Automated Video-Based Approach for the Diagnosis of Tourette Syndrome.

BACKGROUND: The occurrence of tics is the main basis for the diagnosis of Gilles de la Tourette syndrome (GTS). Video-based tic assessments are time consuming. OBJECTIVE: The aim was to assess the potential of automated video-based tic detection for discriminating between videos of adults with GTS and healthy control (HC) participants. METHODS: The quantity and temporal structure of automatically detected tics/extra movements in videos from adults with GTS (107 videos from 42 participants) and matched HCs were used to classify videos using cross-validated logistic regression. RESULTS: Videos were classified with high accuracy both from the quantity of tics (balanced accuracy of 87.9%) and the number of tic clusters (90.2%). Logistic regression prediction probability provides a graded measure of diagnostic confidence. Expert review of about 25% of lower-confidence predictions could ensure an overall classification accuracy above 95%. CONCLUSIONS: Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.

Wearable-based human flow experience recognition enhanced by transfer learning methods using emotion data.

BACKGROUND: Flow experience is a specific positive and affective state that occurs when humans are completely absorbed in an activity and forget everything else. This state can lead to high performance, well-being, and productivity at work. Few studies have been conducted to determine the human flow experience using physiological wearable sensor devices. Other studies rely on self-reported data. METHODS: In this article, we use physiological data collected from 25 subjects with multimodal sensing devices, in particular the Empatica E4 wristband, the Emotiv Epoc X electroencephalography (EEG) headset, and the Biosignalplux RespiBAN - in arithmetic and reading tasks to automatically discriminate between flow and non-flow states using feature engineering and deep feature learning approaches. The most meaningful wearable device for flow detection is determined by comparing the performances of each device. We also investigate the connection between emotions and flow by testing transfer learning techniques involving an emotion recognition-related task on the source domain. RESULTS: The EEG sensor modalities yielded the best performances with an accuracy of 64.97%, and a macro Averaged F1 (AF1) score of 64.95%. An accuracy of 73.63% and an AF1 score of 72.70% were obtained after fusing all sensor modalities from all devices. Additionally, our proposed transfer learning approach using emotional arousal classification on the DEAP dataset led to an increase in performances with an accuracy of 75.10% and an AF1 score of 74.92%. CONCLUSION: The results of this study suggest that effective discrimination between flow and non-flow states is possible with multimodal sensor data. The success of transfer learning using the DEAP emotion dataset as a source domain indicates that emotions and flow are connected, and emotion recognition can be used as a latent task to enhance the performance of flow recognition.

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks.

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93-0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.

Automated Motor Tic Detection: A Machine Learning Approach.

BACKGROUND: Video-based tic detection and scoring is useful to independently and objectively assess tic frequency and severity in patients with Tourette syndrome. In trained raters, interrater reliability is good. However, video ratings are time-consuming and cumbersome, particularly in large-scale studies. Therefore, we developed two machine learning (ML) algorithms for automatic tic detection. OBJECTIVE: The aim of this study was to evaluate the performances of state-of-the-art ML approaches for automatic video-based tic detection in patients with Tourette syndrome. METHODS: We used 64 videos of n = 35 patients with Tourette syndrome. The data of six subjects (15 videos with ratings) were used as a validation set for hyperparameter optimization. For the binary classification task to distinguish between tic and no-tic segments, we established two different supervised learning approaches. First, we manually extracted features based on landmarks, which served as input for a Random Forest classifier (Random Forest). Second, a fully automated deep learning approach was used, where regions of interest in video snippets were input to a convolutional neural network (deep neural network). RESULTS: Tic detection F1 scores (and accuracy) were 82.0% (88.4%) in the Random Forest and 79.5% (88.5%) in the deep neural network approach. CONCLUSIONS: ML algorithms for automatic tic detection based on video recordings are feasible and reliable and could thus become a valuable assessment tool, for example, for objective tic measurements in clinical trials. ML algorithms might also be useful for the differential diagnosis of tics. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition.

Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been proposed to replace the previous gold standard with an automated and objective assessment. While the accuracy of such models could be increased incrementally, the understandability and transparency of these systems have not been the main focus of the research community thus far. Thus, in this work, several outcomes and insights of explainable artificial intelligence applied to the electrodermal activity sensor data of the PainMonit and BioVid Heat Pain Database are presented. For this purpose, the importance of hand-crafted features is evaluated using recursive feature elimination based on impurity scores in Random Forest (RF) models. Additionally, Gradient-weighted class activation mapping is applied to highlight the most impactful features learned by deep learning models. Our studies highlight the following insights: (1) Very simple hand-crafted features can yield comparative performances to deep learning models for pain recognition, especially when properly selected with recursive feature elimination. Thus, the use of complex neural networks should be questioned in pain recognition, especially considering their computational costs; and (2) both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data to make their decision in the context of automated pain recognition.

