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

UWB Body Area Networks: Coexistence Analysis and Performance Optimization

Abstract: Body Area Networks (BANs) are wearable wireless sensor networks with a high potential for medical and sports applications. BANs appear to be a particularly appealing solution to provide information about the health status of a patient in medical environments such as hospitals or medical centres. Ultra Wide Band (UWB) technology grants a high temporal resolution, resistence to multipath, availability of inexpensive sensors and considerably low power requirements for extended monitoring periods. UWB is adopted by IEEE 802.15.4a standard, whose main goal is represented by the achievement of energy-efficient communications with data rates comprised between 1 kbits/s and several Mbits/s. This work analyzes the behaviour of a reference BAN composed of IEEE 802.15.4a UWB sensors in presence of a Low Data Rate (LDR) UWB interfering network represented by a second BAN located in the same room. An optimized code assignment policy to favour network coexistence is also introduced. Performance evaluation takes into account Bit Error Rate (BER) as a function of the number of nodes forming the reference BAN when a second BAN with a fixed number of nodes is present

Removal of electromyography noise from ECG for high performance biomedical systems

This paper presents the review of the biomedical system which consists of an energy source, signal processing, signal conditioning and signal transmission. These blocks are designed by various optimization techniques to achieve high operating speed, compressed area and minimum energy consumption. These techniques are mainly divided in to four aspects: (a) increasing the longevity of device using energy harvesting approaches; (b) reducing the delay to enhance the operating frequency; (c) reducing the data storage using data compression; (d) increasing the data rate transmission with reduced power consumption. This review paper briefly summarizes the various techniques and device performance achieved by these techniques. To attain these high performance systems input played a vital role. This paper also presents the different low pass IIR filter approximation method techniques to remove Electromyography noise from ECG input signal. For this purpose, we have taken MIT-BIH Arrhythmia database. We have calculated signal to noise ratio and power spectral density. On comparing their performance parameters of different low pass IIR filters, Elliptic filter has found best suited to remove this type of noise

Prediction of harvestable energy for self-powered wearable healthcare devices: filling a gap

Self-powered or autonomously driven wearable devices are touted to revolutionize the personalized healthcare industry, promising sustainable medical care for a large population of healthcare seekers. Current wearable devices rely on batteries for providing the necessary energy to the various electronic components. However, to ensure continuous and uninterrupted operation, these wearable devices need to scavenge energy from their surroundings. Different energy sources have been used to power wearable devices. These include predictable energy sources such as solar energy and radio frequency, as well as unpredictable energy from the human body. Nevertheless, these energy sources are either intermittent or deliver low power densities. Therefore, being able to predict or forecast the amount of harvestable energy over time enables the wearable to intelligently manage and plan its own energy resources more effectively. Several prediction approaches have been proposed in the context of energy harvesting wireless sensor network (EH-WSN) nodes. In their architectural design, these nodes are very similar to self-powered wearable devices. However, additional factors need to be considered to ensure a deeper market penetration of truly autonomous wearables for healthcare applications, which include low-cost, low-power, small-size, high-performance and lightweight. In this paper, we review the energy prediction approaches that were originally proposed for EH-WSN nodes and critique their application in wearable healthcare devices. Our comparison is based on their prediction accuracy, memory requirement, and execution time. We conclude that statistical techniques are better designed to meet the needs of short-term predictions, while long-term predictions require the hybridization of several linear and non-linear machine learning techniques. In addition to the recommendations, we discuss the challenges and future perspectives of these technique in our review

A Low Power Low Noise Instrumentation Amplifier For ECG Recording Applications

The instrumentation amplifier (IA) is one of the crucial blocks in an electrocardiogram recording system. It is the first block in the analog front-end chain that processes the ECG signal from the human body and thus it defines some of the most important specifications of the ECG system like the noise and common mode rejection ratio (CMRR). The extremely low ECG signal bandwidth also makes it difficult to achieve a fully integrated system. In this thesis, a fully integrated IA topology is presented that achieves low noise levels and low power dissipation. The chopper stabilized technique is implemented together with an AC coupled amplifier to reduce the effect of flicker noise while eliminating the effect of the differential electrode offset (DEO). An ultra low power operational transconductance amplifier (OTA) is the only active power consuming block in the IA and so an overall low power consumption is achieved. A new implementation of a large resistor using the T-network is presented which makes it easy to achieve a fully integrated solution. The proposed IA operates on a 2V supply and consumes a total current of 1.4µA while achieving an integrated noise of 1.2µVrms within the bandwidth. The proposed IA will relax the power and noise requirements of the analog-to-digital converter (ADC) that immediately follows it in the signal chain and thus reduce the cost and increase the lifetime of the recording device. The proposed IA has been implemented in the ONSEMI 0.5µm CMOS technology

