Novel approach to computerized breath detection in lung function diagnostics
Autor: | Václav Koucký, Jaroslav Horáček, Milan Hladík |
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Rok vydání: | 2018 |
Předmět: |
Male
Adolescent Computer science 0206 medical engineering Health Informatics 02 engineering and technology Pulmonary function testing 03 medical and health sciences 0302 clinical medicine Humans Diagnosis Computer-Assisted Child MULTIPLE BREATH WASHOUT Lung function business.industry Current threshold Infant Signal Processing Computer-Assisted Pattern recognition Gas concentration 020601 biomedical engineering Respiratory Function Tests Computer Science Applications 030228 respiratory system Child Preschool Data quality Female Artificial intelligence business Algorithms Smoothing Pulmonary disorders |
Zdroj: | Computers in Biology and Medicine. 101:1-6 |
ISSN: | 0010-4825 |
Popis: | Background Breath detection, i.e. its precise delineation in time is a crucial step in lung function data analysis as obtaining any clinically relevant index is based on the proper localization of breath ends. Current threshold or smoothing algorithms suffer from severe inaccuracy in cases of suboptimal data quality. Especially in infants, the precise analysis is of utmost importance. The key objective of our work is to design an algorithm for accurate breath detection in severely distorted data. Methods Flow and gas concentration data from multiple breath washout test were the input information. Based on universal physiological characteristics of the respiratory tract we designed an algorithm for breath detection. Its accuracy was tested on severely distorted data from 19 patients with different types of breathing disorders. Its performance was compared to the performance of currently used algorithms and to the breath counts estimated by human experts. Results The novel algorithm outperformed the threshold algorithms with respect to their accuracy and had similar performance to human experts. It proved to be a highly robust and efficient approach in severely distorted data. This was demonstrated on patients with different pulmonary disorders. Conclusion Our newly proposed algorithm is highly robust and universal. It works accurately even on severely distorted data, where the other tested algorithms failed. It does not require any pre-set thresholds or other patient-specific inputs. Consequently, it may be used with a broad spectrum of patients. It has the potential to replace current approaches to the breath detection in pulmonary function diagnostics. |
Databáze: | OpenAIRE |
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