Wearable Electrocardiography Research Articles

Electronics of a Wearable ECG With Level Crossing Sampling and Human Body Communication.

In this paper, the human body communication (HBC) and level crossing sampling (LCS) are combined to design electronics for a wearable electrocardiograph (ECG). The ECG signals acquired by capacitively coupled electrodes are sampled with LCS in place of conventional synchronous sampling. In order to transmit signals through HBC at low frequencies (100kHz, 1MHz), an electric field sensor with high input impedance is adopted as the front end of the HBC receiver. The HBC channel gain is enhanced by more than 30dB with the electric field sensor. An LCS structure based on the send-on-delta concept is implemented with discrete components to convert the ECG signals into binary impulses. The converted impulses are modulated by an on-off keying modulator and then transmitted via the human body to the receiver. A prototype ECG waist belt is developed with commercially available components and experimentally evaluated. The results indicate that the acquired ECG waveforms exhibit good agreement with regular Ag/AgCl ECG methods. The heartbeat detection using a technique based on the Kadane's algorithm and the power consumption performance of the proposed system are also discussed.

A Speed- and Power-Efficient SPIHT Design for Wearable Quality-On-Demand ECG Applications.

In this paper, a speed and power-efficient set partitioning in hierarchical trees (SPIHT) design is introduced for one-dimensional (1-D) wavelet-based electrocardiography (ECG) compression systems with quality guarantee. To achieve real-time and low-power design objectives toward wearable quality-on-demand (QoD) ECG applications, we first propose a coding-time- and computation-efficient SPIHT algorithm using various types of coding status register files to overcome the disadvantages of low coding speeds and complicated hardware architectures characterizing prior SPIHT algorithms resulting from the necessity of dynamic computation and arrangement in the sorting and refinement processing phase. Second, a highly pipelined and power-efficient very large scale integration (VLSI) architecture is developed to implement a high-performance and low-power SPIHT design based on the proposed algorithm. The final simulation results demonstrate that our proposed algorithm can speed up the average coding time 1.52 to 2.74 times compared to prior work with an identical compression ratio for an 11-level $1024\times 1\,1-{\rm{D}}$ discrete wavelet transform at diverse target percentage root-mean-square differences (PRDT) on various MIT-BIH arrhythmia datasets. Applied to wearable wavelet-based QoD ECG applications, our proposed VLSI architecture attains a working frequency of 740MHz and consumes an average of $\text{23}\ \mu {\text{W}}$ of power with Taiwan Semiconductor Manufacturing Company 90-nm CMOS technology, which shows the effectiveness of speed and power over the state-of-the-art designs.

Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment.

Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.

A novel low-complexity digital filter design for wearable ECG devices.

Wearable and implantable Electrocardiograph (ECG) devices are becoming prevailing tools for continuous real-time personal health monitoring. The ECG signal can be contaminated by various types of noise and artifacts (e.g., powerline interference, baseline wandering) that must be removed or suppressed for accurate ECG signal processing. Limited device size, power consumption and cost are critical issues that need to be carefully considered when designing any portable health monitoring device, including a battery-powered ECG device. This work presents a novel low-complexity noise suppression reconfigurable finite impulse response (FIR) filter structure for wearable ECG and heart monitoring devices. The design relies on a recently introduced optimally-factored FIR filter method. The new filter structure and several of its useful features are presented in detail. We also studied the hardware complexity of the proposed structure and compared it with the state-of-the-art. The results showed that the new ECG filter has a lower hardware complexity relative to the state-of-the-art ECG filters.

容量式ウェアラブル胸バンド型心電計における外乱に対する耐性評価

Electrocardiogram (ECG) provides useful information for the diagnosis of cardiovascular disease. In capacitive-coupled ECG sensing, electrostatic artifact and movement artifact become serious problems. In particular, low frequency components such as T wave of ECG are susceptible to the artifacts. To obtain clear ECG, high stability is required for the electrocardiograph using capacitive-coupled electrode. In this study, tolerance of capacitive wearable chest-belt electrocardiograph was tested for the electrostatic and movement artifacts. A constant electrostatic artifact was discharged repeatedly on three types of electrode having different shielding configurations, and their transient responses were compared in terms of deviation area, transient slope and recovery time. The results revealed the best tolerance of doubly-shielded five-layered electrode. The best performed five-layered electrode was used for exercise tolerance test. Then, detection rates of R-wave and T-wave of ECG, and standard deviation of base line of the recordings were calculated as tolerance indices and compared to those obtained by commercial disposable electrode. Although R-wave detection rate of the five-layered electrode decreased by 2.0%, the rate of T-wave was comparable to those of the disposable electrode. Furthermore, the standard deviation of the base line was significantly smaller than that of the disposable electrode (p<0.01).

