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Automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: A machine learning-based approach

El-Badawy IM, Singh OP, Omar Z

Technol Health Care, 2021;29(1):59-72.
PMID: 32716337 DOI: 10.3233/THC-202198

BACKGROUND: The quantitative features of a capnogram signal are important clinical metrics in assessing pulmonary function. However, these features should be quantified from the regular (artefact-free) segments of the capnogram waveform.
OBJECTIVE: This paper presents a machine learning-based approach for the automatic classification of regular and irregular capnogram segments.
METHODS: Herein, we proposed four time- and two frequency-domain features experimented with the support vector machine classifier through ten-fold cross-validation. MATLAB simulation was conducted on 100 regular and 100 irregular 15 s capnogram segments. Analysis of variance was performed to investigate the significance of the proposed features. Pearson's correlation was utilized to select the relatively most substantial ones, namely variance and the area under normalized magnitude spectrum. Classification performance, using these features, was evaluated against two feature sets in which either time- or frequency-domain features only were employed.
RESULTS: Results showed a classification accuracy of 86.5%, which outperformed the other cases by an average of 5.5%. The achieved specificity, sensitivity, and precision were 84%, 89% and 86.51%, respectively. The average execution time for feature extraction and classification per segment is only 36 ms.
CONCLUSION: The proposed approach can be integrated with capnography devices for real-time capnogram-based respiratory assessment. However, further research is recommended to enhance the classification performance.

Matched MeSH terms: Capnography*
Frequency analysis of capnogram signals to differentiate asthmatic and non-asthmatic conditions using radial basis function neural networks

Kazemi M, Bala Krishnan M, Aik Howe T

Iran J Allergy Asthma Immunol, 2013 Sep;12(3):236-46.
PMID: 23893807

In this paper, the method of differentiating asthmatic and non-asthmatic patients using the frequency analysis of capnogram signals is presented. Previously, manual study on capnogram signal has been conducted by several researchers. All past researches showed significant correlation between capnogram signals and asthmatic patients. However all of them are just manual study conducted through the conventional time domain method. In this study, the power spectral density (PSD) of capnogram signals is estimated by using Fast Fourier Transform (FFT) and Autoregressive (AR) modelling. The results show the non-asthmatic capnograms have one component in their PSD estimation, in contrast to asthmatic capnograms that have two components. Furthermore, there is a significant difference between the magnitude of the first component for both asthmatic and non-asthmatic capnograms. The effectiveness and performance of manipulating the characteristics of the first frequency component, mainly its magnitude and bandwidth, to differentiate between asthmatic and non-asthmatic conditions by means of receiver operating characteristic (ROC) curve analysis and radial basis function (RBF) neural network were shown. The output of this network is an integer prognostic index from 1 to 10 (depends on the severity of asthma) with an average good detection rate of 95.65% and an error rate of 4.34%. This developed algorithm is aspired to provide a fast and low-cost diagnostic system to help healthcare professional involved in respiratory care as it would be possible to monitor severity of asthma automatically and instantaneously.

Matched MeSH terms: Capnography*
Use of capnography delaying the diagnosis of tracheal intubation

Chan YK, Zuraidah S, Tan PS

Anaesthesia, 1998 Dec;53(12):1207-8.
PMID: 10193227

There was a delay in making the correct diagnosis of tracheal intubation in a parturient who developed severe bronchospasm after intubation because we relied on the capnogram.

Matched MeSH terms: Capnography*
Fulltext End-tidal to arterial carbon dioxide partial pressure difference during craniotomy in anaesthetised patients

Husaini J, Choy YC

Med J Malaysia, 2008 Dec;63(5):384-7.
PMID: 19803296 MyJurnal

This study to evaluate the relationship between end-tidal carbon dioxide pressure (ETCO2) and arterial partial pressure of carbon dioxide (PaCO2) included 35 patients between the ages of 18 and 65 years, ASA grade 1 and 2, who had elective craniotomies. Measurements of PaCO2 and ETCO2 were taken simultaneously: 1) 10 minutes after induction of general anaesthesia, 2) after cranium opening prior to dural incision, 3) start of dural closure. There was significant correlation between ETCO2 and PaCO2 (correlation coefficient: 0.571, 0.559 and 0.629 respectively). The mean (SD) difference for PaCO2 and ETCO2 were: 3.84 (2.13), 4.85 (5.78) and 3.91 (2.33) mmHg respectively. Although there was agreement, the bias is of significant clinical importance. In conclusion, we find that ETCO2 consistently underestimated the value of PaCO2 during craniotomy.

Matched MeSH terms: Capnography*
The use of end-tidal capnography to monitor non-intubated patients presenting with acute exacerbation of asthma in the emergency department

Howe TA, Jaalam K, Ahmad R, Sheng CK, Nik Ab Rahman NH

J Emerg Med, 2011 Dec;41(6):581-9.
PMID: 19272745 DOI: 10.1016/j.jemermed.2008.10.017

STUDY OBJECTIVE: To determine if the slope of Phase II and Phase III, and the alpha angle of the expiratory capnographic waveform, as measured via computer-recognizable algorithms, can reflect changes in bronchospasm in acute asthmatic non-intubated patients presenting to the emergency department (ED).
METHODS: In this prospective study carried out in a university hospital ED, 30 patients with acute asthma were monitored with clinical severity scoring and peak flow measurements, and then had a nasal cannula attached for sidestream sampling of expired carbon dioxide. The capnographic waveform was recorded onto a personal computer card for analysis. The patients were treated according to departmental protocols. After treatment, when they had improved enough for discharge, a second set of results was obtained for capnographic waveform recording. The pre-treatment and post-treatment results were then compared with paired-samples t-test analysis.
RESULTS: On the capnographic waveform pre- and post-treatment, there was a significant difference in the slope of Phase III (p < 0.001) and alpha angle (p < 0.001), but not in the Phase II slope (p = 0.35). There was significant change in peak flow meter reading, but it was poorly correlated with all the capnographic indices.
CONCLUSION: The study provides some preliminary data showing that capnographic waveform indices can indicate improvement in airway diameter in acute asthmatics in the ED. Capnographic waveform analysis presents several advantages in that it is effort-independent, and provides continuous monitoring of normal tidal respiration. With further refined studies, it may serve as a new method of monitoring non-intubated asthmatics in the ED.
Study site: Emergency department, Hospital Universiti Sains Malaysia (HUSM), Kubang Kerian, Kelantan, Malaysia

Matched MeSH terms: Capnography/methods*