A Review on methods using capnogram signals for detecting asthmatic patients

Abstract

In this paper, a review on methods for detecting the asthmatic conditions by using capnogram is presented. This is included an investigation on capnography as a new approach for monitoring asthma and related researches. Finally, in this preliminary study, a new method based on signal processing approach is developed to automatic detection of asthmatic patients. This developed algorithm is expected to cluster different states of asthmatic conditions by using the RBF (Radial Basis Function) network, instantaneously. However, manual analysis of capnogram is time-consuming and led to erroneous due to human factor such as tiredness and lack of expertise. In the future, this developed algorithm is expected to help heathcare professional involved in respiratory care as it would be possible to monitor severity of asthma automatically and instantaneously.

Use of capnography for prediction of obstruction severity in non-intubated COPD and asthma patients

“…If the network architecture is too complex, e.g., has too many hidden units, or too many input features, then it may fit the noise and outliers, leading to overfitting. Such models perform well on the training set but poorly on new input [23]. To avoid overfitting, model parameters must be chosen correctly, with appropriate regularization technique.…”

Carbon Dioxide Waveform Features During Pre and Post Treatment Asthmatic Patient in Emergency Department

“…Besides, patients who cannot cooperate due to the diseases process or age are unable to perform these assessment methods. As a substitute, the assessment of features extracted from the morphology of the CO 2 waveform, measured by capnography, has been proposed as a pointer of bronchospasm in asthma [6][7][8][9][10][11][12][13][14][15].…”

“…Several studies, conducted in subjects with asthma [6][7][8][9][10][11][12][13][14][15]22], have shown a significant association between the FEV1, PEFR, and indices of the CO 2 waveform. One of the first studies conducted by You et al (1992) on adult asthmatic subjects, reported the excellent correlation between the capnographic index (end-tidal slope) and spirometric index (FEV1%).…”

“…Furthermore, the studies conducted by M.B. Malarvili et al [12], Betancourt et al [13], Kazemi et al [15], and Malik et al [23] disclosed the computer-based algorithm for the classification of asthma and non-asthma. They investigated time and frequency domain indices and proposed to incorporate into the CO 2 measurement device to differentiate asthma and non-asthma conditions.…”