Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study

Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94–98], specificity of 95% [90–99], positive predictive value (PPV) of 99% [98–100], negative predictive value (NPV) of 82% [76–89], diagnostic odds ratio (DOR) of 496 [198–1,245], area under the ROC (AUC) of 0.96 [0.94–0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85–0.88], accuracy of 96% [94–98], and Cohen's Kappa of 0.86 [0.81–0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96–0.98] and 0.92 [0.91–0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.Peer ReviewedPostprint (published version

Epileptic seizure prediction and onset zone localization using intracranial and scalp electroencephalographic and magnetoencephalographic signals

In this chapter, we discuss how intracranial or scalp electroencephalographic and magnetoencephalographic recordings could be used for epileptic seizure prediction and onset zone localization. The signal processing methods and the challenges of the state-of-the-art are discussed.Peer ReviewedPostprint (published version

Prosthesis control using undersampled surface electromyographic signals

Amputations can result in disability, permanent physical injury, and even posttraumatic stress disorder. Upper extremity amputations are mostly work-related, and such injuries include about 7% of the total burden of disease. High-functional artificial limbs are not available to most amputees because of their high price and the lack of public health coverage. Thus, there has been a significant interest in the design and fabrication of low-cost active upper-limb prostheses. Such devices are usually controlled by surface electromyographic (sEMG) signals. Recently, portable, low-cost recording devices such as Thalmic Labs Myo Gesture Control Armband have been used in the movement detection. Such devices use undersampled sEMG signals. In this chapter, we discuss upper-limb prostheses and their control. We further provide the results of some experiments showing that such undersampled signals could be used for various applications required in advanced prosthesis control, e.g., force prediction, elbow angle prediction, movement detection, and time and frequency parameter extraction using undersampled sEMG signals. Finally, a low-cost controller for BRUNEL HAND 2.0 from Open Bionics is designed to link low-cost recording and prosthesis.This work was supported by the Ministry of Economy and Competitiveness (MINECO), Spain, under contract DPI2017-83989-R and the Ministry of Science and Innovation (MICINN), Spain, under contract PRE2018-085387. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. JF Alonso is a Serra Hunter Fellow. The research leading to this results has also received funding from the European's Union Horizon 2020 research and innovation program under the Marie Slodowska-Curie Grant Agreement No 712349 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia.Postprint (published version