The further exploration of the capacitive ECG (cECG) is hindered by frequent fluctuations in signal quality from body movement and changes in sleep position. The processing framework must be fundamentally adapted to make full use of this signal. Therefore, we propose a new signal-processing framework that determines the signal quality for short signal segments (2 and 4 seconds) using a multi-class classification model (qua_model) based on a convolutional neural network (CNN). We built another independent deep CNN classifier (pos_model) to classify the sleep position. In the validation, 12 subjects were recruited for a 30-minute experiment, which required the subjects to lie on a bed in different sleeping positions. The short segments, classified as clear (C1 class) by the qua_model, were used to determine sleep positions with the pos_model. In 10-fold cross-validation, the qua_model for signals of 4-second length could recognize the signal of the C1 class at a 0.99 precision and a 0.99 recall; the pos_model could recognize the supine sleep position, the left, and right lateral sleep positions at a 0.99 averaged precision and a 0.99 averaged recall. Given the amount of data accumulated per night and the instability in the signal quality, this fully automatic processing framework is indispensable for a personal healthcare system. Therefore, this study could serve as an important step for cECG technique trying to explore the cECG for unconstrained heart monitoring.
Using the Plethysmograph (PPG) signal to estimate blood pressure (BP) is attractive given the convenience and possibility of continuous measurement. However, due to the personal differences and the insufficiency of data, the dilemma between the accuracy for a small dataset and the robustness as a general method remains. To this end, we scrutinized the whole pipeline from the feature selection to regression model construction based on a one-month experiment with 11 subjects. By constructing the explanatory features consisting of five general PPG waveform features that do not require the identification of dicrotic notch and diastolic peak and the heart rate, three regression models, which are partial least square, local weighted partial least square, and Gaussian Process model, were built to reflect the underlying assumption about the nature of the fitting problem. By comparing the regression models, it can be confirmed that an individual Gaussian Process model attains the best results with 5.1 mmHg and 4.6 mmHg mean absolute error for SBP and DBP and 6.2 mmHg and 5.4 mmHg standard deviation for SBP and DBP. Moreover, the results of the individual models are significantly better than the generalized model built with the data of all subjects.
Telework has become a universal working style under the background of COVID-19. With the increased time of working at home, problems, such as lack of physical activities and prolonged sedentary behavior become more prominent. In this situation, a self-managing working pattern regulation may be the most practical way to maintain worker's well-being. To this end, this paper validated the idea of using an Internet of Things (IoT) system (a smartphone and the accompanying smartwatch) to monitor the working status in real-time so as to record the working pattern and nudge the user to have a behavior change. By using the accelerometer and gyroscope enclosed in the smartwatch worn on the right wrist, nine-channel data streams of the two sensors were sent to the paired smartphone for data preprocessing, and action recognition in real time. By considering the cooperativity and orthogonality of the data streams, a shallow convolutional neural network (CNN) model was constructed to recognize the working status from a common working routine. As preliminary research, the results of the CNN model show accurate performance [5-fold cross-validation: 0.97 recall and 0.98 precision; leave-one-out validation: 0.95 recall and 0.94 precision; (support vector machine (SVM): 0.89 recall and 0.90 precision; random forest: 0.95 recall and 0.93 precision)] for the recognition of working status, suggesting the feasibility of this fully online method. Although further validation in a more realistic working scenario should be conducted for this method, this proof-of-concept study clarifies the prospect of a user-friendly online working tracking system. With a tailored working pattern guidance, this method is expected to contribute to the workers' wellness not only during the COVID-19 pandemic but also take effect in the post-COVID-19 era.
Malignant ventricular arrhythmias (MAs), such as ventricular tachycardia (VT) that presages cardiac arrest, present the highest hurdle for the healthcare community to overcome. Given that MAs occur unpredictably and lead to emergencies, convenient tracking devices, e.g. photoplethysmogram (PPG), that could predict MAs would be irreplaceably valuable. Since the use of heartbeat intervals (HbI) to predict the occurrence of arrhythmias is becoming more feasible, a further attempt to establish a new convenient approach for predicting impending MAs with HbI is worth trying. Assuming that intrinsic characteristics of MAs (VT and ventricular fibrillation: VF) can be revealed by a suitable approach on the basis of signal complexity, we propose an approach that first expresses the physiological status of the heart by HbI; then delineates the patterns of HbI by a new complexity metric (refined composite multi-scale entropy: RCMsEn); and finally trains a nonlinear machine learning model (random forest: RF) to learn the specific patterns of MAs so as to differentiate them from the normal sinus heart rhythm (N) and other prevalent arrhythmias (atrial fibrillation: AF, and premature ventricular contraction: V).For calculating entropy values and predicting MAs as early as possible (which is the aim of this study), two specifications are of interest: the minimal length of HbI needed to delineate the MAs patterns sufficiently (lenmin), and the maximum time length at which our model can predict impending MAs (timemax). We compared the RF model with support vector machine (SVM) models based on linear and Gaussian kernels. Results show that the RF model performs the best, reaching a 99.24% recall and a 99.87% precision for a HbI of 500 heartbeats (the lenmin) 374 seconds (the timemax) preceding the occurrence of MAs. The HbI samples in this study were extracted from an electrocardiograph (ECG). However, given the subtle difference (0.1 ms typically) between the R-R interval of ECG and the P-P interval of PPG, this approach could be extended to HbI acquired by the PPG sensor and thus should be of substantial theoretical and practical significance in cardiac arrest prevention.
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