Low Cost Portable ECG Data Acquisition System

A design strategy for the data acquisition block of a portable ECG machine for affordable remote CVD detection and diagnosis is proposed. It exploits the ECG property that most of the signal is concentrated within 20 Hz. Using this system one can achieve a low Nyquist data rate of 50 samples/sec. With Data Acquisition System designed one can also perform Irregular Sampling and using Compressive Sensing recover the signal. Using three such boards 3 ECG leads were simultaneously sampled both using Nyquist sampling and Irregular sampling. The cost of the one single board comes to Rs. (83+300) 383 and that of 3-Lead to Rs. (249 +500) 749. The Microcontroller board cost is not included as it was given free of cost

Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals

Modern wearable Internet of Things (IoT) devices enable the monitoring of vital parameters such as heart or respiratory (RESP) rates, electrocardiography (ECG), photo-plethysmographic (PPG) signals within e-health applications. A common issue of wearable technology is that signal transmission is power-demanding and, as such, devices require frequent battery charges and this poses serious limitations to the continuous monitoring of vitals. To ameliorate this, we advocate the use of lossy signal compression as a means to decrease the data size of the gathered biosignals and, in turn, boost the battery life of wearables and allow for fine-grained and long-term monitoring. Considering 1-D biosignals such as ECG, RESP, and PPG, which are often available from commercial wearable IoT devices, we provide a thorough review of existing biosignal compression algorithms. Besides, we present novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of compression, reconstruction and energy consumption performance of all schemes. As we quantify, the most efficient schemes allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4% of the peak-to-peak signal amplitude. Finally, avenues for future research are discussed. © 2014 IEEE

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Sensing and Compression Techniques for Environmental and Human Sensing Applications

In this doctoral thesis, we devise and evaluate a variety of lossy compression schemes for Internet of Things (IoT) devices such as those utilized in environmental wireless sensor networks (WSNs) and Body Sensor Networks (BSNs). We are especially concerned with the efficient acquisition of the data sensed by these systems and to this end we advocate the use of joint (lossy) compression and transmission techniques. Environmental WSNs are considered first. For these, we present an original compressive sensing (CS) approach for the spatio-temporal compression of data. In detail, we consider temporal compression schemes based on linear approximations as well as Fourier transforms, whereas spatial and/or temporal dynamics are exploited through compression algorithms based on distributed source coding (DSC) and several algorithms based on compressive sensing (CS). To the best of our knowledge, this is the first work presenting a systematic performance evaluation of these (different) lossy compression approaches. The selected algorithms are framed within the same system model, and a comparative performance assessment is carried out, evaluating their energy consumption vs the attainable compression ratio. Hence, as a further main contribution of this thesis, we design and validate a novel CS-based compression scheme, termed covariogram-based compressive sensing (CB-CS), which combines a new sampling mechanism along with an original covariogram-based approach for the online estimation of the covariance structure of the signal. As a second main research topic, we focus on modern wearable IoT devices which enable the monitoring of vital parameters such as heart or respiratory rates (RESP), electrocardiography (ECG), and photo-plethysmographic (PPG) signals within e-health applications. These devices are battery operated and communicate the vital signs they gather through a wireless communication interface. A common issue of this technology is that signal transmission is often power-demanding and this poses serious limitations to the continuous monitoring of biometric signals. To ameliorate this, we advocate the use of lossy signal compression at the source: this considerably reduces the size of the data that has to be sent to the acquisition point by, in turn, boosting the battery life of the wearables and allowing for fine-grained and long-term monitoring. Considering one dimensional biosignals such as ECG, RESP and PPG, which are often available from commercial wearable devices, we first provide a throughout review of existing compression algorithms. Hence, we present novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of compression, reconstruction and energy consumption performance of all schemes. As part of this first investigation, dictionaries are built using a suboptimal but lightweight, online and best effort algorithm. Surprisingly, the obtained compression scheme is found to be very effective both in terms of compression efficiencies and reconstruction accuracy at the receiver. This approach is however not yet amenable to its practical implementation as its memory usage is rather high. Also, our systematic performance assessment reveals that the most efficient compression algorithms allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4 % of the peak-to-peak signal amplitude. Based on what we have learned from this first comparison, we finally propose a new subject-specific compression technique called SURF Subject-adpative Unsupervised ecg compressor for weaRable Fitness monitors. In SURF, dictionaries are learned and maintained using suitable neural network structures. Specifically, learning is achieve through the use of neural maps such as self organizing maps and growing neural gas networks, in a totally unsupervised manner and adapting the dictionaries to the signal statistics of the wearer. As our results show, SURF: i) reaches high compression efficiencies (reduction in the signal size of up to 96 times), ii) allows for reconstruction errors well below 4 % (peak-to-peak RMSE, errors of 2 % are generally achievable), iii) gracefully adapts to changing signal statistics due to switching to a new subject or changing their activity, iv) has low memory requirements (lower than 50 kbytes) and v) allows for further reduction in the total energy consumption (processing plus transmission). These facts makes SURF a very promising algorithm, delivering the best performance among all the solutions proposed so far

