Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models

Deep learning models for scoring sleep stages based on single-channel EEG have been proposed as a promising method for remote sleep monitoring. However, applying these models to new datasets, particularly from wearable devices, raises two questions. First, when annotations on a target dataset are unavailable, which different data characteristics affect the sleep stage scoring performance the most and by how much? Second, when annotations are available, which dataset should be used as the source of transfer learning to optimize performance? In this paper, we propose a novel method for computationally quantifying the impact of different data characteristics on the transferability of deep learning models. Quantification is accomplished by training and evaluating two models with significant architectural differences, TinySleepNet and U-Time, under various transfer configurations in which the source and target datasets have different recording channels, recording environments, and subject conditions. For the first question, the environment had the highest impact on sleep stage scoring performance, with performance degrading by over 14% when sleep annotations were unavailable. For the second question, the most useful transfer sources for TinySleepNet and the U-Time models were MASS-SS1 and ISRUC-SG1, containing a high percentage of N1 (the rarest sleep stage) relative to the others. The frontal and central EEGs were preferred for TinySleepNet. The proposed approach enables full utilization of existing sleep datasets for training and planning model transfer to maximize the sleep stage scoring performance on a target problem when sleep annotations are limited or unavailable, supporting the realization of remote sleep monitoring

TensorLayer: A Versatile Library for Efficient Deep Learning Development

Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others. Developing a deep learning system is arduous and complex, as it involves constructing neural network architectures, managing training/trained models, tuning optimization process, preprocessing and organizing data, etc. TensorLayer is a versatile Python library that aims at helping researchers and engineers efficiently develop deep learning systems. It offers rich abstractions for neural networks, model and data management, and parallel workflow mechanism. While boosting efficiency, TensorLayer maintains both performance and scalability. TensorLayer was released in September 2016 on GitHub, and has helped people from academia and industry develop real-world applications of deep learning.Comment: ACM Multimedia 201

Towards Desynchronization Detection in Biosignals

This study presents a novel data-driven approach to detect desynchronization among biosignals from two modalities. We propose to train a deep neural network to learn synchronized patterns between biosignals from two modalities by transcribing signals from one modality into their expected, simultaneous or synchronized signal in another modality. Thus, instead of measuring the degree of synchrony between signals from different modalities using traditional linear and non-linear measures, we simplify this problem into the problem of measuring the degree of synchrony between the real and the synthesized signals from the same modality using the traditional measures. Desynchronization detection is then achieved by applying a threshold function to the estimated degree of synchrony. We demonstrate the approach with the detection of eye-movement artifacts in a public sleep dataset and compare the detection performance with traditional approaches

Learning from biosignals

A long-standing goal of the biosignal analysis is to develop tools that can continuously collect quality biosignals, and algorithms to extract meaningful information from them. This can lead to a better understanding of our health that allows us to adjust our daily activity to be suitable for our well-being, and provide treatments promptly. However, the conventional approaches to analyze biosignals rely on the development of algorithms to extract features from the signals (i.e., hand-engineering features). It can be labor-intensive and time-consuming to develop algorithms to extract such features for particular applications repeatedly. Also, the existing machine learning based systems assume that there are annotations associated with the particular patterns of biosignals; such annotations are very expensive to obtain. In this thesis, our objective is to develop models that can automatically learn features from biosignals without utilizing any hand-engineering features and can extract meaningful information from biosignals even when the labels of biosignals are not available. Our first contribution is that we propose a method that can remotely and accurately estimate walking speeds for people with walking impairments. This work also motivates us to use deep learning to automate the expensive feature engineering process in the remaining contributions. Our second contribution is that we propose a model that can automatically extract meaningful features from raw scalp EEG signals for epileptic seizure detection. We demonstrate that it can detect seizures without utilizing any seizure annotations. Our third contribution is that we propose a model that can automatically learn features that are useful for sleep stage scoring from raw single-channel EEG data, and achieved similar performance compared to the state-of-the-art hand-engineering ones. We also demonstrated that our model can generalize to two sleep datasets that have different properties without any modifications to the model architecture. Our final contribution is that we propose a novel data-driven approach that employs a signal transcription model to capture the relationships between signals from multiple domains to detect desynchronized biosignals as anomalies without utilizing any annotations.Open Acces

Towards Desynchronization Detection in Biosignals

This study presents a novel data-driven approach to detect desynchronization among biosignals from two modalities. We propose to train a deep neural network to learn synchronized patterns between biosignals from two modalities by transcribing signals from one modality into their expected, simultaneous or synchronized signal in another modality. Thus, instead of measuring the degree of synchrony between signals from different modalities using traditional linear and non-linear measures, we simplify this problem into the problem of measuring the degree of synchrony between the real and the synthesized signals from the same modality using the traditional measures. Desynchronization detection is then achieved by applying a threshold function to the estimated degree of synchrony. We demonstrate the approach with the detection of eye-movement artifacts in a public sleep dataset and compare the detection performance with traditional approaches

Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis

Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment. Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking. Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm. Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10−22). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10−8). Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time

Using Support Vector Machine on EEG for Advertisement Impact Assessment

The advertising industry depends on an effective assessment of the impact of advertising as a key performance metric for their products. However, current assessment methods have relied on either indirect inference from observing changes in consumer behavior after the launch of an advertising campaign, which has long cycle times and requires an ad campaign to have already have been launched (often meaning costs having been sunk). Or through surveys or focus groups, which have a potential for experimental biases, peer pressure, and other psychological and sociological phenomena that can reduce the effectiveness of the study. In this paper, we investigate a new approach to assess the impact of advertisement by utilizing low-cost EEG headbands to record and assess the measurable impact of advertising on the brain. Our evaluation shows the desired performance of our method based on user experiment with 30 recruited subjects after watching 220 different advertisements. We believe the proposed SVM method can be further developed to a general and scalable methodology that can enable advertising agencies to assess impact rapidly, quantitatively, and without bias

Data_Sheet_1_Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis.docx

<p>Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment.</p><p>Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking.</p><p>Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm.</p><p>Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10⁻²²). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10⁻⁸).</p><p>Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.</p

Image_3_Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis.JPEG

<p>Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment.</p><p>Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking.</p><p>Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm.</p><p>Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10⁻²²). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10⁻⁸).</p><p>Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.</p