Development and Test of a Portable ECG Device with Dry Capacitive Electrodes and Driven Right Leg Circuit

Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques

Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions.

Cardiorespiratory Sensors and Their Implications for Out-of-Hospital Cardiac Arrest Detection: A Systematic Review

Out-of-hospital cardiac arrest (OHCA) is a major health problem, with a poor survival rate of 2-11%. For the roughly 75% of OHCAs that are unwitnessed, survival is approximately 2-4.4%, as there are no bystanders present to provide life-saving interventions and alert Emergency Medical Services. Sensor technologies may reduce the number of unwitnessed OHCAs through automated detection of OHCA-associated physiological changes. However, no technologies are widely available for OHCA detection. This review identifies research and commercial technologies developed for cardiopulmonary monitoring that may be best suited for use in the context of OHCA, and provides recommendations for technology development, testing, and implementation. We conducted a systematic review of published studies along with a search of grey literature to identify technologies that were able to provide cardiopulmonary monitoring, and could be used to detect OHCA. We searched MEDLINE, EMBASE, Web of Science, and Engineering Village using MeSH keywords. Following inclusion, we summarized trends and findings from included studies. Our searches retrieved 6945 unique publications between January, 1950 and May, 2023. 90 studies met the inclusion criteria. In addition, our grey literature search identified 26 commercial technologies. Among included technologies, 52% utilized electrocardiography (ECG) and 40% utilized photoplethysmography (PPG) sensors. Most wearable devices were multi-modal (59%), utilizing more than one sensor simultaneously. Most included devices were wearable technologies (84%), with chest patches (22%), wrist-worn devices (18%), and garments (14%) being the most prevalent. ECG and PPG sensors are heavily utilized in devices for cardiopulmonary monitoring that could be adapted to OHCA detection. Developers seeking to rapidly develop methods for OHCA detection should focus on using ECG- and/or PPG-based multimodal systems as these are most prevalent in existing devices. However, novel sensor technology development could overcome limitations in existing sensors and could serve as potential additions to or replacements for ECG- and PPG-based devices.

Exploring the Role of Wearable Electronic Medical Devices in Improving Cardiovascular Risk Factors and Outcomes Among Adults: A Systematic Review

There is a developing trend of using wearable electronic devices as health aides, spurred on by telecommunication companies as fitness devices and marketed as such. They have been shown to count steps, pulse, and record arrhythmias, doubling as communication devices and prompting healthcare providers in some instances. We sought to determine if there was a direct correlation between device use and increased physical activity as recommended by the World Health Organization, or if this physical activity increase was only marginal at best. In addition, we sought to investigate if there were additional benefits to using these devices besides increased self-awareness of health. This systematic review used Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Keywords for searching articles centered around cardiovascular disease, wearable electronic devices, and their synonyms. Most of the data were obtained from PubMed, although other contributing databases were used, including ResearchGate, Science.gov, ScienceDirect, and PubMed Medical Subject Headings database. Only full-text articles were used. We identified 62 articles that met our search criteria but narrowed them down to 19 following qualitative assessment. Increased physical activity was found to be the one parameter that stood out by way of benefit from the device. Other findings, such as reduced waist circumference, obesity, glycated hemoglobin, and lipid levels, shared mixed results. At this time, we do not have a definition of what duration of device use is deemed standard for health. We have no consensus on which devices are superior health-wise. Our study, however, indicates that these devices, used with adequate health professional supervision, have a role to play in motivation and increased physical activity, enough to cause impactful gains in cardiovascular health.

Recent Advances in Cardiovascular Disease Biosensors and Monitoring Technologies

Cardiovascular disease (CVD) causes significant mortality and remains the leading cause of death globally. Thus, to reduce mortality, early diagnosis by measurement of cardiac biomarkers and heartbeat signals presents fundamental importance. Traditional CVD examination requires bulky hospital instruments to conduct electrocardiography recording and immunoassay analysis, which are both time-consuming and inconvenient. Recently, development of biosensing technologies for rapid CVD marker screening attracted great attention. Thanks to the advancement in nanotechnology and bioelectronics, novel biosensor platforms are developed to achieve rapid detection, accurate quantification, and continuous monitoring throughout disease progression. A variety of sensing methodologies using chemical, electrochemical, optical, and electromechanical means are explored. This review first discusses the prevalence and common categories of CVD. Then, heartbeat signals and cardiac blood-based biomarkers that are widely employed in clinic, as well as their utilizations for disease prognosis, are summarized. Emerging CVD wearable and implantable biosensors and monitoring bioelectronics, allowing these cardiac markers to be continuously measured are introduced. Finally, comparisons of the pros and cons of these biosensing devices along with perspectives on future CVD biosensor research are presented.

Accurate ECG Classification Based on Spiking Neural Network and Attentional Mechanism for Real-Time Implementation on Personal Portable Devices

Electrocardiogram (ECG) heartbeat classification plays a vital role in early diagnosis and effective treatment, which provide opportunities for earlier prevention and intervention. In an effort to continuously monitor and detect abnormalities in patients’ ECG signals on portable devices, this paper present a lightweight ECG heartbeat classification method based on a spiking neural network (SNN), a relatively shallow SNN model integrated with a channel-wise attentional module. We further explore the best-optimized architecture, which benefits from leveraging the full advantages of the SNN potential with the attention mechanism to process the classification task at low power and capture prominent features concerning the time, morphology, and multi-channel representations of the ECG signal. Results show that our model achieves overall classification accuracy of 98.26%, sensitivity of 94.75%, and F1 score of 89.09% on the MIT-BIH database, with energy consumption of 346.33 μJ per beat and runtime of 1.37 ms. Moreover, we have conducted multiple experiments to compare against current state-of-the-art methods using their assessment strategies to evaluate our model implementation on FPGA. So far, our work achieves comparable overall performance with all the literature in terms of classification accuracy, energy consumption, and real-time capability.

The Comparison Features of ECG Signal with Different Sampling Frequencies and Filter Methods for Real-Time Measurement

Electrocardiogram (ECG) signals have been used to monitor and diagnose signs of cardiovascular disease and abnormal signals about the human body. ECG signals are typically characterized by the PR, QRS, QT interval, ST-segment, and heart rate (HR) parameters. ECG devices are widely used for many applications, especially for the elderly. However, ECG signals are often affected by noises from the environment. There are mainly two types of noises that affect the ECG signals: low frequencies from muscle activity and 50/60 Hz from the electrical grid. Removing these noises is important for improving the quality of the ECG signal. A clear ECG signal makes it easy to diagnose cardiovascular problems. ECG signals with high sampling frequency are more accurate. However, the noises in the signal will be more obvious and it will be difficult to remove these noises with filters. We analyzed the symmetrical correlation between the sampling frequency of the signal and the parameters of the signal such as signal to noise ratio (SNR) and signal amplitude. This study will compare characterization of ECG signals performed at different sampling frequencies before and after applying infinite impulse response (IIR) and symmetric finite impulse response (FIR) filters. Therefore, it is critical that the sampling frequency is consistent at the same frequency of the ECG signal for accurate diagnosis. Furthermore, the approach can be also important for the device to help reduce the device’s computing power and hardware resources. Our results were tested with the MIT/ BIH database at 360 Hz sampling frequency with 11-bit resolution. We also experimented with the device operating in real-time with a sampling frequency from 100 Hz to 2133 Hz and a 24-bit resolution. The test results show the advantages of the symmetric FIR filter over IIR when applied to the filtering of ECG signals. The study’s conclusions can be applied to real-world devices to improve the quality of ECG signals.