ABSTRACT
This study aims to determine the performance of variational mode decomposition (VMD) combined with detrended fluctuation analysis (DFA) as a hybrid framework for extracting seismocardiogram and respiration signals from simulated single-channel accelerometry data and removing its contained noise. The method consists of two consecutive layers of VMD that each contribute to extracting respiration and SCG signal respectively. DFA is utilized to determine the number of modes produced by VMD and select the most appropriate modes to be the constituents of the reconstructed signal based on the Hurst exponent value thresholding. This hybridized VMD successfully extracted respiration and SCG signal with minimal mean absolute error value (0.516 and 0.849, respectively) and boosted the SNR to 2 dB and 4 dB, respectively in heavily noise-interfered conditions. This method also outperformed other empirical mode decomposition strategies and exhibits short computational time. Two main drawbacks exist in this framework, i.e. the determination of balancing parameter ( γ ) that is still conducted manually and the magnitude shifting phenomenon. In conclusion, the hybridized VMD shows an outstanding performance in denoising and extracting respiration and SCG signals from a single input that combines them and generally is impured by noise.
ABSTRACT
[This corrects the article DOI: 10.1007/s13534-022-00235-x.].
ABSTRACT
The rapidly increasing number of COVID-19 patients has posed a massive burden on many healthcare systems worldwide. Moreover, the limited availability of diagnostic and treatment equipment makes it difficult to treat patients in the hospital. To reduce the burden and maintain the quality of care, asymptomatic patients or patients with mild symptoms are advised to self-isolate at home. However, self-isolated patients need to be continuously monitored as their health can turn into critical condition within a short time. Therefore, a portable device that can remotely monitor the condition and progression of the health of these patients is urgently needed. Here we present a portable device, called Respinos, that can monitor multiparameter vital signs including respiratory rate, heart rate, body temperature, and SpO2. It can also operate as a spirometer that measures forced vital capacity (FVC), forced expiratory volume (FEV), FEV in the first second (FEV1), and peak expiratory flow Rate (PEFR) parameters which are useful for detecting pulmonary diseases. The spirometer is designed in the form of a tube that can be ergonomically inflated by the patient, and is equipped with an accurate and disposable turbine based air flow sensor to evaluate the patient's respiratory condition. Respinos uses rechargeable batteries and wirelessly connects to a mobile application whereby the patient's condition can be monitored in real-time and consulted with doctors via chat. Extensive comparison against medical-grade reference devices showed good performance of Respinos. Overall results demonstrate the potential of Respinos for remote patient monitoring during and post pandemic.
