The calculation of electrical impedance tomography based silent spaces requires individual thorax and lung contours
Objective. The present study evaluates the influence of different thorax contours (generic versus individual) on the parameter ‘silent spaces’ computed from electrical impedance tomography (EIT) measurements. Approach. Six patients with acute respiratory distress syndrome were analyzed retrospectively. EIT measurements were performed and the silent spaces were calculated based on (1) patient-specific contours Sind, (2) generic adult male contours SEidorsA and (3) generic neonate contours SEidorsN. Main results. The differences among all studied subjects were 5 ± 6% and 8 ± 7% for Sind versus SEidorsA, Sind versus...
Source: Physiological Measurement - September 8, 2022 Category: Physiology Authors: Lin Yang, Feng Fu, In éz Frerichs, Knut Möller, Meng Dai and Zhanqi Zhao Source Type: research
Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation
We describe an automatic classifier of arrhythmias based on 12-lead and reduced-lead electrocardiograms. Our classifier comprises four modules: scattering transform (ST), phase harmonic correlation (PHC), depthwise separable convolutions (DSC), and a long short-term memory (LSTM) network. It is trained on PhysioNet/Computing in Cardiology Challenge 2021 data. The ST captures short-term temporal ECG modulations while the PHC characterizes the phase dependence of coherent ECG components. Both reduce the sampling rate to a few samples per typical heart beat. We pass the output of the ST and PHC to a depthwise-separable convol...
Source: Physiological Measurement - September 8, 2022 Category: Physiology Authors: Philip A Warrick, Vincent Lostanlen, Michael Eickenberg, Masun Nabhan Homsi, Adri án Campoy Rodríguez and Joakim Andén Source Type: research
Label noise and self-learning label correction in cardiac abnormalities classification
Objective. Learning to classify cardiac abnormalities requires large and high-quality labeled datasets, which is a challenge in medical applications. Small datasets from various sources are often aggregated to meet this requirement, resulting in a final dataset prone to label noise due to inter- and intra-observer variability and different expertise. It is well known that label noise can affect the performance and generalizability of the trained models. In this work, we explore the impact of label noise and self-learning label correction on the classification of cardiac abnormalities on large heterogeneous datasets of elec...
Source: Physiological Measurement - September 4, 2022 Category: Physiology Authors: Cristina Gallego V ázquez, Alexander Breuss, Oriella Gnarra, Julian Portmann, Antonio Madaffari and Giulia Da Poian Source Type: research
Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines
The proliferation of approaches for analyzing accelerometer data using raw acceleration or novel analytic approaches like machine learning ('novel methods') outpaces their implementation in practice. This may be due to lack of accessibility, either because authors do not provide their developed models or because these models are difficult to find when included as supplementary material. Additionally, when access to a model is provided, authors may not include example data or instructions on how to use the model. This further hinders use by other researchers, particularly those who are not experts in statistics or writing c...
Source: Physiological Measurement - September 4, 2022 Category: Physiology Authors: Kimberly A Clevenger, Alexander H K Montoye, Cailyn A Van Camp, Scott J Strath and Karin A Pfeiffer Source Type: research
Sources of automatic office blood pressure measurement error: a systematic review
Objective: Accurate and reliable blood pressure (BP) measurement is important for the prevention and treatment of hypertension. The oscillometric-based automatic office blood pressure measurement (AOBPM) is widely used in hospitals and clinics, but measurement errors are common in BP measurements. There is a lack of systematic review of the sources of measurement errors. Approach: A systematic review of all existing research on sources of AOBPM errors. A search strategy was designed in six online databases, and all the literature published before October 2021 was selected. Those studies that used the AOBPM device to measur...
