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In non-invasive ventilation, continuous monitoring of respiratory volumes is essential. Here, we present a method for the measurement of respiratory volumes by a single fiber-grating sensor of bending and provide the proof-of-principle by applying a calibration-test measurement procedure on a set of 18 healthy volunteers. Results establish a linear correlation between a change in lung volume and the corresponding change in a local thorax curvature. They also show good sensor accuracy in measurements of tidal and minute respiratory volumes for different types of breathing. The proposed technique does not rely on the air flow through an oronasal mask or the observation of chest movement by a clinician, which distinguishes it from the current clinical practice.

The problem of synthesizing the standard 12-lead electrocardiogram (ECG) from the signals recorded using three special ECG leads is studied in detail. The implementation of that concept into the design of a new mobile ECG transtelephonic system is presented. The system has two separate units: a stationary diagnostic-calibration center and a mobile ECG device with integrated electrodes. The patient records by himself three special leads with the mobile ECG recorder and sends data via cellular phone to the personal computer in the diagnostic center where standard 12-lead ECG is numerically reconstructed on the base of the patient transformation matrix previously calculated into the calibration process. The experimental study shows high accuracy of the reconstructed ECG.

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Acute Myocardial Infarction (AMI) remains a leading cause of mortality in the United States. Finding accurate and cost effective solutions for AMI diagnosis in Emergency Departments (ED) is vital. Consecutive, or serial, ECGs, taken minutes apart, have the potential to improve detection of AMI in patients presented to ED with symptoms of chest pain. By transforming the ECG into 3 dimensions (3D), computing 3D ECG markers, and processing marker variations, as extracted from serial ECG, more information can be gleaned about cardiac electrical activity. We aimed at improving AMI diagnostic accuracy relative to that of expert cardiologists. We utilized support vector machines in a multilayer network, optimized via a genetic algorithm search. We report a mean sensitivity of 86.82%±4.23% and specificity of 91.05%±2.10% on randomized subsets from a master set of 201 patients. Serial ECG processing using the proposed algorithm shows promise in improving AMI diagnosis in Emergency Department settings.