Characteristics of a Transactive Phenomenon in Relationships among Older Adults with Dementia, Nurses as Intermediaries, and Communication Robot

Healthcare for older adults with dementia is an important healthcare problem in Japan and in other developed countries. Importantly, healthcare workers in Japan are also getting older. In order to address solutions to this problem, using robots is being realized and is starting to assist healthcare and welfare practice needs. The aim of this study was to identify the characteristics of a transactive phenomenon in relationships among older adults with dementia, with nurses as intermediaries and with a cognitive skill-aid robot. Subjects were two institutionalized older adults who were diagnosed with dementia using Hasegawa’s Dementia Scale-Revised; a Kabochan (Nodding Kabochan) fixed with a remote-controlled speaker, Pechat (Kabochan with Pechat). Measurements of autonomic nervous activity were done using wearable electrocardiography attached to the subjects for four hours. Heart Rate Variability data were assessed at various frequency bands using a HRV software tool. In a continuously recorded data, interbeat (R-R) intervals were obtained for a 1-min segment using the maximum entropy method. In this study, the two major spectral components of HRV, the variances of the low-frequency (LF: 0.04 - 0.15 Hz) band and high-frequency (HF: 0.15 - 0.4 Hz) band, were calculated. Activities and behaviors of subjects transacting with Kabochan were observed by the expert nurses and video-recorded. Throughout the transaction period, observations were recorded of patients’ behavior, words uttered and over-all contents of the transactive conversation with the Kabochan or Kabochan with Pechat. Identified characteristics were transactive relationship phenomenon; changes in autonomic nervous system activity of persons with dementia are expressed as per one minute, linguistic response of elderly people, transactive relations among Kabochan, Kabochan with Pechat, and nurses as intermediaries. These were exhibited as Figures: Situation 1, A Sense of Fear, Situation 2, A Pleasant Sensation, and Situation 3, Misunderstanding and Confusion. It also showed that autonomic nervous system activity of persons with communication skills is actively changing in older adults with dementia. Conversation of adding Pechat to the Kabochan illustrated that nurses as intermediaries showed emotional conversations as established by effectively manipulating the Pechat. Also, it was revealed that if Kabochan with Pechat uttered words about patient’s personal information in a conversation, the patient felt a sense of fear. The researchers suggest that when developing future communication robot for elderly with dementia, it is necessary to consider, for an effective conversation, transactive relationships including nurses as intermediaries. The role functions of these intermediaries have been shown of high versatility, indicating that situations such as these can also be controlled by other healthcare providers such as by occupational therapists.

Flexible Graphene Electrodes for Prolonged Dynamic ECG Monitoring.

This paper describes the development of a graphene-based dry flexible electrocardiography (ECG) electrode and a portable wireless ECG measurement system. First, graphene films on polyethylene terephthalate (PET) substrates and graphene paper were used to construct the ECG electrode. Then, a graphene textile was synthesized for the fabrication of a wearable ECG monitoring system. The structure and the electrical properties of the graphene electrodes were evaluated using Raman spectroscopy, scanning electron microscopy (SEM), and alternating current impedance spectroscopy. ECG signals were then collected from healthy subjects using the developed graphene electrode and portable measurement system. The results show that the graphene electrode was able to acquire the typical characteristics and features of human ECG signals with a high signal-to-noise (SNR) ratio in different states of motion. A week-long continuous wearability test showed no degradation in the ECG signal quality over time. The graphene-based flexible electrode demonstrates comfortability, good biocompatibility, and high electrophysiological detection sensitivity. The graphene electrode also combines the potential for use in long-term wearable dynamic cardiac activity monitoring systems with convenience and comfort for use in home health care of elderly and high-risk adults.

Wearable technology and ECG processing for fall risk assessment, prevention and detection.

Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection. Statistical and data-mining methods are adopted to develop classification and regression models, validated with the cross-validation approach. The first classifier based on HRV features enabled to identify future fallers among hypertensive patients with an accuracy of 72% (sensitivity: 51.1%, specificity: 80.2%). The regression model to predict falls due to orthostatic dropdown from HRV recorded before standing achieved an overall accuracy of 80% (sensitivity: 92%, specificity: 90%). Finally, the classifier to detect simulated falls using ECG achieved an accuracy of 77.3% (sensitivity: 81.8%, specificity: 72.7%). The evidence from these three studies showed that ECG monitoring and processing could achieve satisfactory performances compared to other system for risk assessment, fall prevention and detection. This is interesting as differently from other technologies actually employed to prevent falls, ECG is recommended for many other pathologies of later life and is more accepted by senior citizens.

On ECG reconstruction using weighted-compressive sensing.

