Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains a global model by aggregating local models from hospitals. Existing FL techniques adopt a central server-based network topology, where the server assembles the local models trained in each hospital to create a global model. However, the server could be a point of failure, and models trained in FL usually have worse performance than those trained in the centralized learning manner when the patient's data are not independent and identically distributed (Non-IID) in the hospitals. This paper presents a decentralized FL framework, including training with adaptive ensemble learning and a deployment phase using knowledge distillation. The adaptive ensemble learning step in the training phase leads to the acquisition of a specific model for each hospital that is the optimal combination of local models and models from other available hospitals. This step solves the non-IID challenges in each hospital. The deployment phase adjusts the model's complexity to meet the resource constraints of wearable systems. We evaluated the performance of our approach on edge computing platforms using EPILEPSIAE and TUSZ databases, which are public epilepsy datasets.RYC2021-032853-

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

Preserving privacy in edge computing

Edge computing or fog computing enables realtime services to smart application users by storing data and services at the edge of the networks. Edge devices in the edge computing handle data storage and service provisioning. Therefore, edge computing has become a  new norm for several delay-sensitive smart applications such as automated vehicles, ambient-assisted living, emergency response services, precision agriculture, and smart electricity grids. Despite having great potential, privacy threats are the main barriers to the success of edge computing. Attackers can leak private or sensitive information of data owners and modify service-related data for hampering service provisioning in edge computing-based smart applications. This research takes privacy issues of heterogeneous smart application data into account that are stored in edge data centers. From there, this study focuses on the development of privacy-preserving models for user-generated smart application data in edge computing and edge service-related data, such as Quality-of-Service (QoS) data, for ensuring unbiased service provisioning. We begin with developing privacy-preserving techniques for user data generated by smart applications using steganography that is one of the data hiding techniques. In steganography, user sensitive information is hidden within nonsensitive information of data before outsourcing smart application data, and stego data are produced for storing in the edge data center. A steganography approach must be reversible or lossless to be useful in privacy-preserving techniques. In this research, we focus on numerical (sensor data) and textual (DNA sequence and text) data steganography. Existing steganography approaches for numerical data are irreversible. Hence, we introduce a lossless or reversible numerical data steganography approach using Error Correcting Codes (ECC). Modern lossless steganography approaches for text data steganography are mainly application-specific and lacks imperceptibility, and DNA steganography requires reference DNA sequence for the reconstruction of the original DNA sequence. Therefore, we present the first blind and lossless DNA sequence steganography approach based on the nucleotide substitution method in this study. In addition, a text steganography method is proposed that using invisible character and compression based encoding for ensuring reversibility and higher imperceptibility.  Different experiments are conducted to demonstrate the justification of our proposed methods in these studies. The searching capability of the stored stego data is challenged in the edge data center without disclosing sensitive information. We present a privacy-preserving search framework for stego data on the edge data center that includes two methods. In the first method, we present a keyword-based privacy-preserving search method that allows a user to send a search query as a hash string. However, this method does not support the range query. Therefore, we develop a range search method on stego data using an order-preserving encryption (OPE) scheme. In both cases, the search service provider retrieves corresponding stego data without revealing any sensitive information. Several experiments are conducted for evaluating the performance of the framework. Finally, we present a privacy-preserving service computation framework using Fully Homomorphic Encryption (FHE) based cryptosystem for ensuring the service provider's privacy during service selection and composition. Our contributions are two folds. First, we introduce a privacy-preserving service selection model based on encrypted Quality-of-Service (QoS) values of edge services for ensuring privacy. QoS values are encrypted using FHE. A distributed computation model for service selection using MapReduce is designed for improving efficiency. Second, we develop a composition model for edge services based on the functional relationship among edge services for optimizing the service selection process. Various experiments are performed in both centralized and distributed computing environments to evaluate the performance of the proposed framework using a synthetic QoS dataset

A Biomechanical and Physiological Signal Monitoring System for Four Degrees of Upper Limb Movement

