A Machine Learning Model for Real-Time Asynchronous Breathing Monitoring

Loo, Nien Loong; Chiew, Yeong Shiong; Tan, Chee Pin; Arunachalam, Ganesa Ramachandran; Ralib, Azrina; Mat-Nor, Mohd Basri

doi:10.1016/j.ifacol.2018.11.610

Cited by 30 publications

(18 citation statements)

References 20 publications

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“…However, we reckon that the problem of motion artifact removal should be taken into account since in a real-life scenario, movements of the subjects can occur. To overcome this concern, different solutions have been proposed for both RR [ 20 , 28 , 47 , 48 ] and regarding HR [ 33 , 41 , 49 , 50 , 51 ]. Future works will be devoted to defining a tailored approach on our system, by combining the two different sensor technologies embedded (i.e., textile strain sensors and an M-IMU) to develop a sensor-fusion algorithm to remove motion artifacts occurring during real life.…”

Section: Discussionmentioning

confidence: 99%

A Wearable System with Embedded Conductive Textiles and an IMU for Unobtrusive Cardio-Respiratory Monitoring

Tocco

Raiano

Sabbadini

et al. 2021

Sensors

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The continuous and simultaneous monitoring of physiological parameters represents a key aspect in clinical environments, remote monitoring and occupational settings. In this regard, respiratory rate (RR) and heart rate (HR) are correlated with several physiological and pathological conditions of the patients/workers, and with environmental stressors. In this work, we present and validate a wearable device for the continuous monitoring of such parameters. The proposed system embeds four conductive sensors located on the user’s chest which allow retrieving the breathing activity through their deformation induced during cyclic expansion and contraction of the rib cage. For monitoring HR we used an embedded IMU located on the left side of the chest wall. We compared the proposed device in terms of estimating HR and RR against a reference system in three scenarios: sitting, standing and supine. The proposed system reliably estimated both RR and HR, showing low error averaged along subjects in all scenarios. This is the first study focused on the feasibility assessment of a wearable system based on a multi-sensor configuration (i.e., conductive sensors and IMU) for RR and HR monitoring. The promising results encourage the application of this approach in clinical and occupational settings.

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Section: Discussionmentioning

confidence: 99%

A Wearable System with Embedded Conductive Textiles and an IMU for Unobtrusive Cardio-Respiratory Monitoring

Tocco

Raiano

Sabbadini

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…Some of them transformed the 1D time series into spectrograms using wavelet transforms, Fourier transforms, etc. [ 19 , 35 ], whereas others obtained time–domain characteristics by plotting the waveforms directly onto a canvas [ 36 , 37 ]. The former strategy aimed to highlight the time–frequency characteristics, whereas the latter focused on the original time–domain information.…”

Section: Discussionmentioning

confidence: 99%

“…This led to distortion of waveforms at different lengths. Although most breaths last for 4–6 s and 224 samples were sufficient to represent the characteristics of the normal and PVA cycles (longer than previously reported 150 samples for CNN [ 37 ]), the influence of the resampling processing should be investigated in the future. Future studies are required to apply this approach in real clinical settings.…”

Section: Discussionmentioning

confidence: 99%

Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning

Pan

Jia

Liu

et al. 2021

Sensors

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Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient–ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.

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“…Mulqueeny et al [44] used a naïve Bayes machine learning algorithm with 21 features, including measures of respiratory rate, tidal volume, respiratory mechanics and expiratory flow morphology to a dataset of 5624 breaths manually classified by a single observer, resulting in an accuracy of 84%, but a sensitivity of only 59%. Loo et al [45] trained a convolutional neural network with 5500 abnormal and 5500 normal breathing cycles aimed at developing an algorithm capable of separating normal from abnormal breathing cycles, reporting 96.9% sensitivity and 63.7% specificity.…”

Section: Mechanical Ventilationmentioning

confidence: 99%