Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

Evaluation of sympathetic nerve activity (SNA) using skin sympathetic nerve activity (SKNA) signal has attracted interest in recent studies. However, signal noises may obstruct the accurate location for the burst of SKNA, leading to the quantification error of the signal. In this study, we use the Teager–Kaiser energy (TKE) operator to preprocess the SKNA signal, and then candidates of burst areas were segmented by an envelope-based method. Since the burst of SKNA can also be discriminated by the high-frequency component in QRS complexes of electrocardiogram (ECG), a strategy was designed to reject their influence. Finally, a feature of the SKNA energy ratio (SKNAER) was proposed for quantifying the SKNA. The method was verified by both sympathetic nerve stimulation and hemodialysis experiments compared with traditional heart rate variability (HRV) and a recently developed integral skin sympathetic nerve activity (iSKNA) method. The results showed that SKNAER correlated well with HRV features (r = 0.60 with the standard deviation of NN intervals, 0.67 with low frequency/high frequency, 0.47 with very low frequency) and the average of iSKNA (r = 0.67). SKNAER improved the detection accuracy for the burst of SKNA, with 98.2% for detection rate and 91.9% for precision, inducing increases of 3.7% and 29.1% compared with iSKNA (detection rate: 94.5% (p < 0.01), precision: 62.8% (p < 0.001)). The results from the hemodialysis experiment showed that SKNAER had more significant differences than aSKNA in the long-term SNA evaluation (p < 0.001 vs. p = 0.07 in the fourth period, p < 0.01 vs. p = 0.11 in the sixth period). The newly developed feature may play an important role in continuously monitoring SNA and keeping potential for further clinical tests.

This study designed a novel noncontact capacitive Electrocardiogram (cECG) system for long-term atrial fibrillation (AF) monitoring. Unlike conventional ECG monitoring, the proposed system can record ECG signals without touching a subject’s skin, avoiding the problem of skin allergy by using wet electrodes. The main contributions of this study include the two aspects. Firstly, a noncontact electrode based on a micro-humidity fabric structure is proposed, which can improve the noncontact cECG signal quality. Secondly, based on the proposed electrode, the possibility of its application in long-term AF monitoring was investigated. The micro-humidity fabric electrode can improve the micro-humidity of the skin-electrode interface and the noncontact cECG signal quality. This humidity is low and can hardly be felt by the human body, which can also ensure comfort. The experimental results show that compared with the conventional noncontact fabric electrode, the skin-electrode impedance decreases from 104.1±58.1 MΩ (when using the electrode without the micro-humidity structure) at 0.5 Hz to 2.8±1.79 MΩ (with the micro-humidity structure). In the long-term cECG recording experiment, the system has high consistency with the reference equipment. The RR interval correlation coefficient is 0.998, and the maximum error ≤ 18 ms. For the validation of AF monitoring, a designed AF analysis algorithm is integrated into the noncontact cECG system, including effective signal screening and AF recognition algorithm based on RR interval and SVM, with an average accuracy of 98.57% on the wearable database. The algorithm is verified on 3 healthy people and 1 PAF patient using the noncontact cECG system with an average accuracy of 98.12%, showing that our system has the potential for long-term AF monitoring.

Atrial fibrillation (AF) is a prevalent clinical arrhythmia disease and is an important cause of stroke, heart failure, and sudden death. Due to the insidious onset and no obvious clinical symptoms of AF, the status of AF diagnosis and treatment is not optimal. Early AF screening or detection is essential. Internet of Things (IoT) and artificial intelligence (AI) technologies have driven the development of wearable electrocardiograph (ECG) devices used for health monitoring, which are an effective means of AF detection. The main challenges of AF analysis using ambulatory ECG include ECG signal quality assessment to select available ECG, the robust and accurate detection of QRS complex waves to monitor heart rate, and AF identification under the interference of abnormal ECG rhythm. Through ambulatory ECG measurement and intelligent detection technology, the probability of postoperative recurrence of AF can be reduced, and personalized treatment and management of patients with AF can be realized. This work describes the status of AF monitoring technology in terms of devices, algorithms, clinical applications, and future directions.

Non-contact capacitive coupling ECG (cECG) is considered to have a broad application prospect for its comfort and non-invasive. As an important part of cECG transmission path, the characteristics of dielectric materials significantly affect the signal quality. This paper studies the influence on the dielectric properties of coupling capacitance, human body-electrode impedance, and cECG signal quality from the perspectives of cotton fabric humidity and thickness and gives suggestions on the application of cECG. Firstly, the effects of fabric humidity and thickness on human body-electrode impedance are analyzed theoretically. Then the human body-electrode impedance and cECG signal quality index (SQI) are recorded and calculated to evaluate how thickness and humidity affect the sensing performance of cECG. The results show that influenced by the fabric thickness and humidity, the impedance values reveal observable changes varying from 5.6 GΩ (thickness: 1.00 mm, moisture regain 3.2%) to 50.4 MΩ (thickness: 0.18mm, moisture regain 23.2%) at 0.5 Hz, and the impedance change will cause the impedance mismatch between the two electrodes, resulting in poor signal quality. According to the trend of SQI, the recommended moisture regain under different thicknesses is given. When the fabric thickness is 0.18 mm, 0.35 mm, and 1.00 mm, the moisture regain should be higher than 8.5%, 14.6%, and 23.2%, respectively so as to obtain a relatively high-quality cECG signal. The design parameters discussed in this paper could provide a supporting evidence for the design of efficient non-contact cECG systems in the future.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This paper proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.