A lack of adherence to prescribed physical therapy regimens in improper healing results in poor outcomes for those affected by musculoskeletal disorders (MSDs) of the upper limb. Societal and psychological barriers to proper adherence can be addressed through the system presented in this work consisting of the following components: an ambulatory biosignal acquisition sleeve, an electromyography (EMG) based motion repetition detection algorithm, and the design of a compatible capacitive EMG acquisition module. The biosignal acquisition sleeve was untethered, unobtrusive to motion, contained only modular components, and collected biomechanical and physiological sensor data to form full motion profiles of the following four degrees of freedom: elbow flexion—extension, forearm pronation—supination, wrist flexion—extension, and ulnar--radial deviation. The piloted sleeve simultaneously collected data from four inertial sensors, two electromyography (EMG) sensors and a flex-bend sensor. A visualization application was developed to present the information in a manner meaningful to the user. As well, an EMG based motion repetition detector was developed for use within the system. It was validated using an existing database of 23 subjects with varying musculoskeletal health, achieving a success rate of 95.43%. This algorithm was modified for use with the sleeve, resulting in a 95% success rate. An electrode and analog front end module was proposed, relying on unique material structures and low-noise, precision sensing techniques. The system prototype presented a resource-conscious tool for multi-modality tracking of elbow, forearm, and wrist motion, which could eventually be integrated into upper limb MSD rehabilitation

Effects of Isometric Handgrip Training in Patients With Peripheral Artery Disease: A Randomized Controlled Trial

BackgroundMeta‐analyses have shown that isometric handgrip training (IHT) can reduce brachial systolic and diastolic blood pressure (BP) by >6/4 mm Hg, respectively. However, whether IHT promotes these effects among patients with peripheral artery disease, who exhibit severe impairment in cardiovascular function, is currently unknown. This study aimed to evaluate the effects of IHT on the cardiovascular function of patients with peripheral artery disease. Methods and ResultsA randomized controlled trial with peripheral artery disease patients assigned to either the IHT or control group was conducted. The IHT group performed 3 sessions per week, for 8 weeks, of unilateral handgrip exercises, consisting of 4 sets of isometric contractions for 2 minutes at 30% of maximum voluntary contraction and a 4‐minute interval between sets. The control group received a compression ball in order to minimize the placebo effects, representing sham training. The primary outcome was brachial BP. The secondary outcomes were central BP, arterial stiffness parameters, cardiac autonomic modulation, and vascular function. The IHT program reduced diastolic BP (75 [10] mm Hg preintervention versus 72 [11] mm Hg postintervention), with no change in the control group (74 [11] mm Hg preintervention versus 74 [11] mm Hg postintervention), with this between‐group difference being significant (P=0.04). Flow‐mediated dilation improved in the IHT group (6.0% [5.7] preintervention versus 9.7% [5.5] postintervention), with no change in the control group (7.6% [5.5] preintervention versus 7.4% [5.1] postintervention), with this between‐group difference being significant (P=0.04). There was no change in other measured variables over the intervention period. ConclusionsIHT reduced brachial diastolic BP and improved local vascular function in patients with peripheral artery disease

From conformons to human brains: an informal overview of nonlinear dynamics and its applications in biomedicine

Methods of contemporary physics are increasingly important for biomedical research but, for a multitude of diverse reasons, most practitioners of biomedicine lack access to a comprehensive knowledge of these modern methodologies. This paper is an attempt to describe nonlinear dynamics and its methods in a way that could be read and understood by biomedical professionals who usually are not trained in advanced mathematics. After an overview of basic concepts and vocabulary of nonlinear dynamics, deterministic chaos, and fractals, application of nonlinear methods of biosignal analysis is discussed. In particular, five case studies are presented: 1. Monitoring the depth of anaesthesia and of sedation; 2. Bright Light Therapy and Seasonal Affective Disorder; 3. Analysis of posturographic signals; 4. Evoked EEG and photo-stimulation; 5. Influence of electromagnetic fields generated by cellular phones

