Thematische Bibliographien / ECG-derived respiratory

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Auswahl der wissenschaftlichen Literatur zum Thema „ECG-derived respiratory“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Zeitschriftenartikel zum Thema "ECG-derived respiratory"

Kontaxis, Spyridon, Jesus Lazaro, Valentina D. A. Corino, et al. "ECG-Derived Respiratory Rate in Atrial Fibrillation." IEEE Transactions on Biomedical Engineering 67, no. 3 (2020): 905–14. http://dx.doi.org/10.1109/tbme.2019.2923587.

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Dickhaus, H., and C. Maier. "Central Sleep Apnea Detection from ECG-derived Respiratory Signals." Methods of Information in Medicine 49, no. 05 (2010): 462–66. http://dx.doi.org/10.3414/me09-02-0047.

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Summary Objectives: This study examines the suitability of recurrence plot analysis for the problem of central sleep apnea (CSA) detection and delineation from ECG-derived respiratory (EDR) signals. Methods: A parameter describing the average length of vertical line structures in recurrence plots is calculated at a time resolution of 1 s as ‘instantaneous trapping time’. Threshold comparison of this parameter is used to detect ongoing CSA. In data from 26 patients (duration 208 h) we assessed sensitivity for detection of CSA and mixed apnea (MSA) events by comparing the results obtained from 8-channel Holter ECGs to the annotations (860 CSA, 480 MSA) of simultaneously registered polysomnograms. Results: Multivariate combination of the EDR from different ECG leads improved the detection accuracy significantly. When all eight leads were considered, an average instantaneous vertical line length above 5 correctly identified 1126 of the 1340 events (sensitivity 84%) with a total number of 1881 positive detections. Conclusions: We conclude that recurrence plot analysis is a promising tool for detection and delineation of CSA epochs from EDR signals with high time resolution. Moreover, the approach is likewise applicable to directly measured respiratory signals.

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Bao, Xinqi, Aimé Kingwengwe Abdala, and Ernest Nlandu Kamavuako. "Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites." Sensors 21, no. 1 (2020): 78. http://dx.doi.org/10.3390/s21010078.

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The respiratory rate (RR) is a vital physiological parameter in prediagnosis and daily monitoring. It can be obtained indirectly from Electrocardiogram (ECG) signals using ECG-derived respiration (EDR) techniques. As part of the study in designing an early cardiac screening system, this work aimed to study whether the accuracy of ECG derived RR depends on the auscultation sites. Experiments were conducted on 12 healthy subjects to obtain simultaneous ECG (at auscultation sites and Lead I as reference) and respiration signals from a microphone close to the nostril. Four EDR algorithms were tested on the data to estimate RR in both the time and frequency domain. Results reveal that: (1) The location of the ECG electrodes between auscultation sites does not impact the estimation of RR, (2) baseline wander and amplitude modulation algorithms outperformed the frequency modulation and band-pass filter algorithms, (3) using frequency domain features to estimate RR can provide more accurate RR except when using the band-pass filter algorithm. These results pave the way for ECG-based RR estimation in miniaturised integrated cardiac screening device.

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Klum, Michael, Mike Urban, Timo Tigges, et al. "Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram." Sensors 20, no. 7 (2020): 2033. http://dx.doi.org/10.3390/s20072033.

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Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.

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Schrumpf, Fabian, Matthias Sturm, Gerold Bausch, and Mirco Fuchs. "Derivation of the respiratory rate from directly and indirectly measured respiratory signals using autocorrelation." Current Directions in Biomedical Engineering 2, no. 1 (2016): 241–45. http://dx.doi.org/10.1515/cdbme-2016-0054.

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AbstractThe estimation of respiratory rates from contineous respiratory signals is commonly done using either fourier transformation or the zero-crossing method. This paper introduces another method which is based on the autocorrelation function of the respiratory signal. The respiratory signals can be measured either directly using a flow sensor or chest strap or indirectly on the basis of the electrocardiogram (ECG). We compare our method against other established methods on the basis of real-world ECG signals and use a respiration-based breathing frequency as a reference. Our method achieved the best agreement between respiration rates derived from directly and indirectly measured respiratory signals.

