The aim of this study was to assess the reproducibility of muscle-fiber conduction velocity (MFCV) estimates obtained from multichannel surface electromyographic (EMG) recordings. Surface EMG signals were collected with a matrix of 61 electrodes during isometric, submaximal (50% of the maximal voluntary contraction torque) contractions of the biceps brachii muscle. Conduction velocity was estimated using multichannel maximum likelihood techniques. Reproducibility of MFCV estimates was assessed varying the number of signals (two to seven) used for the estimate and the distance between detection points (5-30 mm). Intraclass correlation coefficient (ICC) of both initial MFCV values and their rates of change with fatigue increased when increasing number of signals and distance between detection points. ICC of initial MFCV was negative using two signals for MFCV estimate, and it increased to approximately 75% with six to seven signals. Thus, reproducibility of MFCV estimates may be improved significantly using advanced multichannel estimation methods with respect to classic two-channel techniques.

Objective To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). Methods A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction–confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. Results The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. Conclusion Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence–enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.

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