Design of a Wearable 12-Lead Noncontact Electrocardiogram Monitoring System

FGSQA-Net: A Weakly Supervised Approach to Fine-Grained Electrocardiogram Signal Quality Assessment

OBJECTIVE

Due to the lack of fine-grained labels, current research can only evaluate the signal quality at a coarse scale. This article proposes a weakly supervised fine-grained electrocardiogram (ECG) signal quality assessment method, which can produce continuous segment-level quality scores with only coarse labels.

METHODS

A novel network architecture, i.e. FGSQA-Net, is developed for signal quality assessment, which consists of a feature shrinking module and a feature aggregation module. Multiple feature shrinking blocks, which combine residual CNN block and max pooling layer, are stacked to produce a feature map corresponding to continuous segments along the spatial dimension. Segment-level quality scores are obtained by feature aggregation along the channel dimension.

RESULTS

The proposed method was evaluated on two real-world ECG databases and one synthetic dataset. Our method produced an average AUC value of 0.975, which outperforms the state-of-the-art beat-by-beat quality assessment method. The results are visualized for 12-lead and single-lead signals over a granularity from 0.64 to 1.7 seconds, demonstrating that high-quality and low-quality segments can be effectively distinguished at a fine scale.

CONCLUSION

FGSQA-Net is flexible and effective for fine-grained quality assessment for various ECG recordings and is suitable for ECG monitoring using wearable devices.

SIGNIFICANCE

This is the first study on fine-grained ECG quality assessment using weak labels and can be generalized to similar tasks for other physiological signals.

Feasibility of patch-type wireless 12-lead electrocardiogram in laypersons

Various efforts have been made to diagnose acute cardiovascular diseases (CVDs) early in patients. However, the sole option currently is symptom education. It may be possible for the patient to obtain an early 12-lead electrocardiogram (ECG) before the first medical contact (FMC), which could decrease the physical contact between patients and medical staff. Thus, we aimed to verify whether laypersons can obtain a 12-lead ECG in an off-site setting for clinical treatment and diagnosis using a patch-type wireless 12-lead ECG (PWECG). Participants who were ≥ 19 years old and under outpatient cardiology treatment were enrolled in this simulation-based one-arm interventional study. We confirmed that participants, regardless of age and education level, can use the PWECG on their own. The median age of the participants was 59 years (interquartile range [IQR] = 56-62 years), and the median duration to obtain a 12-lead ECG result was 179 s (IQR = 148-221 s). With appropriate education and guidance, it is possible for a layperson to obtain a 12-lead ECG, minimizing the contact with a healthcare provider. These results can be used subsequently for treatment.

Biomimetic human eyes in adaptive lenses with conductive gels

To investigate the imaging effect, adaptive robust lenses are prepared by sealing transparent liquid or gel. Lenses are fabricated using the negative-pressure method, which is a benefit for a stable biconvex shape. Under the action of an electric field, the soft lens deforms following the dielectric elastomer actuator (DEA). DE (dielectric elastomer) membranes expand in the plane perpendicular to the electric field lines. The toroidal driving area leads to a decrease in lens diameter and an increase in convex curvature. Therefore, the focal length of the lens becomes shorter. The experimental measurement utilizes the double focal length method. As a result, the largest focal length change that could be achieved was 44.7% (190 mm→105 mm) of the soft lens using a DEA with carbon grease electrodes. Furthermore, the ECG (electrocardiogram) conductive gel could replace traditional carbon grease for DEA electrodes in optics. This type of transparent electrode is creatively applied to a biomedical lens. Under the same conditions, the electrostriction rate in a DEA with ECG gel was achieved at 33%, which was greater than that of 28% in a DEA coupled with carbon grease electrode. Adaptive lenses have characteristics such as easy fabrication, low cost, and strong operability, and they possess great potential application value in biomedical feild.

Development of wearable multi-lead ECG measurement device using cubic flocked electrode

This paper describes the fabrication and fundamental evaluation of the cubic flocked electrode (CFE), which is a dry electrode that is fabricated using electrostatic flocking technology. The development of a wearable multi-lead electrocardiogram (ECG) measurement device using the CFE is also reported. To enable the measurement of ECG signals with sufficient quality for medical applications, the occurrence of motion artifacts (MAs) is the most important problem to be overcome. Therefore, it is necessary to stabilize the contact between the patient's skin and the dry electrode. Because the CFE developed in this work offers both contact stability and flexibility, it is expected to enable ECG measurements with low MA levels. In this study, it is demonstrated that the number of MAs caused by respiration can be reduced when the CFE contact is made at a contact pressure of approximately 500 Pa using MA evaluation equipment that was developed in-house. Additionally, a wearable multi-lead ECG is designed and fabricated based on this contact pressure (500 Pa). The results of the demonstration experiment show that the ECG measurements are successful to the same extent as a conventional medical device.

Robustness of electrocardiogram signal quality indices

Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient’s health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQI_p), skewness (SQI_skew), signal-to-noise ratio (SQI_snr), higher order statistics SQI (SQI_hos) and peakedness of kurtosis (SQI_kur). We analysed the robustness of these SSQIs against different window sizes across diverse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

The ageing population increases the demand for customized home care. As a result, sensing technologies are finding their way into the home environment. However, challenges associated with how users interact with sensors and data are not well-researched, particularly from a design perspective. This review explores the literature on important research projects around sensors, design and smart healthcare in smart homes, and highlights challenges for design research. A PRISMA protocol-based screening procedure is adopted to identify relevant articles (n = 180) on the subject of sensors, design and smart healthcare. The exploration and analysis of papers are performed using hierarchical charts, force-directed layouts and 'bedraggled daisy' Venn diagrams. The results show that much work has been carried out in developing sensors for smart home care. Less attention is focused on addressing challenges posed by sensors in homes, such as data accessibility, privacy, comfort, security and accuracy, and how design research might solve these challenges. This review raises key design research questions, particularly in working with sensors in smart home environments.