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Schmidt, Marcus, Johannes W. Krug, Andy Schumann, Karl-Jürgen Bär, and Georg Rose. "Estimation of a respiratory signal from a single-lead ECG using the 4th order central moments." Current Directions in Biomedical Engineering 1, no. 1 (2015): 61–64. http://dx.doi.org/10.1515/cdbme-2015-0016.

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AbstractFor a variety of clinical applications like magnetic resonance imaging (MRI) the monitoring of vital signs is a common standard in clinical daily routine. Besides the electrocardiogram (ECG), the respiratory activity is an important vital parameter and might reveal pathological changes. Thoracic movement and the resulting impedance change between ECG electrodes enable the estimation of the respiratory signal from the ECG. This ECG-derived respiration (EDR) can be used to calculate the breathing rate without the need for additional devices or monitoring modules. In this paper a new method is presented to estimate the respiratory signal from a single-lead ECG. The 4th order central moments was used to estimate the EDR signal exploiting the change of the R-wave slopes induced by respiration. This method was compared with two approaches by analyzing the Fantasia database from www.physionet.org. Furthermore, the ECG signals of 24 healthy subjects placed in an 3 T MR-scanner were acquired.

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Stergiopoulos, Dimitrios C., Stylianos N. Kounalakis, Panagiotis G. Miliotis, and Nikolaos D. Geladas. "Second Ventilatory Threshold Assessed by Heart Rate Variability in a Multiple Shuttle Run Test." International Journal of Sports Medicine 42, no. 01 (2020): 48–55. http://dx.doi.org/10.1055/a-1214-6309.

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AbstractMany studies have focused on heart rate variability in association with ventilatory thresholds. The purpose of the current study was to consider the ECG-derived respiration and the high frequency product of heart rate variability as applicable methods to assess the second ventilatory threshold (VT2). Fifteen healthy young soccer players participated in the study. Respiratory gases and ECGs were collected during an incremental laboratory test and in a multistage shuttle run test until exhaustion. VΤ2 was individually calculated using the deflection point of ventilatory equivalents. In addition, VT2 was assessed both by the deflection point of ECG-derived respiration and high frequency product. Results showed no statistically significant differences between VT2, and the threshold as determined with high frequency product and ECG-derived respiration (F(2,28)=0.83, p=0.45, η2=0.05). A significant intraclass correlation was observed for ECG-derived respiration (r=0.94) and high frequency product (r=0.95) with VT2. Similarly, Bland Altman analysis showed a considerable agreement between VT2 vs. ECG-derived respiration (mean difference of −0.06 km·h−1, 95% CL: ±0.40) and VT2 vs. high frequency product (mean difference of 0.02 km·h−1, 95% CL: ±0.38). This study suggests that, high frequency product and ECG-derived respiration are indeed reliable heart rate variability indices determining VT2 in a field shuttle run test

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Sayadi, Omid, Eric H. Weiss, Faisal M. Merchant, Dheeraj Puppala, and Antonis A. Armoundas. "An optimized method for estimating the tidal volume from intracardiac or body surface electrocardiographic signals: implications for estimating minute ventilation." American Journal of Physiology-Heart and Circulatory Physiology 307, no. 3 (2014): H426—H436. http://dx.doi.org/10.1152/ajpheart.00038.2014.

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The ability to accurately monitor tidal volume (TV) from electrocardiographic (ECG) signals holds significant promise for improving diagnosis treatment across a variety of clinical settings. The objective of this study was to develop a novel method for estimating the TV from ECG signals. In 10 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the coronary sinus (CS), left ventricle (LV), and right ventricle (RV), as well as body surface electrograms, while TV was varied between 0 and 750 ml at respiratory rates of 7–14 breaths/min. We devised an algorithm to determine the optimized respirophasic modulation of the amplitude of the ECG-derived respiratory signal. Instantaneous measurement of respiratory modulation showed an absolute error of 72.55, 147.46, 85.68, 116.62, and 50.89 ml for body surface, CS, LV, RV, and RV-CS leads, respectively. Minute TV estimation demonstrated a more accurate estimation with an absolute error of 69.56, 153.39, 79.33, 122.16, and 48.41 ml for body surface, CS, LV, RV, and RV-CS leads, respectively. The RV-CS and body surface leads provided the most accurate estimations that were within 7 and 10% of the true TV, respectively. Finally, the absolute error of the bipolar RV-CS lead was significantly lower than any other lead configuration ( P < 0.0001). In conclusion, we have demonstrated that ECG-derived respiratory modulation provides an accurate estimation of the TV using intracardiac or body surface signals, without the need for additional hardware.

