Dataset Information

Detection of Abnormal Respiration from Multiple-Input Respiratory Signals.

ABSTRACT: In this paper, we propose a novel approach for the detection of abnormal signals from multiple respiration signals. An ultrawide-band (UWB) radar was used to acquire respiration signals that represent a distance from the chest to the radar sensor, i.e., shape variation of the chest due to breathing (inhaling or exhaling) activity provides quantitative information (distance values) about respiratory status. Distribution, shape, and variation of values across time provide information to determine respiratory status, one of the most important indicators of human health. In this paper, respiratory status was categorized into two classes, normal and abnormal. Abnormal respiration (apnea in this paper) was emulated by interrupting breathing activity because it is difficult to acquire real apnea from patients in hospital wards. This paper considered two cases, single and multiple respiration. In the first case, a single normal- or abnormal-respiration signal was used as input, and output was the classified status of respiration. In the second case, multiple respiration signals were simultaneously used as inputs, and we focused on determining the existence of abnormal signals in multiple respiration signals. In the case of multiple inputs, filters with varying cut-off frequency were applied to input signals followed by the analysis of output signals in response to the filters. To substantiate the proposed method, experiment results are provided. In this paper, classification results showed 93 % of the successful rate in the case of multiple inputs, and results are promising for applications to monitoring systems of human respiration.

SUBMITTER: Kim JO

PROVIDER: S-EPMC7285501 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Publications

Detection of Abnormal Respiration from Multiple-Input Respiratory Signals.

Kim Ju O JO Lee Deokwoo D

Sensors (Basel, Switzerland) 20200524 10

In this paper, we propose a novel approach for the detection of abnormal signals from multiple respiration signals. An ultrawide-band (UWB) radar was used to acquire respiration signals that represent a distance from the chest to the radar sensor, i.e., shape variation of the chest due to breathing (inhaling or exhaling) activity provides quantitative information (distance values) about respiratory status. Distribution, shape, and variation of values across time provide information to determine ...[more]

PMID: 32456350

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Project description:BackgroundWhile the conserved positions of a multiple sequence alignment (MSA) are clearly of interest, non-conserved positions can also be important because, for example, destabilizing effects at one position can be compensated by stabilizing effects at another position. Different methods have been developed to recognize the evolutionary relationship between amino acid sites, and to disentangle functional/structural dependencies from historical/phylogenetic ones.Methodology/principal findingsWe have used two complementary approaches to test the efficacy of these methods. In the first approach, we have used a new program, MSAvolve, for the in silico evolution of MSAs, which records a detailed history of all covarying positions, and builds a global coevolution matrix as the accumulated sum of individual matrices for the positions forced to co-vary, the recombinant coevolution, and the stochastic coevolution. We have simulated over 1600 MSAs for 8 protein families, which reflect sequences of different sizes and proteins with widely different functions. The calculated coevolution matrices were compared with the coevolution matrices obtained for the same evolved MSAs with different coevolution detection methods. In a second approach we have evaluated the capacity of the different methods to predict close contacts in the representative X-ray structures of an additional 150 protein families using only experimental MSAs.Conclusions/significanceMethods based on the identification of global correlations between pairs were found to be generally superior to methods based only on local correlations in their capacity to identify coevolving residues using either simulated or experimental MSAs. However, the significant variability in the performance of different methods with different proteins suggests that the simulation of MSAs that replicate the statistical properties of the experimental MSA can be a valuable tool to identify the coevolution detection method that is most effective in each case.

Project description:Background: Objective gait assessment is key for the follow-up of patients with progressive multiple sclerosis (pMS). Inertial measurement units (IMUs) provide reliable and yet easy quantitative gait assessment in routine clinical settings. However, to the best of our knowledge, no automated step-detection algorithm performs well in detecting severely altered pMS gait. Method: This article elaborates on a step-detection method based on personalized templates tested against a gold standard. Twenty-two individuals with pMS and 10 young healthy subjects (HSs) were instructed to walk on an electronic walkway wearing synchronized IMUs. Templates were derived from the IMU signals by using Initial and Final Contact times given by the walkway. These were used to detect steps from other gait trials of the same individual (intra-individual template-based detection, IITD) or another participant from the same group (pMS or HS) (intra-group template-based detection, IGTD). All participants were seen twice with a 6-month interval, with two measurements performed at each visit. Performance and accuracy metrics were computed, along with a similarity index (SId), which was computed as the mean distance between detected steps and their respective closest template. Results: For HS participants, both the IITD and the IGTD algorithms had precision and recall of 1.00 for detecting steps. For pMS participants, precision and recall ranged from 0.94 to 1.00 for IITD and 0.85 to 0.95 for IGTD depending on the level of disability. The SId was correlated with performance and the accuracy of the result. An SId threshold of 0.957 (IITD) and 0.963 (IGTD) could rule out decreased performance (F-measure ? 0.95), with negative predictive values of 0.99 and 0.96 with the IITD and IGTD algorithms. Also, the SId computed with the IITD and IGTD algorithms could distinguish individuals showing changes at 6-month follow-up. Conclusion: This personalized step-detection method has high performance for detecting steps in pMS individuals with severely altered gait. The algorithm can be self-evaluating with the SI, which gives a measure of the confidence the clinician can have in the detection. What is more, the SId can be used as a biomarker of change in disease severity occurring between the two measurement times.

Dataset Information

Detection of Abnormal Respiration from Multiple-Input Respiratory Signals.

Publications

Detection of Abnormal Respiration from Multiple-Input Respiratory Signals.

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