Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte

Wireless tools for neuromodulation

Epilepsy is a spectrum of diseases characterized by recurrent seizures. It is estimated that 50 million individuals worldwide are affected and 30% of cases are medically refractory or drug resistant. Vagus nerve stimulation (VNS) and deep brain stimulation (DBS) are the only FDA approved device based therapies. Neither therapy offers complete seizure freedom in a majority of users. Novel methodologies are needed to better understand mechanisms and chronic nature of epilepsy. Most tools for neuromodulation in rodents are tethered. The few wireless devices use batteries or are inductively powered. The tether restricts movement, limits behavioral tests, and increases the risk of infection. Batteries are large and heavy with a limited lifetime. Inductive powering suffers from rapid efficiency drops due to alignment mismatches and increased distances. Miniature wireless tools that offer behavioral freedom, data acquisition, and stimulation are needed. This dissertation presents a platform of electrical, optical and radiofrequency (RF) technologies for device based neuromodulation. The platform can be configured with features including: two channels differential recording, one channel electrical stimulation, and one channel optical stimulation. Typical device operation consumes less than 4 mW. The analog front end has a bandwidth of 0.7 Hz - 1 kHz and a gain of 60 dB, and the constant current driver provides biphasic electrical stimulation. For use with optogenetics, the deep brain optical stimulation module provides 27 mW/mm2 of blue light (473 nm) with 21.01 mA. Pairing of stimulating and recording technologies allows closed-loop operation. A wireless powering cage is designed using the resonantly coupled filter energy transfer (RCFET) methodology. RF energy is coupled through magnetic resonance. The cage has a PTE ranging from 1.8-6.28% for a volume of 11 x 11 x 11 in3. This is sufficient to chronically house subjects. The technologies are validated through various in vivo preparations. The tools are designed to study epilepsy, SUDEP, and urinary incontinence but can be configured for other studies. The broad application of these technologies can enable the scientific community to better study chronic diseases and closed-loop therapies

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Hand, Foot and Mouth Disease in Vietnam: Epidemiology, Heart Rate Variability and Economic Burden

Over the last two decades, hand, foot and mouth disease (HFMD) has become a major clinical problem in Vietnam and the Asia-Pacific region. HFMD affects children, especially those under 5 years old, and has pandemic potential. Since 1997, there have been several outbreaks with severe clinical phenotypes, including brain stem encephalitis, attacking millions of children and causing thousands of deaths. Synthesizing data on epidemiology, etiology, disease pathophysiology and economic burden of this emerging infection remains essential to inform clinical management and health policy makers in prioritizing the development of intervention strategies. Using data from >56,000 hospitalized cases over an 11 year period, I described the spatial and temporal distribution of HFMD in Ho Chi Minh City, the main hotspot of HFMD in Vietnam. I found that the disease started in the west and then moved to the south-east and finally came back the west of the city. Results from a prospective multi-hospital based study conducted during 2015–2018 showed that of ~1200 enrolled patients, enterovirus A71 (EV-A71) was the most common HFMD pathogen detected, while coxsackievirus A6 (CV-A6) has emerged and replaced CV-A16 to become the second most common virus causing HFMD in Vietnam during the study period. Despite the emergence of other pathogens and the diversity of enterovirus serotypes (~20 serotypes) detected in HFMD patients, EV-A71 was the main cause of severe HFMD. Using long-term data synthesized as part of the research program, I also demonstrated for the first time that compared to EV-A71 subgenogroup B5, subgenogroup C4 was associated with more severe clinical phenotypes. Moreover, the predominance of subgenogroup C4 coincided with large, severe HFMD outbreaks in Vietnam (e.g. in 2011-12 and 2018). Collectively, the data suggest that an EV-A71 vaccine would be likely to substantially reduce the burden of HFMD, but a multivalent vaccine should be developed to control the ongoing HFMD epidemic because CV-A6, CV-A10 and CV-A16 were responsible for approximately 12% of severe HFMD cases and cross-reaction between these CV-As and EV-A71 is poor. In order to improve our knowledge of HFMD pathophysiology, I used ECG signal recorded by a wearable device (e-Patch) to depict the distribution of heart rate variability (HRV) indices by severity and by detected pathogens and found that compared to mild disease HRV parameters reflecting parasympathetic nervous system activation in the severe group decreased whereas those mirroring sympathetic activity and autonomic nervous system imbalance increased. In a similar trend, compared to HFMD associated with non-EV-A71, HRV indices reflecting the imbalance between sympathetic and parasympathetic activation in HFMD associated with EV-A71 were significantly higher. This suggests that children with EV-A71 infection were more likely to have ANS imbalance. Alongside with these findings, the feasibility of this wearable device in children has brought promising applications in HFMD case management by early detection of severe disease in future. To inform health policy makers in Vietnam about the burden of HFMD, I also estimated its economic burden. I showed that the total cost per case for mild and severe disease was $245.8 and$1326.7, respectively. Additionally, I also found that compared to CV-A infections, EV-A71 infection resulted in higher illness costs. At nationwide level, the total economic burden in Vietnam was estimated at >US$90 million for two-year period of 2016 – 2017