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Gilfriche, Pierre, Laurent M. Arsac, Yannick Daviaux, et al. "Highly sensitive index of cardiac autonomic control based on time-varying respiration derived from ECG." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 315, no. 3 (2018): R469—R478. http://dx.doi.org/10.1152/ajpregu.00057.2018.

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Frequency-domain indices of heart rate variability (HRV) have been used as markers of sympathovagal balance. However, they have been shown to be degraded by interindividual or task-dependent variability, and especially variations in breathing frequency. The study introduces a method to analyze respiration-(vagally) mediated HRV, to better assess subtle variations in sympathovagal balance using ECG recordings. The method enhances HRV analysis by focusing the quantification of respiratory sinus arrhythmia (RSA) gain on the respiratory frequency. To this end, instantaneous respiratory frequency was obtained with ECG-derived respiration (EDR) and was used for variable frequency complex demodulation (VFCDM) of R-R intervals to extract RSA. The ability to detect cognitive stress in 27 subjects (athletes and nonathletes) was taken as a quality criterion to compare our method to other HRV analyses: Root mean square of successive differences, Fourier transform, wavelet transform, and scaling exponent. Three computer-based tasks from MATB-II were used to induce cognitive stress. Sympathovagal index (HFnu) computed with our method better discriminates cognitive tasks from baseline, as indicated by P values and receiver operating characteristic curves. Here, transient decreases in respiratory frequency have shown to bias classical HRV indices, while only EDR-VFCDM consistently exhibits the expected decrease in the HFnu index with cognitive stress in both groups and all cognitive tasks. We conclude that EDR-VFCDM is robust against atypical respiratory profiles, which seems relevant to assess variations in mental demand. Given the variety of individual respiratory profiles reported especially in highly trained athletes and patients with chronic respiratory conditions, EDR-VFCDM could better perform in a wide range of applications.

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Schumann, Andy, Marcus Schmidt, Marco Herbsleb, et al. "Deriving respiration from high resolution 12-channel-ECG during cycling exercise." Current Directions in Biomedical Engineering 2, no. 1 (2016): 171–74. http://dx.doi.org/10.1515/cdbme-2016-0039.

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AbstractMonitoring of cardiac and respiratory activity, is essential in several clinical interventions like bicycle ergometries. The respiration signal can be derived from the ECG if it is not recorded itself (ECG derived respiration, EDR). In this study, we tried to reconstruct breathing rates (BR) from stress test high resolution 12-channel-ECGs in nine healthy subjects using higher order central moments. A mean absolute error per subjects of 2.9/min and relatively high correlation (rp = 0.85) and concordance coefficient (rc = 0.79) indicated a quite accurate reproduction of respiratory activity. The analysis of the different test stages revealed an increase of BR errors while subjects were effortful cycling compared to rest. During incremental cycling exercise test the mean absolute error per subjects was 3.4/min. Compared to the results reported in other studies at rest in supine position, this seems adequately accurate. In conclusion, our results indicate that EDR using higher order central moments is suited for monitoring BR during physical activity.

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Dissertationen zum Thema "ECG-derived respiratory"

Janáková, Jaroslava. "Odhad dechové frekvence z elektrokardiogramu a fotopletysmogramu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442594.

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The master thesis deals with the issue of gaining the respiratory rate from ECG and PPG signals, which are not only in clinical practice widely used measurable signals. The theoretical part of the work outlines the issue of obtaining a breath curve from these signals. The practical part of the work is focused on the implementation of five selected methods and their final evaluation and comparison.

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Tiinanen, S. (Suvi). "Methods for assessment of autonomic nervous system activity from cardiorespiratory signals." Doctoral thesis, Oulun yliopisto, 2019. http://urn.fi/urn:isbn:9789526223131.

