Low-resolution thermal cameras have already been used in the detection of respiratory flow. However, microbolometer technology has a high production cost compared to thermopile arrays. In this work, the feasibility of using a thermopile array to detect respiratory flow has been investigated in multiple settings. To prove the concept, we tested the detector on six healthy subjects. Our method automatically selects the region-of-interest by discriminating between sensor elements that output noise and flow-induced signals. The thermopile array yielded an average root mean squared error of 1.59 b r e a t h s p e r m i n u t e . Parameters such as distance, breathing rate, orientation, and oral or nasal breathing resulted in being fundamental in the detection of respiratory flow. The paper provides the proof-of-concept that low-cost thermopile-arrays can be used to monitor respiratory flow in a lab setting and without the need for facial landmark detection. Further development could provide a more attractive alternative for the earlier bolometer-based proposals.
In-vitro fertilization (IVF) is the most advanced treatment for infertility problems; however, its failure rate is still above 70% and the exact causes are often unknown. There is increasing evidence of the involvement of uterine contractions in IVF failure, especially during and after embryo transfer (ET). In this paper, we propose a new method to predict the success of IVF based on quantitative features extracted from electrohysterography (EHG) and B-mode transvaginal ultrasound (TVUS) recordings. To this end, probabilistic classification of the uterine activity, as either favorable or adverse to embryo implantation, is investigated using machine learning. Prior to machine learning, an additional method for EHG and TVUS feature extraction is here proposed that is based on singular value decomposition of the acquired EHG and TVUS recordings. Sixteen women were measured during three phases of the IVF treatment: follicular stimulation (FS), one hour before embryo transfer (ET1), and five to seven days after ET (ET5-7). After feature space reduction by correlation filtering, three machine-learning models, namely, support vector machine (SVM), K-nearest neighbors (KNN), and Gaussian mixture model (GMM), were optimized and tested by nested leave-one-out cross validation for their ability to predict successful embryo implantation. The highest accuracy (93.8%) was achieved by KNN in all phases and by SVM and in the FS and ET1 phases. Contraction frequency, unnormalized first moment and standard deviation, obtained from EHG and TVUS analysis, were the best features selected by the three classifiers. Our results show a multi-modal, multi-parametric strategy based on quantitative features to represent a novel, promising option for prediction of successful embryo implantation, overcoming the limitations of alternative approaches based on qualitative assessment of clinical variables. Yet, a larger dataset is required for improved training of the classifiers, as well as to assess their clinical value in the context of IVF procedures. INDEX TERMS In-vitro fertilization-Uterine activity-Electrohysterography-Ultrasound-Feature selection-Machine learning.
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Large numbers of asynchronies during pressure support ventilation cause discomfort and higher work of breathing in the patient, and are associated with an increased mortality. There is a need for real-time decision support to detect asynchronies and assist the clinician towards lung-protective ventilation. Machine learning techniques have been proposed to detect asynchronies, but they require large datasets with sufficient data diversity, sample size, and quality for training purposes. In this work, we propose a method for generating a large, realistic and labeled, synthetic dataset for training and validating machine learning algorithms to detect a wide variety of asynchrony types. We take a model-based approach in which we adapt a non-linear lung-airway model for use in a diverse patient group and add a first-order ventilator model to generate labeled pressure, flow, and volume waveforms of pressure support ventilation. The model was able to reproduce basic measured lung mechanics parameters. Experienced clinicians were not able to differentiate between the simulated waveforms and clinical data (P = 0.44 by Fisher’s exact test). The detection performance of the machine learning trained on clinical data gave an overall comparable true positive rate on clinical data and on simulated data (an overall true positive rate of 94.3% and positive predictive value of 93.5% on simulated data and a true positive rate of 98% and positive predictive value of 98% on clinical data). Our findings demonstrate that it is possible to generate labeled pressure and flow waveforms with different types of asynchronies.
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