scholarly journals Dual-Lead 55 mm Impedance Pneumography

2020 ◽  
Vol 6 (3) ◽  
pp. 205-208
Author(s):  
Michael Klum ◽  
Mike Urban ◽  
Alexandru-Gabriel Pielmus ◽  
Reinhold Orglmeister
Keyword(s):  
Subject Position ◽  
Respiratory Monitoring ◽  
Electrode Spacing ◽  
Estimation Errors ◽  
Impedance Pneumography ◽  
Respiratory Parameters ◽  
High Prevalence ◽  
Respiratory Signal ◽  
Dual Lead ◽  
Relative Errors

AbstractIn recent years, respiratory monitoring has gained attention due to the high prevalence and severe consequences of sleep apnea, post-anesthesia respiratory instability and respiratory diseases. Nevertheless, respiratory monitoring oftentimes relies on obtrusive masks and belts, which are unsuitable for wearable, long-term monitoring. Impedance pneumography (IP) is a bioimpedance method aiming to assess respiratory parameters unobtrusively. However, most IP configurations require far-spaced electrodes. Based on our recent work on wearable IP, we propose a dual-lead, wearable IP setup with 55 mm electrode spacing to estimate respiratory flow and rate (RR). Using our recently presented multimodal patch stethoscope as well as commercial systems, we conducted a study including 10 healthy subjects which were recorded in the supine, lateral and prone position. Using time-delay neural networks, we achieved RR estimation errors below 0.6 breaths per minute and flow correlations of 0.88 with relative errors of 25 % to a pneumotachometer reference. We conclude that dual-lead IP increases the performance of respiratory signal estimation compared to a single lead and recommend research in the area of subject position dependency and movement artefacts.

scholarly journals 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 ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2033 ◽  
Author(s):  
Michael Klum ◽  
Mike Urban ◽  
Timo Tigges ◽  
Alexandru-Gabriel Pielmus ◽  
Aarne Feldheiser ◽  
...  
Keyword(s):  
Cause Of Death ◽  
Left Ventricular ◽  
Noncommunicable Diseases ◽  
Left Ventricular Ejection ◽  
Ejection Time ◽  
Left Ventricular Ejection Time ◽  
Respiratory Parameters ◽  
Respiratory Signal ◽  
Ventricular Ejection Time ◽  
The Impact

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.

scholarly journals Evaluating the role of data quality when sharing information in hierarchical multistock assessment models, with an application to Dover sole

2019 ◽  
Vol 76 (10) ◽  
pp. 1819-1835
Author(s):  
Samuel D.N. Johnson ◽  
Sean P. Cox
Keyword(s):  
Statistical Power ◽  
Fishery Management ◽  
Stock Assessment ◽  
Simulated Data ◽  
Assessment Model ◽  
Production Model ◽  
Estimation Errors ◽  
Lower Estimation ◽  
Relative Errors ◽  
Dover Sole

An emerging approach to data-limited fisheries stock assessment uses hierarchical multistock assessment models to group stocks together, sharing information from data-rich to data-poor stocks. In this paper, we simulate data-rich and data-poor fishery and survey data scenarios for a complex of Dover sole (Microstomus pacificus) stocks. Simulated data for individual stocks were used to compare estimation performance for single-stock and hierarchical multistock versions of a Schaefer production model. The single-stock and best-performing multistock models were then used in stock assessments for the real Dover sole data. Multistock models often had lower estimation errors than single-stock models when assessment data had low statistical power. Relative errors for productivity and relative biomass parameters were lower for multistock assessment model configurations. In addition, multistock models that estimated hierarchical priors for survey catchability performed the best under data-poor scenarios. We conclude that hierarchical multistock assessment models are useful for data-limited stocks and could provide a more flexible alternative to data pooling and catch-only methods; however, these models are subject to nonlinear side effects of parameter shrinkage. Therefore, we recommend testing hierarchical multistock models in closed-loop simulations before application to real fishery management systems.

scholarly journals Wearable Impedance Pneumography

2020 ◽  
Vol 6 (3) ◽  
pp. 233-236
Author(s):  
Michael Klum ◽  
Mike Urban ◽  
Alexandru-Gabriel Pielmus ◽  
Reinhold Orglmeister
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Healthy Subjects ◽  
Respiratory Diseases ◽  
High Accuracy ◽  
Flow Estimation ◽  
Respiratory Flow ◽  
Impedance Pneumography ◽  
Respiratory Parameters ◽  
Relative Flow

AbstractRespiratory diseases are a leading cause of death worldwide. The prevalence of sleep apnea, its cardiovascular consequences, postoperative respiratory instability and severe respiratory syndromes further highlight the importance of respiratory monitoring. Typical methods, however, rely on obtrusive nasal cannulas and belts. Impedance pneumography (IP) is a promising bioimpedance application which aims to estimate respiratory parameters from the thorax impedance. Currently, IP configurations require large inter-electrode distances, diminishing its applicability in a wearable context. We propose an IP configuration with 55 mm spacing using our recently presented sensor patch. In a study including 10 healthy subjects, respiratory rate (RR) and flow are estimated in the supine, lateral and prone position. Using time-delay neural network regression, RR errors below 1 bpm, flow correlations of 0.81 and relative flow errors of 38 % with respect to a pneumotachometer reference were achieved. We conclude that high accuracy RR estimation is possible in a 55 mm IP configuration. Respiratory flow can be roughly estimated. Further research combining several biosignals for a more robust, wearable flow estimation is recommended.