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Abstract A cardiorespiratory system is highly regulated via the autonomic nervous system (ANS), whose function can be quantified noninvasively by analyzing electrocardiogram (ECG), blood pressure (BP) and respiration signals. Several conditions and illnesses are linked with imbalance of the ANS. This thesis aimed to develop methods for describing the ANS regulation of a cardiovascular system from short-term cardiorespiratory measurements. More specifically, the role of breathing rate and its effects on traditional frequency domain based cardiovascular indexes describing ANS control is addressed. The main contributions are as follows: 1) an adaptive filtering based method to remove respiratory influences from cardiovascular signals and indexes was developed. The adaptive filter reduced the bias caused by low respiration rate, enabling the usage of spontaneous respiration measurement protocol over controlled respiration. 2) Methods to quantify respiratory sinus arrhythmia (RSA) index from cardiovascular signals were developed as well: two methods utilizes adaptive filtering and either the measured respiration signal or the ECG-derived respiration signal and one method uses independent component analysis. Developed RSA index methods allow varying respiration rates making them physiologically more accurate than traditional high frequency power with fixed respiration rate, often used as RSA index. 3) Tools for studying the power and the frequency of low frequency (LF) oscillations of cardiovascular signals were developed, including a time-frequency representation for analyzing varying data. An experimental study was conducted with patients of continuum of cardiovascular risks. According to results, aging decreased the frequency of LF oscillation, whereas coronary artery disease decreased it further. 4) Two new ECG-derived respiration (EDR) methods utilizing decomposition techniques were developed. The proposed methods yielded statistically significant improvements over previously developed EDR methods. EDR method enables to get respiratory information from ECG, which in its turn reduces needed modalities in ANS quantification. This thesis provides methods to quantify indexes describing the ANS function more accurately by acknowledging the respiration effects. The results of this thesis may be utilized in various application areas, ranging from clinical to physiology research up to commercial health, wellness and sport products Tiivistelmä Autonominen hermosto säätelee tarkasti sydän- ja verenkiertoelimistöä sekä hengitystä. Autonomisen hermoston toimintaa voidaan analysoida laskennallisin menetelmin noninvasiivisesti mitatuista elektrokardiogrammi- (EKG, sydänsähkökäyrä), verenpaine- ja hengityssignaaleista. Useita tekijöitä ja sairauksia voidaan yhdistää autonomisen hermoston epätasapainoon. Väitöskirjassa kehitettiin menetelmiä sydän- ja verisuonijärjestelmän autonomisen säätelyn kuvaamiseksi lyhytaikaisista kardiorespiratorisista tallenteista. Erityistä huomiota on kiinnitetty hengityksen vaikutukseen perinteisiin taajuustasosta laskettaviin muuttujiin, jotka kuvaavat autonomisen hermoston toimintaa. Väitöskirjan päätuloksia ja -tuotoksia ovat: 1) uusi adaptiiviseen suodatukseen pohjautuva laskennallinen menetelmä hengitysvaikutuksien poistamiseksi sydän- ja verisuonisignaaleista. Adaptiivinen suodatin vähensi matalan hengitystaajuuden aiheuttamaa vääristymää hermoston toimintaa kuvaavista parametreistä. Uusi menetelmä mahdollistaa kontrolloimattoman eli vapaan hengitystaajuus-protokollan käytön autonomisen hermoston toiminnan mittauksissa. 2) Uusia menetelmiä respiratorisen sinus arrytmian (RSA) määrittämiseksi sydän- ja verisuonisignaaleista. Kehitetyissä menetelmistä kahdessa käytetään adaptiivista suodatusta hyödyntäen joko mitattua hengityssignaalia tai EKG:stä johdettua hengityssignaalia. Kolmas menetelmä pohjautuu itsenäisten komponenttien analyysiin. Kehitetyt menetelmät RSA:n laskemiseksi sallivat hengitystaajuuden vaihtelun mittauksien aikana, mikä tekee ne fysiologisesti tarkemmaksi kuin perinteisesti käytetty korkeataajuinen (HF) komponentti, joka lasketaan taajuustasossa tietyltä kaistalta riippumatta hengitystaajuudesta. 3) Kehitettiin ja sovellettiin menetelmiä EKG:n ja verenpaineen matalataajuisten (LF) heilahtelujen tutkimista varten. Yhdessä tutkimuksessa sovellettiin aika-taajuustason esitystapaa vaihtelevan datan analysoimiksi. Kokeellinen tutkimus tehtiin aineistolla, joka oli jatkumo sydän- ja verisuonitautien riskejä omaavista potilaista jo sairastuneisiin potilaisiin. Ikääntyminen pienensi matalataajuisen heilahtelun taajuutta ja sepelvaltimosairaus pienensi sitä edelleen. 4) Kaksi uutta hajotelmatekniikoita hyödyntävää menetelmää, joilla lasketaan EKG:stä hengitysvirtausignaali-estimaatti (EDR). Kehitettyjen EDR-menetelmien suorituskyky osoittautui tilastollisesti paremmaksi kuin aikaisemmat menetelmät. Koska hengityssignaali ja -taajuus voidaan johtaa suoraan EKG:stä, tarvittavien mittaussensoreiden määrää vähenee. Lisäksi EDR:ää voidaan hyödyttää autonomisen hermoston toimintaa kuvaavien parametrien estimoinnissa. Väitöskirja tarjoaa menetelmiä autonomisen hermoston toiminnan mittaamiseksi huomioiden erityisesti hengityksen vaikutus estimoitaviin parametreihin. Väitöskirjan tuloksia voidaan soveltaa useissa kardiorespiratorisia signaaleja hyödyntävissä sovelluksissa aina kliinisestä työstä fysiologian tutkimukseen ja kaupallisiin hyvinvointi-, terveys- ja urheilusovelluksiin Huomautus/Notice Painetussa virheellinen ISBN: 978-952-62-2312-4, oikea 978-952-62-2310-0. Printed version has incorrect ISBN: 978-952-62-2312-4, it should be 978-952-62-2310-0

