Purpose Electrocardiogram (ECG) signals collected from wearable devices are easily corrupted with surrounding noise and artefacts, where the signal-to-noise ratio (SNR) of wearable ECG signals is significantly lower than that from hospital ECG machines. To meet the requirements for monitoring heart disease via wearable devices, eliminating useless or poor-quality ECG signals (e.g., lead-falls and low SNRs) can be solved by signal quality assessment algorithms. Methods To compensate for the deficiency of the existing ECG quality assessment system, a wearable ECG signal dataset from heart disease patients collected by Lenovo H3 devices was constructed. Then, this paper compares the performance of three machine learning algorithms, i.e., the traditional support vector machine (SVM), least-squares SVM (LS-SVM) and long short-term memory (LSTM) algorithms. Different non-morphological signal quality indices (i.e., the approximate entropy (ApEn), sample entropy (SaEn), fuzzy measure entropy (FMEn), Hurst exponent (HE), kurtosis (K) and power spectral density (PSD) features) extracted from the original ECG signals are fed into the three algorithms as input. Results The true positive rate, true negative rate, sensitivity and accuracy are used to evaluate the performance of each method, and the LSTM algorithm achieves the best results on these metrics (97.14%, 86.8%, 97.46% and 95.47%, respectively). Conclusions Among the three algorithms, the LSTM-based quality assessment method is the most suitable for the signals collected by the Lenovo H3 devices. The results also show that the combination of statistical features can effectively evaluate the quality of ECG signals.

Hepatitis B virus (HBV) is a significant public health problem worldwide. Hepatitis B surface antigen (HBsAg) is the principle marker for laboratory testing of HBV, but the rapid identification of HBsAg is challenging in a resource-limited setting. Antibodies to HBsAg (Anti-HBs) levels are measured as markers for an immune response to vaccination as well as for decision making for specific treatment against Hepatitis-B. This research developed a prototype for the rapid detection of HBsAg using immunomagnetic separation, dynamic light scattering, and support vector machine. Magnetic beads coated with polyclonal anti-HBsAg were used to isolate HBsAg from the sample. The performance characteristics of quantitative real-time detection of HBsAg were characterized under optimized conditions. Twelve photodetectors were arranged on four concentric curvatures at different angles. The photodetectors were positioned around the sample flask in forward direction. The prototype acquires the real-time laser scattering light from the sample, and the noise was removed. The power spectral features were extracted from the acquired signal. Support vector machines (SVM) were used for training a classification algorithm by using extracted features. The overall classification accuracy for the identification of HBsAg was 87.7%. The HBsAg detection test was also performed on 20 serum specimens, with 10 serum samples were positive for HBsAg and 10 were healthy control subjects. The test had a dynamic range of 98.86 IU/mL to 3163.5 IU/mL. Results of HBsAg detection agreed completely with those of conventional Chemiluminescence Immunoassay (CLIA). In conclusion, the proposed HBsAg detection method can differentiate the sample that contains HBsAg enriched IM beads and blank IM beads.