A Multi-Tier Distributed fog-based Architecture for Early Prediction of Epileptic Seizures

Epilepsy is the fourth most common neurological problem. With 50 million people living with epilepsy worldwide, about one in 26 people will continue experiencing recurring seizures during their lifetime. Epileptic seizures are characterized by uncontrollable movements and can cause loss of awareness. Despite the optimal use of antiepileptic medications, seizures are still difficult to control due to their sudden and unpredictable nature. Such seizures can put the lives of patients and others at risk. For example, seizure attacks while patients are driving could affect their ability to control a vehicle and could result in injuries to the patients as well as others. Notifying patients before the onset of seizures can enable them to avoid risks and minimize accidents, thus, save their lives. Early and accurate prediction of seizures can play a significant role in improving patients’ quality of life and helping doctors to administer medications through providing a historical overview of patient's condition over time. The individual variability and the dynamic disparity in differentiating between the pre-ictal phase (a period before the onset of the seizure) and other seizures phases make the early prediction of seizures a challenging task. Although several research projects have focused on developing a reliable seizure prediction model, numerous challenges still exist and need to be addressed. Most of the existing approaches are not suitable for real-time settings, which requires bio-signals collection and analysis in real-time. Various methods were developed based on the analysis of EEG signals without considering the notification latency and computational cost to support monitoring of multiple patients. Limited approaches were designed based on the analysis of ECG signals. ECG signals can be collected using consumer wearable devices and are suitable for light-weight real-time analysis. Moreover, existing prediction methods were developed based on the analysis of seizure state and ignored the investigation of pre-ictal state. The analysis of the pre-ictal state is essential in the prediction of seizures at an early stage. Therefore, there is a crucial need to design a novel computing model for early prediction of epileptic seizures. This model would greatly assist in improving the patients' quality of lives. This work proposes a multi-tier architecture for early prediction of seizures based on the analysis of two vital signs, namely, Electrocardiography (ECG) and Electroencephalogram (EEG) signals. The proposed architecture comprises of three tiers: (1) sensing at the first tier, (2) lightweight analysis based on ECG signals at the second tier, and (3) deep analysis based on EEG signals at the third tier. The proposed architecture is developed to leverage the potential of fog computing technology at the second tier for a real-time signal analytics and ubiquitous response. The proposed architecture can enable the early prediction of epileptic seizures, reduce the notification latency, and minimize the energy consumption on real-time data transmissions. Moreover, the proposed architecture is designed to allow for both lightweight and extensive analytics, thus make accurate and reliable decisions. The proposed lightweight model is formulated using the analysis of ECG signals to detect the pre-ictal state. The lightweight model utilizes the Least Squares Support Vector Machines (LS-SVM) classifier, while the proposed extensive analytics model analyzes EEG signals and utilizes Deep Belief Network (DBN) to provide an accurate classification of the patient’s state. The performance of the proposed architecture is evaluated in terms of latency minimization and energy consumption in comparison with the cloud. Moreover, the performance of the proposed prediction models is evaluated using three datasets. Various performance metrics were used to investigate the prediction model performance, including: accuracy, sensitivity, specificity, and F1-Measure. The results illustrate the merits of the proposed architecture and show significant improvement in the early prediction of seizures in terms of accuracy, sensitivity, and specificity

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Design and Implementation of Complexity Reduced Digital Signal Processors for Low Power Biomedical Applications