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors.

The perception of hunger and satiety is of great importance to maintaining a healthy body weight and avoiding chronic diseases such as obesity, underweight, or deficiency syndromes due to malnutrition. There are a number of disease patterns, characterized by a chronic loss of this perception. To our best knowledge, hunger and satiety cannot be classified using non-invasive measurements. Aiming to develop an objective classification system, this paper presents a multimodal sensory system using associated signal processing and pattern recognition methods for hunger and satiety detection based on non-invasive monitoring. We used an Empatica E4 smartwatch, a RespiBan wearable device, and JINS MEME smart glasses to capture physiological signals from five healthy normal weight subjects inactively sitting on a chair in a state of hunger and satiety. After pre-processing the signals, we compared different feature extraction approaches, either based on manual feature engineering or deep feature learning. Comparative experiments were carried out to determine the most appropriate sensor channel, device, and classifier to reliably discriminate between hunger and satiety states. Our experiments showed that the most discriminative features come from three specific sensor modalities: Electrodermal Activity (EDA), infrared Thermopile (Tmp), and Blood Volume Pulse (BVP).

A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars.

Identifying accident patterns is one of the most vital research foci of driving analysis. Environmental or safety applications and the growing area of fleet management all benefit from accident detection contributions by minimizing the risk vehicles and drivers are subject to, improving their service and reducing overhead costs. Some solutions have been proposed in the past literature for automated accident detection that are mainly based on traffic data or external sensors. However, traffic data can be difficult to access, while external sensors can end up being difficult to set up and unreliable, depending on how they are used. Additionally, the scarcity of accident detection data has limited the type of approaches used in the past, leaving in particular, machine learning (ML) relatively unexplored. Thus, in this paper, we propose a ML framework for automated car accident detection based on mutimodal in-car sensors. Our work is a unique and innovative study on detecting real-world driving accidents by applying state-of-the-art feature extraction methods using basic sensors in cars. In total, five different feature extraction approaches, including techniques based on feature engineering and feature learning with deep learning are evaluated on the strategic highway research program (SHRP2) naturalistic driving study (NDS) crash data set. The main observations of this study are as follows: (1) CNN features with a SVM classifier obtain very promising results, outperforming all other tested approaches. (2) Feature engineering and feature learning approaches were finding different best performing features. Therefore, our fusion experiment indicates that these two feature sets can be efficiently combined. (3) Unsupervised feature extraction remarkably achieves a notable performance score.

Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition.

While even the most common definition of pain is under debate, pain assessment has remained the same for decades. But the paramount importance of precise pain management for successful healthcare has encouraged initiatives to improve the way pain is assessed. Recent approaches have proposed automatic pain evaluation systems using machine learning models trained with data coming from behavioural or physiological sensors. Although yielding promising results, machine learning studies for sensor-based pain recognition remain scattered and not necessarily easy to compare to each other. In particular, the important process of extracting features is usually optimised towards specific datasets. We thus introduce a comparison of feature extraction methods for pain recognition based on physiological sensors in this paper. In addition, the PainMonit Database (PMDB), a new dataset including both objective and subjective annotations for heat-induced pain in 52 subjects, is introduced. In total, five different approaches including techniques based on feature engineering and feature learning with deep learning are evaluated on the BioVid and PMDB datasets. Our studies highlight the following insights: (1) Simple feature engineering approaches can still compete with deep learning approaches in terms of performance. (2) More complex deep learning architectures do not yield better performance compared to simpler ones. (3) Subjective self-reports by subjects can be used instead of objective temperature-based annotations to build a robust pain recognition system.

Sleep stage classification for child patients using DeConvolutional Neural Network.

