Narrow Results

The performance of electronic textile (E-textile)-based wearable sensors is largely determined by the wire and electrode contacting stability to the body, which is a multi-discipline challenge for smart garment designs. In this paper, an integrated design of wearable sensors on a smart garment is presented to concurrently measure the multi-channel electrocardiogram, respiration, and temperature signals in different regions of the body. Sensors in separative probe-controller schemes are introduced with full-textile designs of the electrodes and signal transmission wires. An ultra-elastic structure of E-textile wire is proposed with excellent electrical stability, high stretch ratio, and low tension under body dynamics. A complete garment integration solution of the probes, wires, and the sensors is presented. The design is evaluated by comparing the signal quality in static and moderate body movements, which shows clinical level comparable precision and stability. The proposed design may constitute a general solution of distributed noninvasive physiological multi-parameter detection and monitoring applications.

Coronary heart disease (CHD) is a typical cardiovascular disease whose occurrence and development is a long process. Timely and accurate diagnosis of patients with varying degrees of coronary artery stenosis (VDCAS) is conducive to accurate treatment and prognosis assessment. This study aims to correctly classify VDCAS patients by utilizing multi-domain features fusion of single-lead 5-min ECG signals and machine learning methods, so as to provide reference for doctors to judge the CHD development process. ECG signals were collected from 206 subjects with CHD, mild CHD, thoracalgia and normal coronary angiograms (TNCA), and healthy. Then, the time, frequency, time–frequency, and nonlinear domain features of ECG signals were extracted to establish a multi-domain feature set. To get the optimum subset of features, the recursive feature elimination (RFE) and information gain (IG) were selected. Subsequently, eXtreme Gradient Boosting (XGBoost) and random forest (RF) were adopted for classification. Results indicated that RFE combined with XGBoost was significantly effective in classifying VDCAS patients. When the four categories of subjects (CHD, mild CHD, TNCA, and healthy) were classified, the average accuracy, sensitivity, specificity, and F1-score of the proposed method were 91.74%, 89.39%, 96.80%, and 90.09%, respectively. Besides, three categories of subjects (no stenosis, luminal narrowing $<$ 50%, and luminal narrowing $\ge$ 50%) and two categories of subjects (CHD and healthy) were also analyzed, and the average accuracy was 91.27% and 98.46%, respectively. The results suggest that the proposed method can provide reference for doctors to judge VDCAS patients.

Quantitative analysis of electrocardiogram (ECG) signals plays a pivotal role in objectively and quantitatively assessing cardiac electrical activity. This paper presents an innovative approach for quantitative ECG signal analysis utilizing extremum energy decomposition (EED). The methodology encompasses multiple steps: acquisition of unknown ECG signal under specific time and sampling conditions, denoising of acquired ECG signals, and subsequent decomposition of denoised ECG signals into a set of extremum modal function components alongside a residual. The n extremum modal function components obtained effectively represent different frequency bands. By evaluating these n extremum modal function components, the presence and severity of abnormalities within the ECG signal can be determined. The results showcased the effectiveness of the method in accurately identifying abnormal ECG signals, and the technique demonstrated robustness against noise interference, enhancing its practical utility in clinical and diagnostic settings. This research contributes to the field of ECG analysis by offering a quantitative toolset that enhances the objectivity and accuracy of abnormality assessment in cardiac electrical activity.

Fractals can model many classes of time-series data. The fractal dimension is an important characteristic of fractals that contains information about their geometrical structure at multiple scales. The covering methods are a class of efficient approaches, e.g., box-counting (BC) method, to estimate the fractal dimension. In this paper, the differential box-counting (DBC) approach, originally for 2-D applications, is modified and applied to 1-D case. In addition, two algorithms, called 1-D shifting-DBC (SDBC-1D) and 1-D scanning-BC (SBC-1D), are also proposed for 1-D signal analysis. The fractal dimensions for 1-D biomedical pulse and ECG signals are calculated.

Recently, using a wireless technology to transmit physiological signals, such as electrocardiogram (ECG), for a home tele-care system has received great attention. Although wireless transmission can provide the mobility advantage, it has to cope with the potential problem in limited bandwidth and induced interference. In this study, we propose an integration design in which a state-of-the-art compression algorithm called SPIHT (set partitioning in hierarchical trees) is combined with an unequal error protection scheme, to solve the problem for the ECG signals transmitted in Bluetooth packets. In this design, part of the SPIHT bit stream behaves like a fragile variable length code and needs a stronger protection with the forward error correction (FEC) code; the rest of the bit stream is lightly protected by the code. The simulation results show that the 2/3-rate FEC code in DM packets works effectively without the proposed scheme when the interference is fairly small. However, with the proposed scheme, the quality of received ECG signals is usually much better than that without the scheme when stronger interferences from fading channels and wireless LAN are encountered in an indoor environment. Consequently, the important features of an ECG waveform, such as P wave, QRS complex, and T wave, could be well preserved at a receiver site with clinically acceptable reconstruction quality. In addition, the data compression method in the proposed scheme can save total transmission power and time, and therefore reduce its potential interferences to other wireless devices.

Arrhythmia is one kind of diseases that gives rise to the death and possibly forms the immedicable danger. The most common cardiac arrhythmia is the ventricular premature beat. The main purpose of this study is to develop an efficient arrhythmia detection algorithm based on the morphology characteristics of arrhythmias using correlation coefficient in ECG signal. Subjects for experiments included normal subjects, patients with atrial premature contraction (APC), and patients with ventricular premature contraction (PVC). So and Chan's algorithm was used to find the locations of QRS complexes. When the QRS complexes were detected, the correlation coefficient and RR-interval were utilized to calculate the similarity of arrhythmias. The algorithm was tested using MIT-BIH arrhythmia database and every QRS complex was classified in the database. The total number of test data was 538, 9 and 24 for normal beats, APCs and PVCs, respectively. The results are presented in terms of, performance, positive predication and sensitivity. High overall performance (99.3%) for the classification of the different categories of arrhythmic beats was achieved. The positive prediction results of the system reach 99.44%, 100% and 95.35% for normal beats, APCs and PVCs, respectively. The sensitivity results of the system are 99.81%, 81.82% and 95.83% for normal beats, APCs and PVCs, respectively. Results revealed that the system is accurate and efficient to classify arrhythmias resulted from APC or PVC. The proposed arrhythmia detection algorithm is therefore helpful to the clinical diagnosis.

We had proposed a novel and fast Electrocardiogram (ECG) signal compression algorithm for non-uniform sampling in time domain [1]. It meets the real-time requirement for clinical application. Moreover, the compression performance is stable and uniform even for abnormal ECG signals. A criterion called sum square difference (SSD) is defined as an error test equation. The algorithm using SSD to calculate error tolerance is applied to the records in MIT-BIH database (with 11-bit resolution and 360 Hz sampling rate). It belongs to the threshold-limited algorithm but [1] does not mention much about this kind of algorithm. In this paper we provide more comparisons among SSD, Fan, scan-along polygonal approximation (SAPA), maximum enclosed area (MEA), and optimization algorithm (OPT) using the two measures called sample compression ratio (SCR) and percent root mean squared difference (PRD) with proper mean offset that [1] does not adopt. The results show SSD outperforms the mentioned algorithms with the same computational complexity O(n). Moreover, the comparison with the best but time-consuming coder OPT (O (n³)) shows how much the algorithm can be improved.