Thermoplasmonic effect onto Toad physiology signals by plasmonic microchip structure

Cardiovascular diseases are considered as the leading cause of death and almost 80% of deaths from this disease are developed in poor and less developed countries where early detection facilities are less available, along with overlooking the importance of screening. In other words, real-time monitoring of the physiological signals using flexible and wearable biosensors plays an important role in human life style. Thus, the present study aims to propose two dimensional flexible and wearable gold covered plasmonic samples as a physiological signal recorder, in which chips with nano array of resonant nanowire patterns performing in an integrated platform of plasmonic devices. The produced surface plasmon waves in our main chip were paired with an electric wave from the heart pulse and it use for recording and detecting the heartbeat of a toad with high accuracy. This measurement was performed in normal state and under external laser heating process to check the ability of signal recording and also thermoplasmonic effect onto the toad's heart signal. Our results show that our sensor was enough sensitive for detection while raising the body temperature of the toad and changing its heart rate as flatting T and P waves by thermoplasmonic effect.

A DETECTION SYSTEM OF EXERCISE ELECTROCARDIOGRAM

ABSTRACT Introduction: A new exercise electrocardiogram (ECG) detection system was investigated in this study to diagnose and analyze cardiopulmonary function and related diseases in a comprehensive and timely manner and improve the accuracy of diagnosis. Besides, its reliability and clinical applicability were judged. Objective: A new type of exercise ECG detection system was constructed by adding parameters such as respiratory mechanics, carbon dioxide, and oxygen concentration monitoring based on the traditional ECG detection system. Methods: The new system constructed in this study carried out the ECG signal detection, ECG acquisition module, blood pressure and respiratory mechanics detection and conducted a standard conformance test. Results: The heart rate accuracy detected by the exercise ECG system was greatly higher than that of the doctor's manual detection (P < 0.05). The accuracy of the new exercise ECG detection system increased obviously in contrast to that of the manual detection result (P < 0.05). The key technical index input noise and input impedance test results (24.5 μV and 12.4 MΩ) of the exercise ECG detection system conformed to the standard (< 30 μV and > 2.5 MΩ). The common-mode rejection and sampling rate test results (103.5 dB and 515 Hz) of key technical indicators in the exercise ECG detection system were all in line with the standards (≥89 dB and ≥500 Hz). Conclusion: The complete exercise ECG detection system was constructed through the ECG acquisition module, blood pressure detection, and respiratory mechanics detection module. In addition, this system could be applied to detect ECG monitoring indicators with high accuracy and reliability, which could also be extensively adopted in clinical diagnosis. Level of evidence II; Therapeutic studies - investigation of treatment results.

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier

Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases.

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

Common-Mode Noise Reduction in Noncontact Biopotential Acquisition Circuit Based on Imbalance Cancellation of Electrode-Body Impedance

Biopotential sensing technology with electrodes has a great future in medical treatment and human—machine interface, whereas comfort and longevity are two significant problems during usage. Noncontact electrode is a promising alternative to achieve more comfortable and long term biopotential signal recordings than contact electrode. However, it could pick up a significantly higher level of common-mode (CM) noise, which is hardly solved with passive filtering. The impedance imbalance at the electrode-body interface is a limiting factor of this problem, which reduces the common mode rejection ratio (CMRR) of the amplifier. In this work, we firstly present two novel CM noise reduction circuit designs. The circuit designs are based on electrode-body impedance imbalance cancellation. We perform circuit analysis and circuit simulations to explain the principles of the two circuits, both of which showed effectiveness in CM noise rejection. Secondly, we proposed a practical approach to detect and monitor the electrode-body impedance imbalance change. Compared with the conventional approach, it has certain advantages in interference immunity, and good linearity for capacitance. Lastly, we show experimental evaluation results on one of the designs we proposed. The results indicated the validity and feasibility of the approach.

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject's body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

Wearable Vital Signs Monitoring for Patients With Asthma: A Review

Worldwide,an estimated 461 000 people die from asthma attacks each year. While there remain treatments to alleviate asthma symptoms and reduce deaths, patient deterioration needs to be identified in sufficient time. To prevent asthma deterioration, patients need to be aware of personal and environmental triggers and monitor their asthma symptoms. The aim of this article is to provide a comprehensive review of the current state-of-the-art wearable sensors and devices that use vital signs for asthma patient monitoring and management. Among all vital signs, breathing rate and airflow sound are key indicators of asthmatic patients' health that can be measured directly using wearable sensors to provide continuous and constant patient monitoring or indirectly by estimations based on proven algorithms using electrocardiogram (ECG), photoplethysmogram (PPG), and chest movements. ECG and PPG signals are widely used in smart watches and chest bands, enabling easy integration of a more extensive body sensor framework for asthmatic exacerbation prediction. Other vital signs used in asthma patient monitoring include blood oxygen saturation, temperature, blood pressure, verbal sound, and pain responses. The use of wearable vital signs enabled a broad range of wearable sensor application scenarios for asthma monitoring and management.