Wearable health monitoring systems can provide remote care with supervised, inde-pendent living which are capable of signal sensing, acquisition, local processing and transmission. A generic biopotential signal (such as Electrocardiogram (ECG), and Electroencephalogram (EEG)) processing platform consists of four main functional components. The signals acquired by the electrodes are amplified and preconditioned by the (1) Analog-Front-End (AFE) which are then digitized via the (2) Analog-to-Digital Converter (ADC) for further processing. The local digital signal processing is usually handled by a custom designed (3) Digital Signal Processor (DSP) which is responsible for either anyone or combination of signal processing algorithms such as noise detection, noise/artefact removal, feature extraction, classification and compres-sion. The digitally processed data is then transmitted via the (4) transmitter which is renown as the most power hungry block in the complete platform. All the afore-mentioned components of the wearable systems are required to be designed and fitted into an integrated system where the area and the power requirements are stringent. Therefore, hardware complexity and power dissipation of each functional component are crucial aspects while designing and implementing a wearable monitoring platform. The work undertaken focuses on reducing the hardware complexity of a biosignal DSP and presents low hardware complexity solutions that can be employed in the aforemen-tioned wearable platforms. A typical state-of-the-art system utilizes Sigma Delta (Σ∆) ADCs incorporating a Σ∆ modulator and a decimation filter whereas the state-of-the-art decimation filters employ linear phase Finite-Impulse-Response (FIR) filters with high orders that in-crease the hardware complexity [1–5]. In this thesis, the novel use of minimum phase Infinite-Impulse-Response (IIR) decimators is proposed where the hardware complexity is massively reduced compared to the conventional FIR decimators. In addition, the non-linear phase effects of these filters are also investigated since phase non-linearity may distort the time domain representation of the signal being filtered which is un-desirable effect for biopotential signals especially when the fiducial characteristics carry diagnostic importance. In the case of ECG monitoring systems the effect of the IIR filter phase non-linearity is minimal which does not affect the diagnostic accuracy of the signals. The work undertaken also proposes two methods for reducing the hardware complexity of the popular biosignal processing tool, Discrete Wavelet Transform (DWT). General purpose multipliers are known to be hardware and power hungry in terms of the number of addition operations or their underlying building blocks like full adders or half adders required. Higher number of adders leads to an increase in the power consumption which is directly proportional to the clock frequency, supply voltage, switching activity and the resources utilized. A typical Field-Programmable-Gate-Array’s (FPGA) resources are Look-up Tables (LUTs) whereas a custom Digital Signal Processor’s (DSP) are gate-level cells of standard cell libraries that are used to build adders [6]. One of the proposed methods is the replacement of the hardware and power hungry general pur-pose multipliers and the coefficient memories with reconfigurable multiplier blocks that are composed of simple shift-add networks and multiplexers. This method substantially reduces the resource utilization as well as the power consumption of the system. The second proposed method is the design and implementation of the DWT filter banks using IIR filters which employ less number of arithmetic operations compared to the state-of-the-art FIR wavelets. This reduces the hardware complexity of the analysis filter bank of the DWT and can be employed in applications where the reconstruction is not required. However, the synthesis filter bank for the IIR wavelet transform has a higher computational complexity compared to the conventional FIR wavelet synthesis filter banks since re-indexing of the filtered data sequence is required that can only be achieved via the use of extra registers. Therefore, this led to the proposal of a novel design which replaces the complex IIR based synthesis filter banks with FIR fil-ters which are the approximations of the associated IIR filters. Finally, a comparative study is presented where the hybrid IIR/FIR and FIR/FIR wavelet filter banks are de-ployed in a typical noise reduction scenario using the wavelet thresholding techniques. It is concluded that the proposed hybrid IIR/FIR wavelet filter banks provide better denoising performance, reduced computational complexity and power consumption in comparison to their IIR/IIR and FIR/FIR counterparts

Biomedical Engineering

Biomedical engineering is currently relatively wide scientific area which has been constantly bringing innovations with an objective to support and improve all areas of medicine such as therapy, diagnostics and rehabilitation. It holds a strong position also in natural and biological sciences. In the terms of application, biomedical engineering is present at almost all technical universities where some of them are targeted for the research and development in this area. The presented book brings chosen outputs and results of research and development tasks, often supported by important world or European framework programs or grant agencies. The knowledge and findings from the area of biomaterials, bioelectronics, bioinformatics, biomedical devices and tools or computer support in the processes of diagnostics and therapy are defined in a way that they bring both basic information to a reader and also specific outputs with a possible further use in research and development

Tailor-made chemical sensing platforms for decentralized healthcare and wellbeing