Quantitative Ventricular Fibrillation Metrics in a Biosignal Guided Cardiopulmonary Resuscitation Device for Cardiac Arrest and Their Translation to Clinical Data

Out of hospital cardiac arrest is a major cause of mortality with an estimated yearly incidence of 350,000 in the United States alone. Cardiopulmonary resuscitation (CPR) is a treatment for cardiac arrest involving chest compressions and rescues breaths that can save lives but is limited by the fact that it currently treats all patients in a 'one size fits all' approach. This work describes an adaptive approach to chest compressions controlled by a mechanical device that receives biosignals from the patient it treats. The device is capable of adjusting its chest compression parameters such as rate and depth in response to the biosignals it receives. We focused on integrating the quantitative electrocardiogram (QECG) of the ventricular fibrillation signal, a biosignal shown to respond to increased perfusion of the myocardium during CPR, into a chest compression algorithm controlled by the adaptive chest compression device. QECG is readily available for cardiac arrest patients since ECG analysis is standard of care in cardiac arrest. In our first aim we developed the adaptive chest compression device and tested it in animal feasibility studies which demonstrated that it responded appropriately to the biosignals it received. Next, in a computational model of adaptive chest compressions, adjustments in chest compression depth yielded the largest increase in cardiac output in patients with simulated variable physiology. In follow-up animal studies, select QECG measures responded to changes in chest compression parameters which demonstrated the initial feasibility of QECG measures as a potential biosignal in this model. We found that the QECG measures of median slope, centroid frequency, and log of the absolute correlation responded to changes in chest compression rate in the early phase of chest compressions. We found that in late phases of chest compressions the QECG measure median slope responded to chest compression rate changes and the QECG measure AMSA responded to chest compression duty cycle changes. Our second aim sought to retrospectively translate the findings in the first aim animal studies to human clinical data in the continuous chest compression trial of the Resuscitation Outcomes Consortium (ROC). The clinical trial provided us with ECG and compression data in covering thousands of cardiac arrest events. All QECG metrics in the clinical data set was predictive of shock outcome and chest compression rate along with chest compression bout duration were predictive of survival. However, when controlled for the presenting first rhythm status and demographic variables, only chest compression bout duration was predictive of survival. In addition to the predictive value of chest compression parameters and QECG measures, associations were found between varying chest compression parameters averaged across bouts of compressions with change in QECG values (dQECG) in the clinical data. Chest compression rate was found to be predictive of the dQECG metric median slope (dMS) and the dQECG metric (dAMSA). Dosed compression rate was found to be predictive of the dQECG metric dMS as well. dCF responded to changes in chest compression duty cycle. These findings provide a foundation for delivering adaptive chest compressions with the potential of improving survival outcomes to cardiac arrest

Design and Initial Analysis of a Wearable Device for Anxiety Management

Deep pressure therapy (DPT) is a method of anxiety relief that uses pressure sensations to lower the stress response produced in the brain, and functions in a manner similar to a hug. Heavily weighted blankets and weighted vests are most commonly used to apply DPT, though recent work has shown that manually-inflated vests can produce similar effects. However, these devices are limited in their portability and usability by their weight or extended delay in activation. In this work, we introduce the Automatic Inflatable DPT (AID) Vest as an automated alternative to existing DPT devices. To inform the design of the AID Vest, we held discussions with an expert in the field and began a focus group study, which is currently ongoing. From the expert discussions, we identified design requirements for the AID Vest and for its improvement in a future empirical study. We then go on to design, fabricate, and conduct an initial evaluation of the AID Vest. Preliminary results show that the proposed device can successfullyproduce a compression effect using soft actuators, that our proposed procedure for inducing and measuring anxiety are viable for a future empirical study, and that a user wearing the AID Vest may experience greater anxiety reduction than a user wearing an unmodified commercial vest