Denoising of the Respiratory Signal of Electrical Bio-Impedance

2013 ◽  
Vol 718-720 ◽  
pp. 1024-1028
Author(s):  
Ning Song ◽  
Lian Ying Ji ◽  
Yong Peng Xu
Keyword(s):  
Signal Processing ◽  
Electrical Impedance ◽  
Mechanical Vibration ◽  
Wiener Filter ◽  
Processing Method ◽  
Motion Artifacts ◽  
Signal Processing Method ◽  
Impedance Pneumography ◽  
Respiratory Signal ◽  
Correlated Motion

Human respiratory signal provides important information in modern medical care. In daily life, respiratory signal is usually captured under different motion states with the help of Electrical impedance pneumography (EIP). Consequently, the captured signal is easily corrupted by electronic/electromagnetic noise, internal mechanical vibration of the lung and motion artifacts. Because respiratory signal and interferences co-exist in an overlapping spectra manner, classical filtering method cannot work here. In this paper, we present a new signal processing method for eliminating the noise and interferences included in EIP signal, by separating the correlated motion artifacts from the raw EIP and 3-axis Acceleration (ACC) signals, restoring the rough respiration signal from the mixed signal, and further processing using wavelet analysis approach. Results are compared to traditional denosing algorithms by wiener filter, which indicates that the new signal processing method we presented is suitable for EIP signals under the motion states.

scholarly journals Color Spectrographic Analysis of Respiratory Sounds: A Promising Technology for Respiratory Monitoring

2020 ◽  
Vol 14 (1) ◽  
pp. 22-29
Author(s):  
Daniel J. Doyle
Keyword(s):  
Respiratory Monitoring ◽  
Respiratory Sounds ◽  
Spectrographic Analysis ◽  
Knowledge Domains ◽  
Future Technology ◽  
Electret Microphone ◽  
Frequent Use ◽  
High Prevalence ◽  
Perioperative Pain Management ◽  
Computer Based

Background: The need for reliable respiratory monitoring has increased in recent years with the frequent use of opioids for perioperative pain management as well as a high prevalence of patients suffering from respiratory comorbidities. Objective: Motivated by the success of acoustical color spectrographic techniques in other knowledge domains, we sought to build proof-of-concept systems for the computer-based color spectrographic analysis of respiratory sounds, recorded from various sites. Methods: We used a USB miniature electret microphone and a Windows-based color spectrographic analysis package to obtain color spectrograms for breath sound recordings from the neck, from an oxygen mask, from the ear canal, and from a leak-free microphone pneumatically connected to the cuff of a laryngeal mask airway. Results: Potentially useful color spectrographic displays were obtained from all four recording sites, although the spectrograms obtained varied in their characteristics. It was also found that obtaining high-quality color spectrograms requires attention to a number of technical details. Conclusion: Color spectrographic analysis of respiratory sounds is a promising future technology for respiratory monitoring.

Improved Binning of Operating Modes in EPA Simulator Program for Transit Buses: Case Study in Beijing

2017 ◽  
Vol 2627 (1) ◽  
pp. 57-66 ◽  
Author(s):  
Chenxu Li ◽  
Lei Yu ◽  
Guohua Song
Keyword(s):  
Motor Vehicle ◽  
Operating Mode ◽  
Critical Parameters ◽  
Hydrocarbon Emissions ◽  
Estimation Errors ◽  
Operating Modes ◽  
Transit Buses ◽  
Relative Errors ◽  
Two Parameters ◽  
Simulator Program

The Motor Vehicle Emissions Simulator (MOVES) quantifies emissions as a function of the operating mode (opmode) and emissions rates. The opmode, the determinant parameter in estimating emissions, is defined by two critical parameters: speed and scaled tractive power (STP). Activity characteristics of transit buses are commonly recognized as being quite different from those of other vehicles, and this study found the values of the two parameters for transit buses to be much smaller than those for other vehicles. However, the MOVES program uses an identical opmode binning method for transit buses and other vehicles, a method that likely leads to errors in emissions estimations for transit buses. This paper developed a binning method based on massive field data collected in Beijing to improve the opmode binning for transit buses. To this end, STP fractions, vehicle kilometers traveled (VKT) fractions, and emissions contributions were first investigated. The STP values were grouped into nine bins on the basis of analysis of emissions rates and emissions contributions. Three speed bins were then determined with the hierarchical clustering method and the averaging of VKT fractions. As a result, 29 opmode bins were defined for transit buses. Finally, the proposed method was applied to real-world emissions data in Beijing. The results indicated that the proposed binning method could reduce errors in emissions estimation errors. On average, the relative errors in estimating carbon dioxide, carbon monoxide, nitrogen oxide, and hydrocarbon emissions by the proposed method were 2.0%, 5.9%, 1.6%, and 1.5% lower, respectively, than errors made by the MOVES method.

Bioelectronic Measurement of Nasality in Trainable Mentally Retarded Children

1977 ◽  
Vol 42 (3) ◽  
pp. 436-439 ◽  
Author(s):  
David A. Daly
Keyword(s):  
Mentally Retarded ◽  
High Prevalence ◽  
Mentally Retarded Children ◽  
Trainable Mentally Retarded

Fifty trainable mentally retarded children were evaluated with TONAR II, a bioelectronic instrument for detecting and quantitatively measuring voice parameters. Results indicated that one-half of the children tested were hypernasal. The strikingly high prevalence of excessive nasality was contrasted with results obtained from 64 nonretarded children and 50 educable retarded children tested with the same instrument. The study demonstrated the need of retarded persons for improved voice and resonance.

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