Automatic quality assessment of capacitively-coupled bioimpedance signals for respiratory activity monitoring. (July 2021) - Albaba, Adnan; Castro, Ivan; Borzée, Pascal; Buyse, Bertien; Testelmans, Dries; Varon, Carolina; Van Huffel, Sabine; Torfs, Tom
Highlights: Non-contact monitoring of capacitively-coupled bioimpedance (ccBioZ) comes at the cost of signal quality. An automated ML-based approach to assess the quality of ccBioZ signals is demonstrated, tested, and statistically evaluated. A multi-stage testing pipeline, composed of three testing phases, is applied for statistically evaluating the classifiers. Three large datasets of ccBioZ acquired with varying characteristics were utilized. High performance is reported with balanced accuracy up to 94 % using a Fine Gaussian SVM model with 13 features out of 52. Abstract: The objective of this work is to design an algorithm capable of classifying segments of capacitively-coupled bioimpedance (ccBioZ) for respiratory activity monitoring based on their signal quality. Such algorithm is an important building block as a pre-processing step to increase the confidence of extracted information such as respiration rate (RR). Long over-night ccBioZ recordings acquired from 12 subjects, are used for training and testing the proposed algorithm. To create a ground-truth labelling, the annotation is done manually by an experienced biomedical engineer. A total number of 52 features are extracted to capture information related to the quality of the ccBioZ segments. Five subsets of features are selected based on five different feature selection methods and tested against a full set of features to find the best trade-off between the performance of the classifier and the number of features. For classification, 19 classifiers are trained, cross-validated, and tested in three different datasets, acquired from 12 subjects: DS1 (training and validation data from 11 patients with suspected sleep apnea), DS2 (containing apneic epochs acquired from the same 11 patients), and DS3 (testing data acquired from one healthy subject). The balanced accuracy is used along with other statistical evaluation metrics. For each test set, the best results of the quantitative evaluation came as following; DS1: Accuracy = 0.91, Sensitivity = 0.90, Specificity = 0.95, and Balanced Accuracy = 0.91. DS2: Accuracy = 0.87, Sensitivity = 0.88, Specificity = 0.87, and Balanced Accuracy = 0.88. DS3: Accuracy = 0.91, Sensitivity = 0.98, Specificity = 0.91, and Balanced Accuracy = 0.94. The results of the testing phases prove the reliability and robustness of the presented approach.