017). Characterization of a tooth microphone coupled to an oral appliance device: A new system for monitoring OSA patients Engineering in Medicine and Biology Society (EMBC) 39th Annual International Conference of the IEEE , IEEE (Seogwipo, South Korea) , 1543-1546
Obstructive sleep apnea (OSA) is a highly prevalent chronic disease, especially in elderly and obese populations. Despite constituting a serious health, social and economic problem, most patients remain undiagnosed and untreated due to limitations in current equipment. In this work, we propose a novel method to diagnose OSA and monitor therapy adherence and effectiveness at home in a non-invasive and inexpensive way: combining acoustic analysis of breathing and snoring sounds with oral appliance therapy (OA). Audiodontics has introduced a new sensor, a tooth microphone coupled to an OA device, which is the main pillar of this system. The objective of this work is to characterize the response of this sensor, comparing it with a commercial tracheal microphone (Biopac transducer). Signals containing OSA-related sounds were acquired simultaneously with the two microphones for that purpose. They were processed and analyzed in time, frequency and time-frequency domains, in a custom MATLAB interface. We carried out a single-event approach focused on breaths, snores and apnea episodes. We found that the quality of the signals obtained by both microphones was quite similar, although the tooth microphone spectrum concentrated more energy at the high-frequency band. This opens a new field of study about high-frequency components of snores and breathing sounds. These characteristics, together with its intraoral position, wireless option and combination with customizable OAs, give the tooth microphone a great potential to reduce the impact of sleep disorders, by enabling prompt detection and continuous monitoring of patients at home.
JTD Keywords: Microphones, Teeth, Sleep apnea, Time-frequency analysis, Signal to noise ratio, Monitoring, Acoustics
Estrada, L., Torres, A., Garcia-Casado, J., Sarlabous, L., Prats-Boluda, G., Jané, R., (2016). Time-frequency representations of the sternocleidomastoid muscle electromyographic signal recorded with concentric ring electrodes Engineering in Medicine and Biology Society (EMBC) 38th Annual International Conference of the IEEE , IEEE (Orlando, USA) , 3785-3788
The use of non-invasive methods for the study of respiratory muscle signals can provide clinical information for the evaluation of the respiratory muscle function. The aim of this study was to evaluate time-frequency characteristics of the electrical activity of the sternocleidomastoid muscle recorded superficially by means of concentric ring electrodes (CREs) in a bipolar configuration. The CREs enhance the spatial resolution, attenuate interferences, as the cardiac activity, and also simplify the orientation problem associated to the electrode location. Five healthy subjects underwent a respiratory load test in which an inspiratory load was imposed during the inspiratory phase. During the test, the electromyographic signal of the sternocleidomastoid muscle (EMGsc) and the inspiratory mouth pressure (Pmouth) were acquired. Time-frequency characteristics of the EMGsc signal were analyzed by means of eight time-frequency representations (TFRs): the spectrogram (SPEC), the Morlet scalogram (SCAL), the Wigner-Ville distribution (WVD), the Choi-Williams distribution (CHWD), two generalized exponential distributions (GED1 and GED2), the Born-Jordan distribution (BJD) and the Cone-Kernel distribution (CKD). The instantaneous central frequency of the EMGsc showed an increasing behavior during the inspiratory cycle and with the increase of the inspiratory load. The bilinear TFRs (WVD, CHWD, GEDs and BJD) were less sensitive to cardiac activity interference than classical TFRs (SPEC and SCAL). The GED2 was the TFR that shown the best results for the characterization of the instantaneous central frequency of the EMGsc.
