A novel algorithm for spirometric signal processing and classification by evolutionary approach and its implementation on an ARM embedded platform

Nandakumar, Lakshmi; Nandakumar, Parvathy

doi:10.1109/iccc.2013.6731684

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…In the literature, we found studies that evaluated different software for the interpretation of spirometries, but none of them was a mobile app. In 2013, Nandakumar et al evaluated a software for spirometry interpretation and found an average accuracy of 95.74% [ 36 ]. More recently, in 2022, Wang et al explored the accuracy of deep learning-based analytic models based on flow-volume curves; they found that one of the models exhibited an accuracy of 95.6% when interpreting ventilatory patterns and that the physicians had an accuracy of 76.9 ± 18.4% [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Validity of the Espiro Mobile Application in the Interpretation of Spirometric Patterns: An App Accuracy Study

Savic-Pesic,

Chamorro,

Lopez-Rodriguez

et al. 2023

Diagnostics

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Spirometry is a pulmonary function test where correct interpretation of the results is crucial for accurate diagnosis of disease. There are online tools to assist in the interpretation of spirometry results; however, as yet none are validated. We evaluated the interpretation accuracy of the Espiro app using pulmonologist interpretations as the gold standard. This is an observational descriptive study in which 118 spirometry results were interpreted by the Espiro app, two pulmonologists, two primary care physicians, and two residents of a primary care training program. We determined the interpretation accuracy of the Espiro app and the concordance of the pattern and severity interpretation between the Espiro app and each of the observers using Cohen’s kappa coefficient (k). We obtained a sensitivity and specificity for the Espiro app of 97.5% (95% confidence interval (CI): 86.8–99.9%) and 94.9% (95%CI: 87.4–98.6%) with pulmonologist 1 and 100% (95%CI: 91.6–100%) and 98.7% (95%CI: 92.9–99.9%) with pulmonologist 2. The concordance for the pattern interpretation was greater than k 0.907, representing almost perfect agreement. The concordance of the severity interpretation was greater than k 0.807, representing substantial to almost perfect agreement. We concluded that the Espiro app is a valid tool for spirometry interpretation.

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Section: Discussionmentioning

confidence: 99%

Validity of the Espiro Mobile Application in the Interpretation of Spirometric Patterns: An App Accuracy Study

Savic-Pesic,

Chamorro,

Lopez-Rodriguez

et al. 2023

Diagnostics

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“…A study showed an accuracy of 97.6% when using flow-volume curves and artificial intelligence algorithms to identify normal and abnormal ventilatory patterns (Jafari et al, 2010). Moreover, some studies involving small sample size explored algorithms for PFT signal processing and classification (Veezhinathan and Ramakrishnan, 2007;Sahin et al, 2010;Nandakumar and Nandakumar, 2013). Topalovic et al (2019) developed a model to recognize normal, obstructive, restrictive, and mixed ventilatory patterns based on spirometry and lung volume test results according to the ATS/ERS guideline.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Analytic Models Based on Flow-Volume Curves for Identifying Ventilatory Patterns

Wang

Chen

et al. 2022

Front. Physiol.

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IntroductionSpirometry, a pulmonary function test, is being increasingly applied across healthcare tiers, particularly in primary care settings. According to the guidelines set by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), identifying normal, obstructive, restrictive, and mixed ventilatory patterns requires spirometry and lung volume assessments. The aim of the present study was to explore the accuracy of deep learning-based analytic models based on flow–volume curves in identifying the ventilatory patterns. Further, the performance of the best model was compared with that of physicians working in lung function laboratories.MethodsThe gold standard for identifying ventilatory patterns was the rules of ATS/ERS guidelines. One physician chosen from each hospital evaluated the ventilatory patterns according to the international guidelines. Ten deep learning models (ResNet18, ResNet34, ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet50_vc, SE_ResNet18_vd, VGG11, VGG13, and VGG16) were developed to identify patterns from the flow–volume curves. The patterns obtained by the best-performing model were cross-checked with those obtained by the physicians.ResultsA total of 18,909 subjects were used to develop the models. The ratio of the training, validation, and test sets of the models was 7:2:1. On the test set, the best-performing model VGG13 exhibited an accuracy of 95.6%. Ninety physicians independently interpreted 100 other cases. The average accuracy achieved by the physicians was 76.9 ± 18.4% (interquartile range: 70.5–88.5%) with a moderate agreement (κ = 0.46), physicians from primary care settings achieved a lower accuracy (56.2%), while the VGG13 model accurately identified the ventilatory pattern in 92.0% of the 100 cases (P < 0.0001).ConclusionsThe VGG13 model identified ventilatory patterns with a high accuracy using the flow–volume curves without requiring any other parameter. The model can assist physicians, particularly those in primary care settings, in minimizing errors and variations in ventilatory patterns.

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Classification of Spirometry Using Stacked Autoencoder based Neural Network

Trivedy

Goyal

Mishra

et al. 2019

2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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A novel algorithm for spirometric signal processing and classification by evolutionary approach and its implementation on an ARM embedded platform

Cited by 8 publications

References 12 publications

Validity of the Espiro Mobile Application in the Interpretation of Spirometric Patterns: An App Accuracy Study

Validity of the Espiro Mobile Application in the Interpretation of Spirometric Patterns: An App Accuracy Study

Deep Learning-Based Analytic Models Based on Flow-Volume Curves for Identifying Ventilatory Patterns

Classification of Spirometry Using Stacked Autoencoder based Neural Network

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