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江政運. "Abnormal Sleep Breath Detection Based on the ECG Derived Respiratory Signal." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/07305267684692481290.

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碩士 逢甲大學 自動控制工程學系 102 Abstract The main purpose of this research is to develop a real-time abnormal breathing anomaly system, integrated with the physiological measurements smart clothing for monitoring abnormal breathing during sleep. In order to reduce the abnormal phenomena, physiological measurements will instead be done by smart-shirt measurements, and uses electrocardiograph derived respiratory signal from previous studies in order to achieve the same functionality with breathing bands. This system will be developed using LabVIEW® for analysis, while the signals will be measured and acquired using physiological cardiac signals smart-shirt with a sampling frequency 250 Hz, and linked by Bluetooth for real-time analysis and detection. In order to prove the accuracy of the system for the analysis of abnormal breathing detection, short-term controlled breathing data will be used, such as inhaling 2 seconds (s) exhaling 2s, inhaling 3s exhaling 2s, long breath holding, short breath holding, long weak breathing, short weak breathing, and other specific breathing pattern sampling, for instantaneous detection and analysis in order to achieve the accuracy measurement. After testing, the system has an accuracy of 90% when detecting normal breathing state and 90% when detecting abnormal breathing state. The results confirm the breathing state determining system has a good accuracy, and if more than ten seconds of abnormal breathing is detected, the system will alert caregivers. In the future, the data can also be combined with a respirator to achieve an ergonomic and efficient respirator. Keywords: ECG-derived respiratory signal, ECG, abnormal breathing, smart clothing.

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Huang, Po-Yu, and 黃柏諭. "Parallelized Empirical Mode Decomposition in CUDA and Its Application to ECG-Derived Respiratory." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/wz8x65.

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碩士 國立交通大學 電機工程學系 103 ECG-Derived Respiratory (EDR) is a technique to derive respiratory from electrocardiography (ECG), which can help to overcome limitation of traditional respiratory acquisition method. Empirical Mode Decomposition (EMD) is process of adaptive analysis applicable to non-linear and non-stationary data such as ECG, hence it can be used to deal with EDR application. EMD analyzes data by iteratively decomposing data into multiple Intrinsic Mode Functions (IMFs). Traditionally, EMD is computed on all data points in a serial manner, thus making its execution time grows linearly with the data size. In this work, a parallelized EMD algorithm working on a General-Purpose computing on Graphics Processing Units (GPGPU) in CUDA language is proposed to improve performance over traditional EMD. Moreover, additional merging cubic spline interpolation and GPU acceleration techniques are also incorporated for achieving high parallelism and high accuracy. Statistical result of database shows that our parallelized EMD in CUDA achieves 6.6X speedup with 0.0003% error after 50 times iteration on datasets of 1-million points. For EDR application, our parallelized EMD achieves average 69.75% accuracy with average execution time of 7.91 second for 1-minute windows ECG from Fantasia Database.

