A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.

We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.

Federated Learning in Smart City Sensing: Challenges and Opportunities.

Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city services. The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale. These valuable data can be leveraged to train advanced Artificial Intelligence (AI) models that serve various smart services that benefit society in all aspects. Despite their effectiveness, legacy data acquisition models backed with centralized Machine Learning models entail security and privacy concerns, and lead to less participation in large-scale sensing and data provision for smart city services. To overcome these challenges, Federated Learning is a novel concept that can serve as a solution to the privacy and security issues encountered within the process of data collection. This survey article presents an overview of smart city sensing and its current challenges followed by the potential of Federated Learning in addressing those challenges. A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights on open issues, challenges, and opportunities in this field are provided as guidance for the researchers studying this subject matter.

A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective.

In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.

Home use of a compact, 12­lead ECG recording system for newborns.

BACKGROUND: An easy-to-operate ECG recorder should be useful for newborn screening for heart conditions, by health care workers - or parents. We developed a one-piece electrode strip and a compact, 12­lead ECG recorder for newborns. METHOD: We enrolled 2582 newborns in a trial to assess abilities of parents to record a 12­lead ECG on their infants (2-4 weeks-old). Newborns were randomized to recordings by parents (1290) or our staff (1292 controls). Educational backgrounds of parents varied, including 64% with no more than a high school diploma. RESULTS: For newborns randomized to parent recorded ECGs, 94% of parents completed a 10-minute recording. However, 42.6% asked for verbal help, and 12.7% needed physical help. ECG quality was the same for recordings by parents versus staff. CONCLUSIONS: By use of a one-piece electrode strip and a compact recorder, 87% of parents recorded diagnostic quality ECGs on their newborn infants, with minimal assistance.

Emerging Security Mechanisms for Medical Cyber Physical Systems.

The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These Medical Cyber Physical Systems (MCPS) will be capable of transmitting the acquired data to a private or public cloud for storage and processing. Machine learning algorithms running in the cloud and processing this data can provide decision support to healthcare professionals. There is no doubt that the security and privacy of the medical data is one of the most important concerns in designing an MCPS. In this paper, we depict the general architecture of an MCPS consisting of four layers: data acquisition, data aggregation, cloud processing, and action. Due to the differences in hardware and communication capabilities of each layer, different encryption schemes must be used to guarantee data privacy within that layer. We survey conventional and emerging encryption schemes based on their ability to provide secure storage, data sharing, and secure computation. Our detailed experimental evaluation of each scheme shows that while the emerging encryption schemes enable exciting new features such as secure sharing and secure computation, they introduce several orders-of-magnitude computational and storage overhead. We conclude our paper by outlining future research directions to improve the usability of the emerging encryption schemes in an MCPS.

"QT clock" to improve detection of QT prolongation in long QT syndrome patients.

BACKGROUND: The QT interval is a risk marker for cardiac events such as torsades de pointes. However, QT measurements obtained from a 12-lead ECG during clinic hours may not capture the full extent of a patient's daily QT range. OBJECTIVE: The purpose of this study was to evaluate the utility of 24-hour Holter ECG recording in patients with long QT syndrome (LQTS) to identify dynamic changes in the heart rate-corrected QT interval and to investigate methods of visualizing the resulting datasets. METHODS: Beat-to-beat QTc (Bazett) intervals were automatically measured across 24-hour Holter recordings from 202 LQTS type 1, 89 type 2, and 14 type 3 genotyped patients and a reference group of 200 healthy individuals. We measured the percentage of beats with QTc greater than the gender-specific threshold (QTc ≥470 ms in women and QTc ≥450 ms in men). The percentage of beats with QTc prolongation was determined across the 24-hour recordings. RESULTS: Based on the median percentage of heartbeats per patient with QTc prolongation, LQTS type 1 patients showed more frequent QTc prolongation during the day (~3 PM) than they did at night (~3 AM): 97% vs 48%, P ~10(-4) for men, and 68% vs 30%, P ~10(-5) for women. LQTS type 2 patients showed less frequent QTc prolongation during the day compared to nighttime: 87% vs 100%, P ~10(-4) for men, and 62% vs 100%, P ~10(-3) for women. CONCLUSION: In patients with genotype-positive LQTS, significant differences exist in the degree of daytime and nocturnal QTc prolongation. Holter monitoring using the "QT clock" concept may provide an easy, fast, and accurate method for assessing the true personalized burden of QTc prolongation.

Cloud-based privacy-preserving remote ECG monitoring and surveillance.

BACKGROUND: The number of technical solutions for monitoring patients in their daily activities is expected to increase significantly in the near future. Blood pressure, heart rate, temperature, BMI, oxygen saturation, and electrolytes are few of the physiologic factors that will soon be available to patients and their physicians almost continuously. The availability and transfer of this information from the patient to the health provider raises privacy concerns. Moreover, current data encryption approaches expose patient data during processing, therefore restricting their utility in applications requiring data analysis. METHODS: We propose a system that couples health monitoring techniques with analytic methods to permit the extraction of relevant information from patient data without compromising privacy. This proposal is based on the concept of fully homomorphic encryption (FHE). Since this technique is known to be resource-heavy, we develop a proof-of-concept to assess its practicality. Results are presented from our prototype system, which mimics live QT monitoring and detection of drug-induced QT prolongation. RESULTS: Transferring FHE-encrypted QT and RR samples requires about 2 Mbps of network bandwidth per patient. Comparing FHE-encrypted values--for example, comparing QTc to a given threshold-runs quickly enough on modest hardware to alert the doctor of important results in real-time. CONCLUSIONS: We demonstrate that FHE could be used to securely transfer and analyze ambulatory health monitoring data. We present a unique concept that could represent a disruptive type of technology with broad applications to multiple monitoring devices. Future work will focus on performance optimizations to accelerate expansion to these other applications.