Studies from the literature show that the prevalence of sleep disorder in children is far higher than that in adults. Although much research effort has been made on sleep stage classification for adults, children have significantly different characteristics of sleep stages. Therefore, there is an urgent need for sleep stage classification targeting children in particular. Our method focuses on two issues: The first is timestamp-based segmentation (TSS) to deal with the fine-grained annotation of sleep stage labels for each timestamp. Compared to this, popular sliding window approaches unnecessarily aggregate such labels into coarse-grained ones. We utilize DeConvolutional Neural Network (DCNN) that inversely maps features of a hidden layer back to the input space to predict the sleep stage label at each timestamp. Thus, our DCNN can yield better classification performances by considering labels at numerous timestamps. The second issue is the necessity of multiple channels. Different clinical signs, symptoms or other auxiliary examinations could be represented by different Polysomnography (PSG) recordings, so all of them should be analyzed comprehensively. We therefor exploit multivariate time-series of PSG recordings, including 6 electroencephalograms (EEGs) channels, 2 electrooculograms (EOGs) channels (left and right), 1 electromyogram (chin EMG) channel and two leg electromyogram channels. Our DCNN-based method is tested on our SDCP dataset collected from child patients aged from 5 to 10 years old. The results show that our method yields the overall classification accuracy of 84.27% and macro F1-score of 72.51% which are higher than those of existing sliding window-based methods. One of the biggest advantages of our DCNN-based method is that it processes raw PSG recordings and internally extracts features useful for accurate sleep stage classification. We examine whether this is applicable for sleep data of adult patients by testing our method on a well-known public dataset Sleep-EDFX. Our method achieves the average overall accuracy of 90.89% which is comparable to those of state-of-the-art methods without using any hand-crafted features. This result indicates the great potential of our method because it can be generally used for timestamp-level classification on multivariate time-series in various medical fields. Additionally, we provide source codes so that researchers can reproduce the results in this paper and extend our method.

Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification.

The scarcity of labelled time-series data can hinder a proper training of deep learning models. This is especially relevant for the growing field of ubiquitous computing, where data coming from wearable devices have to be analysed using pattern recognition techniques to provide meaningful applications. To address this problem, we propose a transfer learning method based on attributing sensor modality labels to a large amount of time-series data collected from various application fields. Using these data, our method firstly trains a Deep Neural Network (DNN) that can learn general characteristics of time-series data, then transfers it to another DNN designed to solve a specific target problem. In addition, we propose a general architecture that can adapt the transferred DNN regardless of the sensors used in the target field making our approach in particular suitable for multichannel data. We test our method for two ubiquitous computing problems-Human Activity Recognition (HAR) and Emotion Recognition (ER)-and compare it a baseline training the DNN without using transfer learning. For HAR, we also introduce a new dataset, Cognitive Village-MSBand (CogAge), which contains data for 61 atomic activities acquired from three wearable devices (smartphone, smartwatch, and smartglasses). Our results show that our transfer learning approach outperforms the baseline for both HAR and ER.

Rank Pooling Approach for Wearable Sensor-Based ADLs Recognition.

This paper addresses wearable-based recognition of Activities of Daily Living (ADLs) which are composed of several repetitive and concurrent short movements having temporal dependencies. It is improbable to directly use sensor data to recognize these long-term composite activities because two examples (data sequences) of the same ADL result in largely diverse sensory data. However, they may be similar in terms of more semantic and meaningful short-term atomic actions. Therefore, we propose a two-level hierarchical model for recognition of ADLs. Firstly, atomic activities are detected and their probabilistic scores are generated at the lower level. Secondly, we deal with the temporal transitions of atomic activities using a temporal pooling method, rank pooling. This enables us to encode the ordering of probabilistic scores for atomic activities at the higher level of our model. Rank pooling leads to a 5-13% improvement in results as compared to the other popularly used techniques. We also produce a large dataset of 61 atomic and 7 composite activities for our experiments.

Extracellular vesicles as an emerging tool for the early detection of Alzheimer's disease.

Alzheimer's disease (AD) is characterized by a series of interacting pathophysiological cascades, including the aggregation of ß-amyloid plaques and the formation of neurofibrillary tangles derived from hyperphosphorylated tau proteins. AD is the cause of approximately 70 % of dementia, an irreversible and untreatable syndrome at its late stage. Hence, more efforts should be devoted to identifying at-risk or preclinical AD populations for early intervention and the improved design of drug trials. The exosome, a nanoscale subtype of extracellular vesicle that serves as a cell-to-cell communication messenger, is an emerging liquid biopsy tool for various diseases including AD. Recently, it has been discovered that brain-derived exosomes can flow through the blood-brain barrier to the peripheral blood, containing important protein and nucleic acid biomarkers that are associated with the pathogenesis and progression of AD. Other reports showed a strong involvement of exosomes in synaptic function, insulin resistance, and neuroinflammation, among others. Here, we summarize those studies and assess the value of exosomes as an emerging tool for the early detection of AD in conjunction with the current clinical diagnosis paradigm.

Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors.

Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.

The transcription cofactor c-JUN mediates phenotype switching and BRAF inhibitor resistance in melanoma.

Most patients with BRAF-mutant metastatic melanoma display remarkable but incomplete and short-lived responses to inhibitors of the BRAF kinase or the mitogen-activated protein kinase kinase (MEK), collectively BRAF/MEK inhibitors. We found that inherent resistance to these agents in BRAF(V600)-mutant melanoma cell lines was associated with high abundance of c-JUN and characteristics of a mesenchymal-like phenotype. Early drug adaptation in drug-sensitive cell lines grown in culture or as xenografts, and in patient samples during therapy, was consistently characterized by down-regulation of SPROUTY4 (a negative feedback regulator of receptor tyrosine kinases and the BRAF-MEK signaling pathway), increased expression of JUN and reduced expression of LEF1. This coincided with a switch in phenotype that resembled an epithelial-mesenchymal transition (EMT). In cultured cells, these BRAF inhibitor-induced changes were reversed upon removal of the drug. Knockdown of SPROUTY4 was sufficient to increase the abundance of c-JUN in the absence of drug treatment. Overexpressing c-JUN in drug-naïve melanoma cells induced similar EMT-like phenotypic changes to BRAF inhibitor treatment, whereas knocking down JUN abrogated the BRAF inhibitor-induced early adaptive changes associated with resistance and enhanced cell death. Combining the BRAF inhibitor with an inhibitor of c-JUN amino-terminal kinase (JNK) reduced c-JUN phosphorylation, decreased cell migration, and increased cell death in melanoma cells. Gene expression data from a panel of melanoma cell lines and a patient cohort showed that JUN expression correlated with a mesenchymal gene signature, implicating c-JUN as a key mediator of the mesenchymal-like phenotype associated with drug resistance.

Phenotype switching in melanoma: implications for progression and therapy.

Epithelial-mesenchymal transition (EMT) is a key process associated with the progression of epithelial cancers to metastatic disease. In melanoma, a similar process of phenotype switching has been reported and EMT-related genes have been implicated in promotion to a metastatic state. This review examines recent research on the role of signaling pathways and transcription factors regulating EMT-like processes in melanoma and their association with response to therapy in patients, especially response to BRAF inhibition, which is initially effective but limited by development of resistance and subsequent progression. We highlight studies implicating specific roles of various receptor tyrosine kinases (RTKs) in advancing melanoma progression by conferring a proliferative advantage and through promoting invasive phenotypes and metastasis. We also review the current knowledge of the mechanisms underlying resistance to BRAF inhibition and the potential role of melanoma phenotype switching in this process. In particular, we discuss how these important new insights may significantly enhance our ability to predict patterns of melanoma progression during treatment, and may facilitate rational development of combination therapies in the future.

Study design and cohort characteristics of the Childhood Cancer Survivor Study: a multi-institutional collaborative project.

BACKGROUND: Increased attention has been directed toward the long-term health outcomes of survivors of childhood cancer. To facilitate such research, a multi-institutional consortium established the Childhood Cancer Survivor Study (CCSS), a large, diverse, and well-characterized cohort of 5-year survivors of childhood and adolescent cancer. PROCEDURE: Eligibility for the CCSS cohort included a selected group of cancer diagnoses prior to age 21 years between 1970-1986 and survival for at least 5 years. RESULTS: A total of 20,276 eligible subjects were identified from the 25 contributing institutions, of whom 15% are considered lost to follow-up. Currently, 14,054 subjects (69.3% of the eligible cohort) have participated by completing a 24-page baseline questionnaire. The distribution of first diagnoses includes leukemia (33%), lymphoma (21%), neuroblastoma (7%), CNS tumor (13%), bone tumor (8%), kidney tumor (9%), and soft-tissue sarcoma (9%). Abstraction of medical records for chemotherapy, radiation therapy, and surgical procedures has been successfully completed for 98% of study participants. Overall, 78% received radiotherapy and 73% chemotherapy. CONCLUSION: The CCSS represents the largest and most extensively characterized cohort of childhood and adolescent cancer survivors in North America. It serves as a resource for addressing important issues such as risk of second malignancies, endocrine and reproductive outcome, cardiopulmonary complications, and psychosocial implications, among this unique and ever-growing population.