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Bücher zum Thema "ECG-derived respiratory"

Johnson, Nicholas J., and Judd E. Hollander. Management of cocaine poisoning. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0324.

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Cocaine is powerful central nervous system (CNS) stimulant derived from the coca plant. It affects the body via a number of mechanisms including blockade of fast sodium channels, increased catecholamine release, inhibition of catecholamine reuptake, and increased concentration of excitatory amino acid concentrations in the CNS. It is rapidly absorbed via the aerodigestive, respiratory, gastrointestinal, and genitourinary mucosa, and also may be injected. When injected intravenously or inhaled, cocaine is rapidly distributed throughout the body and CNS, with peak effects in 3–5 minutes. With nasal insufflation, absorption peaks in 20 minutes. Its half-life is approximately 1 hour. Common clinical manifestations include agitation, euphoria, tachycardia, hyperthermia, and hypertension. Chest pain is a common presenting complaint among cocaine users; 6% of these patients will have myocardial infarction. Other life-threatening sequelae include stroke, intracranial haemorrhage, seizures, dysrhythmias, and rhabdomyolysis. Clinical signs and symptoms, as well as severity of intoxication, should dictate the diagnostic evaluation and treatment of cocaine intoxicated patients. If the patient has chest pain, an ECG, chest radiograph, and measurement of cardiac biomarkers should be performed. A brief observation period may be useful in these patients. Many manifestations of cocaine intoxication, including agitation, hypertension, and chest pain, are effectively treated with benzodiazepines. Beta-blockers should be avoided in patients with suspected cocaine intoxication. Special attention should be paid to pregnant patients and those who present after ingesting packets filled with cocaine, as they may exhibit severe toxicity if these packets rupture.

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Buchteile zum Thema "ECG-derived respiratory"

Sahin, Mesut, Howard Fidel, and Raquel Perez-Castillejos. "Extraction of Respiratory Rate from ECG (ECG-Derived Respiration-EDR)." In Instrumentation Handbook for Biomedical Engineers. CRC Press, 2020. http://dx.doi.org/10.1201/9780429193989-11.

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Konferenzberichte zum Thema "ECG-derived respiratory"

Rajagopalan, Pradeep, and Sabarish Ramachandran. "ECG derived respiratory rate estimation for wearable devices." In 2017 International Conference on Computational Intelligence in Data Science (ICCIDS). IEEE, 2017. http://dx.doi.org/10.1109/iccids.2017.8272664.

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Campolo, M., D. Labate, F. La Foresta, F. C. Morabito, A. Lay-Ekuakille, and P. Vergallo. "ECG-derived respiratory signal using Empirical Mode Decomposition." In 2011 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2011. http://dx.doi.org/10.1109/memea.2011.5966727.

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Birrenkott, Drew A., Marco A. F. Pimentel, Peter J. Watkinson, and David A. Clifton. "Robust estimation of respiratory rate via ECG- and PPG-derived respiratory quality indices." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7590792.

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Konik, Arda, Joyeeta Mitra Mukherjee, Karen L. Johnson, Eric Helfenbein, Lingxiong Shao, and Michael A. King. "Comparison of ECG derived respiratory signals and pneumatic bellows for respiratory motion tracking." In 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference (2011 NSS/MIC). IEEE, 2011. http://dx.doi.org/10.1109/nssmic.2011.6153746.

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Avci, Cafer, Ibrahim Delibasoglu, and Ahmet Akbas. "Sleep apnea detection using wavelet analysis of ECG derived respiratory signal." In 2012 International Conference on Biomedical Engineering (ICoBE). IEEE, 2012. http://dx.doi.org/10.1109/icobe.2012.6179019.

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Sadr, Nadi, and Philip de Chazal. "Comparing ECG Derived Respiratory Signals and Chest Respiratory Signal for the Detection of Obstructive Sleep Apnoea." In 2016 Computing in Cardiology Conference. Computing in Cardiology, 2016. http://dx.doi.org/10.22489/cinc.2016.296-336.

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Correa, L. S., E. Laciar, V. Mut, A. Torres, and R. Jane. "Sleep apnea detection based on spectral analysis of three ECG - derived respiratory signals." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5334196.

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