Lightweight Machine Learning for Seizure Detection on Wearable Devices

For patients with epilepsy, automatic epilepsy monitoring, i.e., the process of direct observation of the patient’s health status in real time, is crucial. Wearable systems provide the possibility of real-time epilepsy monitoring and alerting caregivers upon the occurrence of a seizure. In the context of the ICASSP 2023 Seizure Detection Challenge, we pro- pose a lightweight machine-learning framework for real-time epilepsy monitoring on wearable devices. We evaluate our proposed framework on the SeizeIT2 dataset from the wear- able SensorDot (SD) of Byteflies. The experimental results show that our proposed framework achieves a sensitivity of 73.6% and a specificity of 96.7% in seizure detection

DP-ACT: Decentralized Privacy-Preserving Asymmetric Digital Contact Tracing

Digital contact tracing substantially improves the identification of high-risk contacts during pandemics. Despite several attempts to encourage people to use digital contact-tracing applications by developing and rolling out decentralized privacy-preserving protocols (broadcasting pseudo-random IDs over Bluetooth Low Energy-BLE), the adoption of digital contact tracing mobile applications has been limited, with privacy being one of the main concerns.In this paper, we propose a decentralized privacy-preserving contact tracing protocol, called DP-ACT, with both active and passive participants. Active participants broadcast BLE beacons with pseudo-random IDs, while passive participants model conservative users who do not broadcast BLE beacons but still listen to the broadcasted BLE beacons. We analyze the proposed protocol and discuss a set of interesting properties. The proposed protocol is evaluated using both a face-to-face individual interaction dataset and five real-world BLE datasets. Our simulation results demonstrate that the proposed DP-ACT protocol outperforms the state-of-the-art protocols in the presence of passive users

Anomalies in Scheduling Control Applications and Design Complexity

Today, many control applications in cyberphysical systems are implemented on shared platforms. Such resource sharing may lead to complex timing behaviors and, in turn, instability of control applications. This paper highlights a number of anomalies demonstrating complex timing behaviors caused as a result of resource sharing. Such anomalous scenarios, then, lead to a dramatic increase in design complexity, if not properly considered. Here, we demonstrate that these anomalies are, in fact, very improbable. Therefore, design methodologies for these systems should mainly be devised and tuned towards the majority of cases, as opposed to anomalies, but should also be able to handle such anomalous scenarios

SafeDeep: A Scalable Robustness Verification Framework for Deep Neural Networks

The state-of-the-art machine learning techniques come with limited, if at all any, formal correctness guarantees. This has been demonstrated by adversarial examples in the deep learning domain. To address this challenge, here, we propose a scalable robustness verification framework for Deep Neural Networks (DNNs). The framework relies on Linear Programming (LP) engines and builds on decades of advances in the field for analyzing convex approximations of the original network. The key insight is in the on-demand incremental refinement of these convex approximations. This refinement can be parallelized, making the framework even more scalable. We have implemented a prototype tool to verify the robustness of a large number of DNNs in epileptic seizure detection. We have compared the results with those obtained by two state-of-the-art tools for the verification of DNNs. We show that our framework is consistently more precise than the over-approximation-based tool ERAN and more scalable than the SMT-based tool Reluplex

Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

A considerable portion of government health-care spending is allocated to the continuous monitoring of patients suffering from cardiovascular diseases, particularly myocardial infarction (MI). Wearable devices present a cost-effective means of monitoring patients’ vital signs in ambulatory settings. A major challenge is to design such ultra-low energy devices for long-term patient monitoring. In this paper, we present a real-time event-driven classification technique based on the random forest classification scheme, which uses a confidence-related decision-making process. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. We validate our approach on a well-established and complete MI database (Physiobank, PTB Diagnostic ECG database [1]). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 2.60, with no classification quality loss

Stability-Aware Analysis and Design of Embedded Control Systems

Abstract—Many embedded systems comprise several controllerssharingavailableresources.Itiswellknownthat such resource sharing leads to complex timing behavior that can jeopardize stability of control applications, if it is not properly taken into account in the design process, e.g., mapping and scheduling. As opposed to hard real-time systems where meeting the deadline is a critical requirement, control applications do not enforce hard deadlines. Therefore,thetraditionalreal-timeanalysisapproachesare not readily applicable to control applications. Rather, in the context of control applications, stability is often the main requirement to be guaranteed, and can be expressed as the amount of delay and jitter a control application can tolerate. The nominal delay and response-time jitter can be regarded as the two main factors which relate the real-time aspects of a system to control performance and stability. Therefore, it is important to analyze the impact of variations in scheduling parameters, i.e., period and priority, on the nominal delay and response-time jitter and, ultimately, on stability. Based on such an analysis, we address, in this paper, priority assignment and sensitivity analysis problems for control applications considering stability as the main requirement. I

Optimization of Message Encryption for Real-Time Applications in Embedded Systems

Today, security can no longer be treated as a secondary issue in embedded and cyber-physical systems. Therefore, one of the main challenges in these domains is the design of secure embedded systems under stringent resource constraints and real-time requirements. However, there exists an inherent trade-off between the security protection provided and the amount of resources allocated for this purpose. That is, the more the amount of resources used for security, the higher the security, but the fewer the number of applications which can be run on the platform and meet their timing requirements. This trade-off is of high importance since embedded systems are often highly resource constrained. In this paper, we propose an efficient solution to maximize confidentiality, while also guaranteeing the timing requirements of real-time applications on shared platforms

EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems

Epilepsy is one of the most common neurological disorders that is characterized by recurrent and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure and notify family members and emergency units for rescue. The majority of state-of-the-art studies in the epilepsy domain currently explore modern machine learning techniques, e.g., deep neural networks, to accurately detect epileptic seizures. However, training deep learning networks requires a large amount of data and computing resources, which is a major challenge for resource-constrained wearable systems. In this paper, we propose EpilepsyNet, the first interpretable self-supervised network tailored to resource-constrained devices without using any seizure data in its initial offline training. At runtime, however, once a seizure is detected, it can be incorporated into our self-supervised technique to improve seizure detection performance, without the need to retrain our learning model, hence incurring no energy overheads. Our self-supervised approach can reach a detection performance of 79.2%, which is on par with the state-of-the-art fully-supervised deep neural networks trained on seizure data. At the same time, our proposed approach can be deployed in resource-constrained wearable devices, reaching up to 1.3 days of battery life on a single charge

e-Glass: A Wearable System for Real-Time Detection of Epileptic Seizures

Today, epilepsy is one of the most common chronic diseases affecting more than 65 million people worldwide and is ranked number four after migraine, Alzheimer’s disease, and stroke. Despite the recent advances in anti-epileptic drugs, one-third of the epileptic patients continue to have seizures. More importantly, epilepsy-related causes of death account for 40% of mortality in high-risk patients. However, no reliable wearable device currently exists for real-time epileptic seizure detection. In this paper, we propose e-Glass, a wearable system based on four electroencephalogram (EEG) electrodes for the detection of epileptic seizures. Based on an early warning from e-Glass, it is possible to notify caregivers for rescue to avoid epilepsy-related death due to the underlying neurological disorders, sudden unexpected death in epilepsy, or accidents during seizures. We demonstrate the performance of our system using the Physionet.org CHB-MIT Scalp EEG database for epileptic children. Our experimental evaluation demonstrates that our system reaches a sensitivity of 93.80% and a specificity of 93.37%, allowing for 2.71 days of operation on a single battery charge