The potential of the new weighted-compressive sensing approach for efficient reconstruction of electrocardiograph (ECG) signals is investigated. This is motivated by the observation that ECG signals are hugely sparse in the frequency domain and the sparsity changes slowly over time. The underlying idea of this approach is to extract an estimated probability model for the signal of interest, and then use this model to guide the reconstruction process. The authors show that the weighted-compressive sensing approach is able to achieve reconstruction performance comparable with the current state-of-the-art discrete wavelet transform-based method, but with substantially less computational cost to enable it to be considered for use in the next generation of miniaturised wearable ECG monitoring devices.

A Feasibility Study Scheme of an Android-based Integrated Wearable ECG Monitoring System

Ubiquitous healthcare system is becoming essential in the attempt to improve healthcare delivery. Not only it is able to facilitate two-way communication between physician and patient, but it is also useful for the caregivers to maximize efficiency in running daily activities. Managing ischemia heart disease, one of the deadly diseases, would definitely benefit from such ubiquitous healthcare system. The current treatment method for ischemia requires patients to be physically present at their respective treatment centres for regular monitoring. This increases the need for remote monitoring method, due to the urgent nature of treating ischemia heart attack. Therefore, this paper presents preliminary system architecture and feasibility study of HeartPals, a low-cost Android-based integrated wearable electrocardiography (ECG) monitoring system that enables ischemia patients to be remotely monitored by their physicians. We also propose TOFUSER, a Technology-Oriented Feasibility stUdy Scheme for End-useRs as an extended feasibility study scheme to allow users to verify the preliminary requirements of the technology and users’ requirements of HeartPals.

Wearable ECG Recorder with Acceleration Sensors for Monitoring Daily Stress

A small and light-weight wearable electrocardiograph (ECG) device with three accelerometers (x, y, and z axis) was developed for prolonged monitoring of everyday stress. It consists of an amplifier, a microcomputer with an analog/digital converter, a tri-axial accelerometer, and a memory card. Four parameters can be sampled at 1 kHz for a maximum of 27 h with the default battery and a 1 GB memory card. An algorithm for the reliable and clear detection of R wave peaks of ECG was also developed for accurate heart rate variability (HRV) analysis. The algorithm reduces ECG motion artifacts induced by body movements. Off-line data processing includes autonomic nervous system (ANS) activity bi-spectral analysis and the application of the tone-entropy method to HRV data. The availability of the system was tested through simulated office work and three-day monitoring. Both short-term and circadian rhythms of ANS activity were clearly observed. The experimental data verifies the functionality of the proposed system.

An EKG in your Underwear

This article discusses the use and benefits of e-Nanoflex that combines nanostructured sensors, smart phones, and cloud computing and promises a new platform for everyday medical monitoring. At the University of Arkansas, a team of surgeons, engineers, and computer scientists has developed a personal heart monitor based on e-Nanoflex nanostructured sensors. The sensors can be embedded in clothing that can be worn and washed just like any other garment. The e-Nanoflex can also monitor recovery from surgery, medical compliance, and neurological diseases, or help athletes optimize their training routines. The nanostructured sensors developed at the University of Arkansas make true portable, wearable electrocardiograph (EKG) systems possible. They consist of tiny metal nanowires growing outwards from the surface of the electrodes. Sophisticated software can monitor data from e-Nanoflex sensors about heart rate consistency and other factors to ensure that patients comply with their prescriptions. The combination of wearable nanostructured sensors, smart phones, and cloud computing makes wearable EKGs possible.

A Comprehensive Ubiquitous Healthcare Solution on an Android™ Mobile Device

Provision of ubiquitous healthcare solutions which provide healthcare services at anytime anywhere has become more favorable nowadays due to the emphasis on healthcare awareness and also the growth of mobile wireless technologies. Following this approach, an Android™ smart phone device is proposed as a mobile monitoring terminal to observe and analyze ECG (electrocardiography) waveforms from wearable ECG devices in real time under the coverage of a wireless sensor network (WSN). The exploitation of WSN in healthcare is able to substitute the complicated wired technology, moving healthcare away from a fixed location setting. As an extension to the monitoring scheme, medicine care is taken into consideration by utilizing the mobile phone as a barcode decoder, to verify and assist out-patients in the medication administration process, providing a better and more comprehensive healthcare service.

QRS Detection Based on Multiscale Mathematical Morphology for Wearable ECG Devices in Body Area Networks

A novel wearable electrocardiograph (ECG) QRS detection algorithm for wearable ECG devices in body area networks is presented in this paper, which utilizes the multistage multiscale mathematical morphology filtering to suppress the impulsive noise and uses the multiframe differential modulus accumulation to remove the baseline drift and enhance the signal. The proposed algorithm, verified with data from the MIT/BIH Arrhythmia Database and wearable ECG devices, achieves an average QRS detection rate of 99.61%, a sensitivity of 99.81%, and a positive prediction of 99.80%. It compares favorably to the published methods.