Reliable and Energy Efficient Network Protocols for Wireless Body Area Networks

In a wireless Body Area Network (WBAN) various sensors are attached on clothing, on the body or are even implanted under the skin. The wireless nature of the network and the wide variety of sensors offers numerous new, practical and innovative applications. A motivating example can be found in the world of health monitoring. The sensors of the WBAN measure for example the heartbeat, the body temperature or record a prolonged electrocardiogram. Using a WBAN, the patient experiences a greater physical mobility and is no longer compelled to stay in a hospital. A WBAN imposes the networks some strict and specific requirements. The devices are tiny, leaving only limited space for a battery. It is therefore of uttermost importance to restrict the energy consumption in the network. A possible solution is the development of energy efficient protocols that regulate the communication between the radios. Further, it is also important to consider the reliability of the communication. The data sent contains medical information and one has to make sure that it is correctly received at the personal device. It is not allowed that a critical message gets lost. In addition, a WBAN has to support the heterogeneity of its devices. This thesis focuses on the development of energy efficient and reliable network protocols for WBANs. Considered solutions are the use of multi-hop communication and the improved interaction between the different network layers. Mechanisms to reduce the energy consumption and to grade up the reliability of the communication are presented. In a first step, the physical layer of the communication near the human body is studied and investigated. The probability of a connection between two nodes on the body is modeled and used to investigate which network topologies can be considered as the most energy efficient and reliable. Next, MOFBAN, a lightweight framework for network architecture is presented. Finally, CICADA is presented: a new cross layer protocol for WBANs that both handles channel medium access and routing

Cross-Layer Optimization for Power-Efficient and Robust Digital Circuits and Systems

With the increasing digital services demand, performance and power-efficiency become vital requirements for digital circuits and systems. However, the enabling CMOS technology scaling has been facing significant challenges of device uncertainties, such as process, voltage, and temperature variations. To ensure system reliability, worst-case corner assumptions are usually made in each design level. However, the over-pessimistic worst-case margin leads to unnecessary power waste and performance loss as high as 2.2x. Since optimizations are traditionally confined to each specific level, those safe margins can hardly be properly exploited. To tackle the challenge, it is therefore advised in this Ph.D. thesis to perform a cross-layer optimization for digital signal processing circuits and systems, to achieve a global balance of power consumption and output quality. To conclude, the traditional over-pessimistic worst-case approach leads to huge power waste. In contrast, the adaptive voltage scaling approach saves power (25% for the CORDIC application) by providing a just-needed supply voltage. The power saving is maximized (46% for CORDIC) when a more aggressive voltage over-scaling scheme is applied. These sparsely occurred circuit errors produced by aggressive voltage over-scaling are mitigated by higher level error resilient designs. For functions like FFT and CORDIC, smart error mitigation schemes were proposed to enhance reliability (soft-errors and timing-errors, respectively). Applications like Massive MIMO systems are robust against lower level errors, thanks to the intrinsically redundant antennas. This property makes it applicable to embrace digital hardware that trades quality for power savings.Comment: 190 page

Enabling technologies for distributed body sensor networks

Low Power Wireless Sensor Networks, Preventative Healthcare and Pervasive Systems are set to provide long-term continuous monitoring, diagnosis and care for patients in the next few years. Distributed forms of these networks are investigated from a holistic point of view. Individual components of these systems including: sensors, software and hardware implementations are investigated and analysed. Novel sensors are developed for low power capturing of Body Sensor Network (BSN) information to enable long term use. Software frameworks are designed to enable these technologies to run on low power nodes as well as enabling them to perform evaluation of their data before transmission into the network. An architecture is designed to enable task distribution to intensive processing from low power nodes. Two forms of distributed BSNs are also developed: a horizontal network and a vertical network. It is shown that using these two types of networks enables information and task distribution allowing low power sensing nodes to evaluate information before transmission. These systems have the opportunity to revolutionalise expensive acute episodic care systems of today, but are not currently being implemented or investigated to the extent that they could. The technological barriers to entry are addressed in this thesis with the investigation and evaluation of distributed body sensor networks. It is shown that horizontal networks can distribute information efficiently, while vertical networks can distribute processing efficiently