JTD Keywords: Electrodes, Interference, Kernel, Mouth, Muscles, Spectrogram, Time-frequency analysis
Arcentales, A., Voss, A., Caminal, P., Bayes-Genis, A., Domingo, M. T., Giraldo, B. F., (2013). Characterization of patients with different ventricular ejection fractions using blood pressure signal analysis CinC 2013 Computing in Cardiology Conference (CinC) , IEEE (Zaragoza, Spain) , 795-798
Ischemic and dilated cardiomyopathy are associated with disorders of myocardium. Using the blood pressure (BP) signal and the values of the ventricular ejection fraction, we obtained parameters for stratifying cardiomyopathy patients as low- and high-risk. We studied 48 cardiomyopathy patients characterized by NYHA ≥2: 19 patients with dilated cardiomyopathy (DCM) and 29 patients with ischemic cardiomyopathy (ICM). The left ventricular ejection fraction (LVEF) percentage was used to classify patients in low risk (LR: LVEF > 35%, 17 patients) and high risk (HR: LVEF ≤ 35%, 31 patients) groups. From the BP signal, we extracted the upward systolic slope (BPsl), the difference between systolic and diastolic BP (BPA), and systolic time intervals (STI). When we compared the LR and HR groups in the time domain analysis, the best parameters were standard deviation (SD) of 1=STI, kurtosis (K) of BPsl, and K of BPA. In the frequency domain analysis, very low frequency (VLF) and high frequency (HF) bands showed statistically significant differences in comaprisons of LR and HR groups. The area under the curve of power spectral density was the best parameter in all classifications, and particularly in the very-low-and high- frequency bands (p <; 0.001). These parameters could help to improve the risk stratification of cardiomyopathy patients.
JTD Keywords: blood pressure measurement, cardiovascular system, diseases, medical disorders, medical signal processing, statistical analysis, time-domain analysis, BP signal, HR groups, LR groups, blood pressure signal analysis, cardiomyopathy patients, diastolic BP, dilated cardiomyopathy, frequency domain analysis, high-frequency bands, ischemic cardiomyopathy, left ventricular ejection fraction, low-frequency bands, myocardium disorders, patient characterization, power spectral density curve, standard deviation, statistical significant differences, systolic BP, systolic slope, systolic time intervals, time domain analysis, ventricular ejection fraction, Abstracts, Databases, Parameter extraction, Telecommunication standards, Time-frequency analysis
Giraldo, B. F., Tellez, J. P., Herrera, S., Benito, S., (2013). Study of the oscillatory breathing pattern in elderly patients Engineering in Medicine and Biology Society (EMBC) 35th Annual International Conference of the IEEE , IEEE (Osaka, Japan) , 5228-5231
Some of the most common clinical problems in elderly patients are related to diseases of the cardiac and respiratory systems. Elderly patients often have altered breathing patterns, such as periodic breathing (PB) and Cheyne-Stokes respiration (CSR), which may coincide with chronic heart failure. In this study, we used the envelope of the respiratory flow signal to characterize respiratory patterns in elderly patients. To study different breathing patterns in the same patient, the signals were segmented into windows of 5 min. In oscillatory breathing patterns, frequency and time-frequency parameters that characterize the discriminant band were evaluated to identify periodic and non-periodic breathing (PB and nPB). In order to evaluate the accuracy of this characterization, we used a feature selection process, followed by linear discriminant analysis. 22 elderly patients (7 patients with PB and 15 with nPB pattern) were studied. The following classification problems were analyzed: patients with either PB (with and without apnea) or nPB patterns, and patients with CSR versus PB, CSR versus nPB and PB versus nPB patterns. The results showed 81.8% accuracy in the comparisons of nPB and PB patients, using the power of the modulation peak. For the segmented signal, the power of the modulation peak, the frequency variability and the interquartile ranges provided the best results with 84.8% accuracy, for classifying nPB and PB patients.
JTD Keywords: cardiovascular system, diseases, feature extraction, geriatrics, medical signal processing, oscillations, pneumodynamics, signal classification, time-frequency analysis, Cheyne-Stokes respiration, apnea, cardiac systems, chronic heart failure, classification problems, discriminant band, diseases, elderly patients, feature selection process, frequency variability, interquartile ranges, linear discriminant analysis, nonperiodic breathing, oscillatory breathing pattern, periodic breathing, respiratory How signal, respiratory systems, signal segmentation, time 5 min, time-frequency parameters, Accuracy, Aging, Frequency modulation, Heart, Senior citizens, Time-frequency analysis