Source: Physiological Measurement - September 4, 2022 Category: Physiology Authors: Jian Liu, Yumin Li, Jianqing Li, Dingchang Zheng and Chengyu Liu Source Type: research
Accessibility and use of novel methods for predicting physical activity and energy expenditure using accelerometry: a scoping review
Use of raw acceleration data and/or ‘novel’ analytic approaches like machine learning for physical activity measurement will not be widely implemented if methods are not accessible to researchers. Objective: This scoping review characterizes the validation approach, accessibility and use of novel analytic techniques for classifyin g energy expenditure and/or physical activity intensity using raw or count-based accelerometer data. Approach: Three databases were searched for articles published between January 2000 and February 2021. Use of each method was coded from a list of citing articles compiled from Google Scholar....
Source: Physiological Measurement - September 4, 2022 Category: Physiology Authors: Karin A Pfeiffer, Kimberly A Clevenger, Andrew Kaplan, Cailyn A Van Camp, Scott J Strath and Alexander H K Montoye Source Type: research
Accuracy enhancement in reflective pulse oximetry by considering wavelength-dependent pathlengths
Objective. Noninvasive measurement of oxygen saturation (SpO2) using transmissive photoplethysmography (tPPG) is clinically accepted and widely employed. However, reflective photoplethysmography (rPPG) —currently present in smartwatches—has not become equally accepted, partially because the pathlengths of the red and infrared PPGs are patient-dependent. Thus, even the most popular ‘Ratio of Modulation’ (R) method requires patient-dependent calibration to reduce the errors in the measuremen t of SpO2 using rPPGs. Approach. In this paper, a correction factor or ‘pathlength ratio’ β is introduced in an existing c...
Source: Physiological Measurement - September 4, 2022 Category: Physiology Authors: Idoia Badiola, Vladimir Blazek, V Jagadeesh Kumar, Boby George, Steffen Leonhardt and Christoph Hoog Antink Source Type: research
Issues in the automated classification of multilead ecgs using heterogeneous labels and populations
Objective. The standard twelve-lead electrocardiogram (ECG) is a widely used tool for monitoring cardiac function and diagnosing cardiac disorders. The development of smaller, lower-cost, and easier-to-use ECG devices may improve access to cardiac care in lower-resource environments, but the diagnostic potential of these devices is unclear. This work explores these issues through a public competition: the 2021 PhysioNet Challenge. In addition, we explore the potential for performance boosting through a meta-learning approach. Approach. We sourced 131,149 twelve-lead ECG recordings from ten international sources. We posted ...
Source: Physiological Measurement - August 25, 2022 Category: Physiology Authors: Matthew A Reyna, Nadi Sadr, Erick A Perez Alday, Annie Gu, Amit J Shah, Chad Robichaux, Ali Bahrami Rad, Andoni Elola, Salman Seyedi, Sardar Ansari, Hamid Ghanbari, Qiao Li, Ashish Sharma and Gari D Clifford Source Type: research
A pre-impact fall detection data segmentation method based on multi-channel convolutional neural network and class activation mapping
In this study, a trigger-based algorithm combining multi-channel convolutional neural network (CNN) and class activation mapping was proposed to solve the problem of data segmentation. First, a pre-impact fall detection training dataset was established and divided into two parts. For falls, the 1 s data was divided from the peak value of the acceleration signal magnitude vector to the starting direction. For activities of daily living, the cycle segmentation was performed for a 1 s window size. Second, a heat map of the class activation regions of the sensor data was formed using a multi-channel CNN and a class activation ...
Source: Physiological Measurement - August 25, 2022 Category: Physiology Authors: Mingxu Feng and Jizhong Liu Source Type: research
Uterine slow wave: directionality and changes with imminent delivery
Objective. The slow wave (SW) of the electrohysterogram (EHG) may contain relevant information on the electrophysiological condition of the uterus throughout pregnancy and labor. Our aim was to assess differences in the SW as regards the imminence of labor and the directionality of uterine myoelectrical activity. Approach. The SW of the EHG was extracted from the signals of the Icelandic 16-electrode EHG database in the bandwidth [5, 30] mHz and its power, spectral content, complexity and synchronization between the horizontal (X) and vertical (Y) directions were characterized by the root mean square (RMS), dominant freque...