Aquesta tesis fa referència a la necessitat social de la implementació de sensors electroquímics en la nostra vida quotidiana a diferents nivells. Des d’un enfocament sanitari, l’ús i l’aplicació real de plataformes fàcils d’utilitzar pel propi pacient facilitarien la presa de decisions gràcies a la obtenció d’informació rellevant i monitoratge d’una malaltia. Així mateix, l’ús d’aquestes eines de manera individual, en centres de salut o inclús hospitals, ajudarien a disminuir el cost que la sanitat ha d’afrontar diàriament. Des d’un enfocament diferent, aquest tipus de sensors poden oferir també altres tipus de aplicacions, poden ser usats amb finalitats mediambientals o de seguretat. La fabricació de sensors electroquímics (amperomètrics i potenciomètrics) integrats i impresos en diferents substrats fàcils de manipular, de baix cost i robustos (com tèxtils, globus o paper) ha estat aconseguida durant aquesta tesis. L’estudi del seu rendiment analític sota la influencia de diferents situacions d’estres i en diferents fluids biològics (detectant ions en suor o glucosa en sèrum i sang) també ha estat realitzat amb èxit. Aquestes aportacions tecnològiques van dirigides a superar els reptes que la societat d’avui en dia necessita solucionar: com pot ser la sostenibilitat del sistema sanitari en una població cada vegada mes envellida; el manteniment d’una seguretat i un estat del benestar; i el control mediambiental. Aquesta tesis suposa un avenç en aquest sentit i mostra diferents solucions científiques i eines útils per aquests reptes que la societat necessita afrontar.Esta tesis hace referencia a la necesidad social de la implantación de sensores electroquímicos en nuestra vida diaria a distintos niveles. Desde un enfoque sanitario, el uso y la aplicación real de plataformas fáciles de usar mediante el propio paciente facilitarían la toma de decisiones gracias a la obtención de información relevante y monitoreo de una enfermedad. Así mismo, el uso de estas herramientas de manera individual, en centros de salud o incluso hospitales disminuiría el costo que la sanidad debe afrontar diariamente. Desde un enfoque diferente, este tipo de sensores pueden ofrecer también otro tipo de usos, pudiendo ser aplicados para fines medioambientales o de seguridad. La fabricación de sensores electroquímicos (amperométricos y potenciométricos) integrados e impresos en diferentes sustratos fáciles de manipular, de bajo costo y robustos (como textiles, globos o papel) ha sido lograda durante esta tesis. El estudio de su rendimiento analítico bajo diferentes situaciones de estrés y en diferentes fluidos biológicos (detectando iones en sudor o glucosa en suero y sangre) también ha sido realizado de manera exitosa. Estas aportaciones tecnológicas van dirigidas a superar los retos que la sociedad de hoy en día necesita solucionar: como puede ser la sostenibilidad del sistema sanitario en una población cada vez más envejecida; el mantenimiento de una seguridad y un bienestar general; y el control medioambiental. Esta tesis supone un avance en este sentido y muestra diferentes soluciones científicas y herramientas útiles para estos retos que la sociedad necesita afrontar.This thesis refers to the social need of the implementation of electrochemical sensors in our daily life at different levels. From a sanitary point of view, the use and real application of user-friendly platforms by the patient itself would facilitate the decision-making process thanks to the obtaining of relevant information and monitoring of a disease. Besides, the use of these tools individually, in health centers or even hospitals, would reduce the cost that healthcare must pay on a daily basis. In a different approach, this type of sensors can also offer other types of applications, which can be applied for environmental or safety purposes. The manufacturing of electrochemical sensors (amperometric and potentiometric) integrated and embedded on different substrates easy to manipulate, low cost and robust (such as textiles, balloons or paper) has been achieved during this thesis. The study of their analytical performance under different mechanical stress and using different biological fluids (detecting ions in sweat or glucose in serum and blood) has also been carried out successfully. These technological contributions are aimed at overcoming the challenges that today's society needs to solve: such as the sustainability of the health system in an aging population; the maintenance of security and general wellbeing; and environmental control. This thesis contributes with huge advancements to face these issues and shows different scientific solutions and useful tools for these challenges that society needs to address. This thesis refers to the social need of the implementation of electrochemical sensors in our daily life at different levels. From a sanitary point of view, the use and real application of user-friendly platforms by the patient itself would facilitate the decision-making process thanks to the obtaining of relevant information and monitoring of a disease. Besides, the use of these tools individually, in health centers or even hospitals, would reduce the cost that healthcare must pay on a daily basis. In a different approach, this type of sensors can also offer other types of applications, which can be applied for environmental or safety purposes. The manufacturing of electrochemical sensors (amperometric and potentiometric) integrated and embedded on different substrates easy to manipulate, low cost and robust (such as textiles, balloons or paper) has been achieved during this thesis. The study of their analytical performance under different mechanical stress and using different biological fluids (detecting ions in sweat or glucose in serum and blood) has also been carried out successfully. These technological contributions are aimed at overcoming the challenges that today's society needs to solve: such as the sustainability of the health system in an aging population; the maintenance of security and general wellbeing; and environmental control. This thesis contributes with huge advancements to face these issues and shows different scientific solutions and useful tools for these challenges that society needs to address