Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added value of machine learning algorithms to separate clean from noisy bio-impedance signals. We compared three approaches: a heuristic algorithm, a feature-based classification model (SVM) and a convolutional neural network (CNN). The dataset consists of 47 chronic obstructive pulmonary disease patients who performed an inspiratory threshold loading protocol. During this protocol, their respiration was recorded with a bio-impedance device and a spirometer, which served as a gold standard. Four annotators scored the signals for the presence of artefacts, based on the reference signal. We have shown that the accuracy of both machine learning approaches (SVM: 87.77 ± 2.64% and CNN: 87.20 ± 2.78%) is significantly higher, compared to the heuristic approach (84.69 ± 2.32%). Moreover, no significant differences could be observed between the two machine learning approaches. The feature-based and neural network model obtained a respective AUC of 92.77±2.95% and 92.51±1.74%. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

Towards adaptive deep brain stimulation: clinical and technical notes on a novel commercial device for chronic brain sensing

ABSTRACTBackgroundTechnical advances in deep brain stimulation (DBS) are crucial to improve therapeutic efficacy and battery life. A prerogative of new devices is the recording and processing of a given input signal to instruct the delivery of stimulation.ObjectiveWe studied the advances and pitfalls of one of the first commercially available devices capable of recording brain local field potentials (LFP) from the implanted DBS leads, chronically and during stimulation.MethodsWe collected clinical and neurophysiological data of the first 20 patients (14 with Parkinson’s disease [PD], five with various types of dystonia, one with chronic pain) that received the Percept™ PC in our centers. We also performed tests in a saline bath to validate the recordings quality.ResultsThe Percept PC reliably recorded the LFP of the implanted site, wirelessly and in real time. We recorded the most promising clinically useful biomarkers for PD and dystonia (beta and theta oscillations) with and without stimulation. Critical aspects of the system are presently related to contact selection, artefact detection, data loss, and synchronization with other devices. Furthermore, we provide an open-source code to facilitate export and analysis of data.ConclusionNew technologies will soon allow closed-loop neuromodulation therapies, capable of adapting the stimulation based on real-time symptom-specific and task-dependent input signals. However, technical aspects need to be considered to ensure clean synchronized recordings. The critical use by a growing number of DBS experts will alert new users about the currently observed shortcomings and inform on how to overcome them.

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.

Snow Supervision in Digital Pathology: Managing Imperfect Annotations for Segmentation in Deep Learning

In digital pathology, image segmentation algorithms are usually ranked on clean, benchmark datasets. However, annotations in digital pathology are hard, time-consuming and by nature imperfect. We expand on the SNOW (Semi-, Noisy and/or Weak) supervision concept introduced in an earlier work to characterize such data supervision imperfections. We analyse the effects of SNOW supervision on typical DCNNs, and explore learning strategies to counteract those effects. We apply those lessons to the real-world task of artefact detection in whole-slide imaging. Our results show that SNOW supervision has an important impact on the performances of DCNNs and that relying on benchmarks and challenge datasets may not always be relevant for assessing algorithm performance. We show that a learning strategy adapted to SNOW supervision, such as “Generative Annotations", can greatly improve the results of DCNNs on real-world datasets.

Perceptually motivated model for predicting banding artefacts in high-dynamic range images

Banding is a type of quantisation artefact that appears when a low-texture region of an image is coded with insufficient bitdepth. Banding artefacts are well-studied for standard dynamic range (SDR), but are not well-understood for high dynamic range (HDR). To address this issue, we conducted a psychophysical experiment to characterise how well human observers see banding artefacts across a wide range of luminances (0.1 cd/m2–10,000 cd/m2). The stimuli were gradients modulated along three colour directions: black-white, red-green, and yellow-violet. The visibility threshold for banding artefacts was the highest at 0.1 cd/m2, decreased with increasing luminance up to 100 cd/m2, then remained at the same level up to 10,000 cd/m2. We used the results to develop and validate a model of banding artefact detection. The model relies on the contrast sensitivity function (CSF) of the visual system, and hence, predicts the visibility of banding artefacts in a perceptually accurate way.