Source: Physiological Measurement - August 18, 2022 Category: Physiology Authors: Monica Albaladejo-Belmonte, Gema Prats-Boluda, Yiyao Ye-Lin, Robert E Garfield and Javier Garcia-Casado Source Type: research
Effect of positive end-expiratory pressure on central venous pressure in the closed and open thorax
Objective. The magnitude and mechanism of the rise of central venous pressure (CVP) after positive end-expiratory pressure (PEEP) among patients with cardiac disease is poorly understood. Therefore, the study aimed to compare the magnitude of change in CVP after PEEP in patients with TR (tricuspid regurgitation), high CVP, and high PCWP (pulmonary capillary wedge pressure) and in those with no TR, low CVP, and low PCWP. Additionally, we hypothesized that PEEP in the open thorax would also lead to a rise in CVP. Approach. This prospective, quasi-experimental study was conducted in patients undergoing cardiac surgery. Three ...
Source: Physiological Measurement - August 18, 2022 Category: Physiology Authors: Lalit Jha, Suman Lata, Ajay Kumar Jha and Sreevathsa KS Prasad Source Type: research
Detecting beats in the photoplethysmogram: benchmarking open-source algorithms
This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology. Approach: Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using the F1 score, which combines sensitivity and positive predictive value. Main results: Eight beat detectors performed well in the absence of movement with F1 scores of ≥90% on hospital data and wearable data c...
Source: Physiological Measurement - August 18, 2022 Category: Physiology Authors: Peter H Charlton, Kevin Kotzen, Elisa Mej ía-Mejía, Philip J Aston, Karthik Budidha, Jonathan Mant, Callum Pettit, Joachim A Behar and Panicos A Kyriacou Source Type: research
Visualization deep learning model for automatic arrhythmias classification
Objective. With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. Electrocardiography (ECG) is an effective way of diagnosing cardiovascular diseases. With the rapid growth of ECG examinations and the shortage of cardiologists, accurate and automatic arrhythmias classification has become a research hotspot. The main purpose of this paper is to improve accuracy in detecting abnormal ECG patterns. Approach. A hybrid 1D Resnet-GRU method, consisting of the Resnet and gated recurrent unit (GRU) modules, is proposed to implement classification of arrhythmias fro...
Source: Physiological Measurement - August 11, 2022 Category: Physiology Authors: Mingfeng Jiang, Yujie Qiu, Wei Zhang, Jucheng Zhang, Zhefeng Wang, Wei Ke, Yongquan Wu and Zhikang Wang Source Type: research
A systematic review of deep learning methods for modeling electrocardiograms during sleep
Sleep is one of the most important human physiological activities, and plays an essential role in human health. Polysomnography (PSG) is the gold standard for measuring sleep quality and disorders, but it is time-consuming, labor-intensive, and prone to errors. Current research has confirmed the correlations between sleep and the respiratory/circulatory system. Electrocardiography (ECG) is convenient to perform, and ECG data are rich in breathing information. Therefore, sleep research based on ECG data has become popular. Currently, deep learning (DL) methods have achieved promising results on predictive health care tasks ...
Source: Physiological Measurement - August 11, 2022 Category: Physiology Authors: Chenxi Sun, Shenda Hong, Jingyu Wang, Xiaosong Dong, Fang Han and Hongyan Li Source Type: research
Multiscale partial information decomposition of dynamic processes with short and long-range correlations: theory and application to cardiovascular control
Objective. In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations. Approach. We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H...
Source: Physiological Measurement - August 11, 2022 Category: Physiology Authors: H élder Pinto, Riccardo Pernice, Maria Eduarda Silva, Michal Javorka, Luca Faes and Ana Paula Rocha Source Type: research
